COVIDECONOMICS: The Evaluation of COVID-19 Economic Effects

COVIDECONOMICS: The Evaluation of COVID-19 Economic Effects Mario Arturo Ruiz Estrada University of Malaya, 50603 Kuala Lumpur, Malaysia Email: [email protected] Tel: +006012-6850293 1 Acknowledgment “To all victims of COVID-19 from everywhere” 2 Content Chapter I Introduction 5. Chapter II The Application of Econophysics in the Evaluation Of the COVID-19 Economic Damage 8. Chapter III How Much Chaos is making the COVID-19 Crisis among Us? 16. Chapter IV How Long more the World Economy can Resist the COVID-19 Crisis Pressure? 18. Chapter V How much COVID-19 Affects the Conservation of Economic Energy (EE) of the U.S.? 29. Chapter VI A New Way to Measuring the GDP in the COVID-19 Crisis: The Input-Output Electronic Online Transactions Monitoring System (IOEOTM-System) 39. Chapter VII The Impact of COVID-19 on the World Oil Prices 44. Chapter VIII The Global Economic Smash Effect of COVID-19 52. 3 Chapter IX The Application of the Economic Sustainable Accelerators (TESA) in the COVID-19 Crisis 66. 4 Chapter I Introduction This book is divided into nine chapters. The main objective of this book is to evaluate different challenges and lessons from COVID-19 from an economic point of view. The main aim is to propose “COVIDECONOMICS” is to assess the impact of COVID-19 economically under the use of new analytical tools to evaluate the damage and its final effect on different social groups. We define COVIDECONOMICS as a new research field in Economics to assess the COVID-19 from different holistic approaches. The first chapter is the introductory part to explain each chapter of this book. The second chapter graphically demonstrates the patterns of economic recession from any epidemic in the world, i.e. the COVID-19 contagious infectious disease. This can generate economic waves on different markets (countries or regions). This chapter evaluates the way in which an economic recession from the COVID-19 contagious infectious disease damage can simultaneously affect five different markets economic hotspots viz. East Asia (Japan, South Korea, Taiwan, and Hong Kong), China, ASEAN, United States and the European Union (EU). To visualize how a worldwide epidemic can generate economic waves on world economy, it is necessary to apply the inter-linkage coordinate space. Finally, this chapter proposes the use of computer graphical animation, which is based on the construction of a large number of slides joined together through the production of a video. In our case, we will use Windows Microsoft movie maker software to generate the real time effect of these economic waves in the same graphical space. The third chapter evaluates how much COVID-19 affects consumer behavior in the U.S. and Japan. Hence, this study wants to present two new concepts, followed by the massive unnecessary overconsumption levels and massive irreversible under consumption levels. The main objective is to determine how COVID-19 generates a profound transformation in American and Japanese consumer behavior in the short run. We propose a new dynamic indicator entitled "The Consumption Unstable Behavior in COVID-19 Index (CUBE-COVID19-Index)." The CUBE-COVID-19-Index offers policy-makers and researchers a new analytical tool to evaluate how much distortion COVID-19 can generate in any country's consumption of essential goods and services (food and basic services). The CUBE-COVID19-Index is not intended to be a predicting indicator in any case. It shows the rapid consumer preferences changes originated by COVID-19 in the U.S. and Japan recently. In the fourth chapter is willing to evaluate how long the world economy can resist COVID-19 crisis pressure. In the present COVID-19 crisis looks unstoppable and uncertain its end. We try to make a serial of simulations using possible slight or extreme scenarios with different environments in the short and long run. The main objective is to offer a specific date until the world economy can likely resist COVID-19 crisis pressure. Finally, we try to evaluate how much developed and developing countries can resist the COVID-19 crisis pressure and find its critical point for recovery or full collapse, respectively. 5 The fifth chapter applies the special theory of relativity to evaluate the impact of COVID-19 on the U.S. economy. Therefore, we propose a new indicator is called “the Conservation of Economic Energy (EE ).” The construction of the conservation of economic energy (EE ) requests the use of two economic variables into the special theory of relativity, followed by the unemployment growth rate (U) and the COVID-19 human damage speed (ΔC2 ). Finally, the conservation of economic energy (EE) was applied for the case of the U.S. The sixth chapter proposes a new national account system to calculate the GDP in the COVID-19 crisis. The new national account approach is entitled "The Input-Output Electronic Online Transactions Monitoring System (IOEOTM-System)." The IOEOTM-System is based on the interaction among four main strategic sectors (producers, sellers, logistic, and final consumers) by "i" number of strategic sub-sectors under the uses of "j" number of goods and services. The main idea is to generate an alternative national account system for the COVID19 crisis under the total electronic transactions online accounting daily, monthly, and yearly to elaborate the economy's final output under the GDP-Surfaces construction. The seventh chapter tries to evaluate the impact of COVID-19 on the world oil prices from a multidimensional graphical perspective. The alternative multidimensional graphical is based on a new graphical method, the “Infinity Physical Space (I-Physical Space).” The I-Physical Space can systematically show the world oil prices from a multidimensional point of view in shorter or longer periods. To analyze the behavior of world oil prices, we divided historical data from April 2019 to April 2020. In this study, the behavior of world oil prices has two categories: stable oil price range and unstable oil price range. The application of the I-Physical Space framework allows us to identify periods of world oil price volatility quickly. Our findings indicate that any pandemic can generate high volatility of oil prices, such as the case of COVID-19 anytime and anywhere. According to chapter eighth discuses about the impact of COVID-19 on economic performance is crucial, but measuring such implications to get a sense of the intensity of its effects on macro-variables such as consumption, investment, government spending, and net trade is subject to a great deal of uncertainty. As such, this chapter primarily attempts to close this gap by using the Global Economic Smash Crisis Effect Simulator (GESCE-Simulator), a new economic simulator that could be used to evaluate the impact of COVID-19 on different macroeconomic scenarios simultaneously. Hence, this article, the world economy, was used to illuminate and illustrate the applicability of the GESCE-Simulator from where analyses provide a coherent evaluation of the degree to which Post-COVID-19 adverse economic effects from a multidimensional perspective. Finally, this chapter proposes an alternative production, distribution, and consumption platform for the case of COVID-19 crisis, this new platform is entitled "The Economic Sustainable Accelerators (TESA)." The TESA is based on a strategical plan to reactivate the production, distribution, and consumption of any country in the present COVID-19 crisis. Before we proceed to 6 implement TESA in any country, it is necessary to use twelve different modules of evaluation follow by (i) Module-1: COVID-19 infection cases geographical location; (ii) Module-2: Movement Control Order perimeters size; (iii) Module-3: Labour concentration and mobility systems, (iv) Module-4: Production priority plan; (v) Module-5: Transportation systems integral mobility; (vi) Module-6: Suppliers distribution; (vii) Module-7: Sanitation and Prevention strategic points; (viii) Module-8: Agriculture and Food Security; (ix) Module-9: COVID-19 Private and Public Partnership (public transportation controls, free health support, welfare programs, taxation exoneration), government spending controls; (x) Module-10: Industrial restructuration; (xi) Module-11: Services dynamicity; (xii) Module-12: COVID-19 Consumers opening levels mobility. The main objective of TESA is to offer policy-makers a new proposal to help countries to recovery faster from the COVID-19 crisis. The application of TESA is not limited to the study of a select group of cities. TESA, in effect, is a flexible and straightforward production, distribution, and consumption alternative platform. The third part of this chapter shows a multidimensional diagram to explain how TESA can work in any country. 7 CHAPTER II The Application of Econophysics in the Evaluation of the COVID-19 Economic Damage Mario Arturo Ruiz Estrada 2.1.Introduction Usually, evaluating the COVID-19 impact on the economy is playing around econometrics modeling and statistical methods. In our case, we try to apply Econophysics using the classic theories of Physics such as the special theory of relativity, the general theory of relativity, and the macroeconomic black holes to evaluate the COVID-19 negative economic impact. This document defines Econophysics as an alternative quantitative technique and sub-research field in economics. Econophysics is using physics and economics together to explain complex and dynamic socio-economic phenomena theoretically or empirically. 2.2.Section I: Measuring the Enough Economic Energy to Against the COVID-19 crisis (Ёc) Before we go deeper into the measurement of enough economic energy to against the COVID-19 crisis (Ёc), we need to do a general review of the special theory of relativity and how this theory works. It is based on joining two fundamentals laws in physics into a single law. This single law is called the special theory of relativity (Einstein, 1952), where the law of conservation of energy (C2) and the law of conservation of mass (M) was joined together into a single equation identified by E=MC2. According to the special theory of relativity, this theory said, “If followed from the special theory of relativity that mass (M) and energy (E) are both but for different manifestations of the same thing. A somewhat unfamiliar conception for the average mind; Furthermore, the equation Energy (E) is equal to MC 2, in which energy is put equal to mass (M), multiplied by the square of the velocity of light shows that tiny amounts of mass may be converted into a massive amount of energy and vice-versa. Finally, this chapter tries to use the special theory of relativity to measure enough economic energy to against the COVID-19 crisis (Ёc) and demonstrate that it is possible to apply the formula E=MC 2 into the COVID-19 economic turmoil. We are using two variables: the inflation rate originated from COVID-19 (I) and the employment absorption speed rate (J2), to measure enough economic energy against the COVID-19 crisis (Ёc). We like to say that the measurement of Ёc can be an alternative approach to analyze the COVID-19 economic crisis from an alternative quantitative approach. Initially, we suggest the application of the equation E=MC2 to measure the enough economic energy to against COVID-19 (Ёc). Hence, we suggest to replaces all the original variables 8 from E=MC2 by two economic variables to measure Ёc. The Energy (E) we like to replace by the enough economic energy to against the COVID-19 crisis (Ёc); the mass (M) is replaced by the inflation rate originated from the COVID-19 crisis “I” (see Expression 1) and the C2 will be replaced by the employment absorption speed rate represented by “J 2 ” (see Expression 2). Initially, we suggest to calculate first the inflation rate originated from COVID-19 rate between two years (2019/2020), in our case we apply partial differentiation based on time, we have two periods of time divided by the past year (t) and the next year (t+1). δIt+1(year 2020)/δIt(year 2019) ≡ I (1) On the other hand, the construction of the employment absorption speed rate (J 2 ), we need to start by build the absorption employment output rate “δAE” that is based on the total sum of three large integrals under the uses of the total sum of new part-time-jobs creation from the COVID-19 crisis (∫Σpi) plus the total sum of new technological-jobs creation from the COVID-19 crisis (Σ∫tj) plus the total sum of new logistic jobs creation from the COVID-19 crisis (∫Σlk ) (See Expression 3). Hence, the variable “t” represents time, in our model we calculate time based on a growth rate between two years. To measure the employment absorption speed rate (J 2 ), we suggest to apply the original formula of speed that is equal to distance divided by time (D/t); but in our case we replace distance by the absorption employment output rate “δAE”. We assume that the employment absorption speed rate (J 2 ) is not a constant variable into our equation such as the speed of the light is explained into the formula E=MC2. We suggest that to measure the enough economic energy to against the COVID-19 (Ёc) to keep the employment absorption speed rate (J 2 ) variable because the constant creation new jobs with sophisticated skills can generate a constant transformation in the labor market. Secondly, why the J2 need to be variable is because natural phenomenon can be measure with accuracy based on experimentation such as the speed of the light (C2 ), but in the case of social phenomenon such as economic phenomena measure cannot be measure with accuracy based on the experimentation at the same level that the natural phenomenon, it is the case of the labor market development. However, another reason about why the employment absorption speed rate (J2) is exponential square, it is because we assume that the employment absorption speed rate can generate a double spillover effect on the final amount of enough economic energy to against the COVID-19 crisis (Ёc) in the short and long term. In this part of our model, we request the application of the partial differentiation into the measure of the δAE and δt (see Expression 4 and 5). J 2 ≡ (δAE/δt) 2 (2) δAE = ∫∞pi + ∫∞tj + ∫∞lk (3) δAE = δAEt+1 / δAEt (4) δt = δtt+1 / δtt (5) Finally, the formula of the enough economic energy to against the COVID-19 crisis (Ёc) is equal to: 9 Ёc=IJ 2 (6) The formula of the enough economic energy to against the COVID-19 crisis (Ёc) can show four possible results are following by: First result, if we have low inflation rate originated from the COVID-19 crisis (I) multiply by high employment absorption speed rate square (J 2 ) then IJ 2 together can convert into a very large amount of enough economic energy to against the COVID-19 crisis (Ёc ). Second result, if we have high inflation rate originated from the COVID-19 crisis (I) multiply by low employment absorption speed rate square (J 2 ) then IJ 2 together can convert into a very small amount of enough economic energy to against the COVID-19 crisis (Ёc). Third result, if we have high inflation rate originated from the COVID-19 crisis (I) multiply by high employment absorption speed rate square (J 2 ) then JI2 together can convert into a very small amount of enough economic energy to against the COVID-19 crisis (Ёc). Fourth result, if we have low inflation rate originated from the COVID-19 crisis (I) multiply by low employment absorption speed rate square (J 2 ) then IJ 2 together can convert into a very small amount of enough economic energy to against the COVID-19 crisis (Ёc). In the case to measure the enough economic energy to against the COVID-19 crisis (Ёc), we request the application of the Omnia Mobilis Assumption (Ruiz Estrada, 2011) translated from Latin means “everything is moving”. The Omnia Mobilis assumption gives the freedom to our equation of the enough economic energy to against the COVID-19 crisis (Ёc) to use less ceteris paribus assumption into our modeling. Simultaneously, we assume also that the market always keeps in a “Dynamic Imbalanced State” (Ruiz Estrada and Park, 2018) under non control and highly vulnerable. In fact, the concept of equilibrium in the economic modeling of the enough economic energy to against the COVID-19 crisis (Ёc) is considered as a leak momentum of balance among inflation rate originated from the COVID-19 crisis (I) and the employment absorption speed rate square (J 2 ) that can appear any time, but we cannot predict when exactly this synchronized balance is going to be appeared. From a graphical perspective, we suggest the application of surfaces to visualize the behavior of the four possible scenarios into the enough economic energy to against the COVID-19 crisis (Ёc) equation explained by Ё=IJ 2. According to preliminary results that the enough economic energy to against the COVID-19 crisis (Ёc) worldwide economy request an inflation rate originated from the COVID-19 crisis “I” equal to 3.5% (0.035) and an employment absorption speed rate (J2) equal to (1.91)2 = 0.83. Hence, we have an Ёc equivalent to 1.27. With the lower inflation and a constant expansion of the employment absorption under the creation of part-times in different production sectors, we strongly recommend never stop production and distribution from keeping income and spending active in the economy. 10 2.3.Section II: The Application of the General Theory of Production Transformation Initially, our basic argument is that the production by sector [agriculture (PS1), industrial (PS2), and services (PS3)] mass (Ð p)1 trend can generate outflow or attraction. Hence, the production by sector mass (Ð p) expansion can generate a positive effect to generate a dynamic national production transformation between the traditional production sectors and new production sectors efficiently (see Figure 1.) Fig. 1. The Transition of Orbits Source: Author Our analysis for the general theory of production transformation originated from the COVID-19 crisis depends on the full production platform, including all sectors under the uses of the space-time continuum. It can be considered as a geometrical space under the usefulness of a formal co-ordinates system (see Figure 2). This geometrical space is represented by a manifold or plane surface by combining the space and time together. Our first assumption is that the full production platform is a large space-time continuum based on the intersection between space and time. Hence, the full production platform is represented by the space-time continuum. It is the place that all the production by sector mass (Ð p) is displayed in different places (or orbits). In fact, all production sectors transformation depends on its performance in In the construction of the production by sector mass (Ðp) (see Expression 8) the π is also a constant equal to 3.1416 and the radius is fixed by each total production output by sector growth rate (∆PS) between two periods of time, in our case the last year each total production output by sector growth rate (PSt-1) in real terms and the present year each total production output by sector growth rate (P St+1) in real terms (see Expression 7). 1 [ΔPS1 = (PSt+1 – PSt-1 / PSt-1)], [ΔPS2 = (PSt+1 – PSt-1 / PSt-1 )], [ΔP3 = (PSt+1 – PSt-1 / PSt-1 )]] (7) Ðp1 = (4/3)π∆PS12 , Ðp2 = (4/3)π∆PS22 , Ðp3 = (4/3)π∆PS32 (8) Finally, the analysis of the production by sector mass (Ðp) is based on the size of the volume of different spheres across different periods of time and spaces. T he main variable who establishes the size of the sphere volume is each total production output by sector rates annually. We find that the different sizes of spheres can help us to observe if exist expansion, contraction or stagnation of each total production output by sector rates annually across different periods of time and space. However, we suggest the uses of different colors for different spheres. It can help to visualize easily the behavior of the production by sector mass (Ðc) into the same graphical space. 11 the COVID-19 crisis to generate a strong attraction between the traditional and new production sectors respectively. Fig.2. The Space-Time Continuum Source: Author We assume also that the COVID-19 economic crisis damages exist two types of production sectors divided in the large production by sector mass (Ð lp) and small production by sector mass (Ð sp) in the same country. Therefore, always the small production by sector mass (Ð sp) is attached to a large production by sector mass (Ðlp) (see Figure 3). Hence, the small production by sector mass (Ð sp) is moving around the large production by sector mass (Ð lp). It is means that the large production by sector mass (Ð lp) generates a strong production gravity attraction to the small production by sector mass (Ð sp). Therefore, the attraction from the small production by sector mass (Ð sp) to the large production by sector mass (Ð lp) always exist is strong until exist the possibility of a reduction into the mass of the large production by sector mass (Ð lp). It can force to the small production by sector mass (Ð sp) change to another orbit that experience more high production by sector mass (Ð p) expansion. The reduction of the mass on any large production by sector mass (Ð lp) can be unexpected and fleeted (see Figure 3). Fig.3. The reduction of the mass on any large production by sector mass (Ð lp) can be unexpected and fleeted Source: Author Additionally, the general theory of production transformation also assumes that the production by sector mass (Ð p) by a large production sectors affected by a pandemic crisis always keep in a constant transformation or evolution across the time and space. It is means 12 that someday the closed production sectors can unfold to another production sector with more high production by sector mass (Ð p) expansion. The process of transition cannot be determinate WHEN exactly a production sector in a pandemic crisis can change its orbit to another production sector with high production by sector mass (Ð p) expansion (see Figure 1 and Figure 3). The application of the general theory of production transformation on the analysis of the world production by sector (agriculture, manufacturing, industry, and services) to find how a large production by sector mass (Ð p) can generate a strong production gravity attraction from traditional and new production sectors is possible to be probed. It is the case of agriculture, industry, and services. We can observe into three different trimesters that industry shows a strong production gravity attraction to services especially between March and May 2020. However, we can also observe that from the March 2020 until today the production gravity attraction reduce considerably between the agriculture sector production mass (Ða p) and the services sector production mass (Ðsp). Hence, the reduction of the services sector production mass (Ðsp) force to industrial leave its orbit and move to another orbit more far or change to another orbit with a production sector that experience a fast and large industrial sector production mass (Ðip). But in the case of industrial sector production mass (Ðip) expansion is generating a strong production gravity attraction to the service sector production mass (Ðip) in recently months. In the case of industrial sector, it was pushed out to another orbit between May and November 2020, but also exist the high possibility that industry sector is moving to a further orbit, it is originated by weakening of Ða p compare to Ðip (see Figure 4). 13 Fig.4. The Behavior of Ðp in Agriculture (A), Industry (I), and Services (S) Worldwide in year 2020 Source: World Bank (2020) 2.4.Section III: The Macroeconomic Black Holes From the COVID-19 Pandemic Crisis The idea to write about the application of black holes (Wheeler, 1962) on economics. It is to observe how the macroeconomic black holes can generate a negative impact on the final GDP performance in any country. In our case, we represent the macroeconomic black holes using the COVID-19 crisis as a main reference in this study. It is to observe how the economic black hole (the COVID-19 crisis) can generate considerable outflow from the GDP formation. In the process to analyze and visualize the impact of the macroeconomic black holes (see Figure 5 and 6) on the outflow of economic growth, we propose a new indicator that counteract on the performance of the GDP. The basic premise in the construction of macroeconomic black holes depend on the “the economic leaking outflow circumference (ELO-Circumference)”. To build the ELO-Circumference, we suggest first to find the diameter “☼Y i” (see Expression 9). It is equal to the total sum of the COVID-19 infected cases growth rate under the application of multi-dimensional partial differentiation in real time (☼X1) = 0.75, the death growth rate under the application of multi-dimensional partial differentiation in real time (☼X2) = 0.64, COVID19 non-recovered cases growth rate under the application of multi-dimensional partial differentiation in real time (☼X3) = 0.67, the COVID-19 recovered cases growth rate under the application of multi-dimensional partial differentiation in real time (☼X4) = 0.35, the health care budget per capita growth rate under the application of multi-dimensional partial differentiation in real time (☼X5) = 0.33, the beds/hospitals per capita growth rate under the application of multi-dimensional partial differentiation in real time (☼X6) = 0.27, the social security coverage growth rate under the application of multi-dimensional partial differentiation in real time (☼X7) = 0.40, the social subsidies growth rate under the application of multidimensional partial differentiation in real time (☼X8) = 0.77, the full quarantine time growth 14 rate under the application of multi-dimensional partial differentiation in real time (☼X9) = 0.45 (WHO, 2020) (see Expression 9). ☼Yi = √(☼∂X1(t)/∂X1(t+1) + ☼∂X2(t)/∂X2(t+1) + ☼∂X3(t)/∂X3(t+1) + ☼∂X4(t)/∂X4(t+1) + ☼∂X5(t)/∂X5(t+1) + ☼∂X6(t)/∂X6(t+1) + ☼∂X7(t)/∂X7(t+1) + ☼∂X8(t)/∂X8(t+1) + ☼∂X9(t)/∂X9(t+1)) (9) Note: (t) = present period of time and (t+1) = next period of time Hence, the construction of the macroeconomic black holes is following by: firstly, we use the economic leaking outflow circumference (ELO-Circumference) on the top and bottom of the macroeconomic black hole. We assume that the top and bottom of the ELOcircumference size in the black hole is the same, and the middle part or throat size of the macroeconomic black hole is equal to 1/3 part of the original size from the top and bottom ELO-circumference in the same macroeconomic black hole (see Figure 5). Therefore, the ELO- Circumference of the macroeconomic black hole is equal to π (3.14159…) multiply by the diameter “☼Yi” (see Expression 10). ELO-Circumference = π * ☼Yi (10) The diameter of the ELO-Circumference can show two possible results follow by: First, if the diameter (☼Yi) is large then we can observe a huge economic leaking outflow of the GDP growth from the original GDP to the final GDP. On the other hand, if the diameter (☼Y i) is small then we can observe a small outflow from the original GDP growth to the final GDP. However, the top and bottom circumference always keep in constant movement and sizes. It is possible based on the application of multi-dimensional partial differentiation in real time (Ruiz Estrada, 2017) and the application of the Omnia Mobilis assumption (Ruiz Estrada, 2011) to generate the relaxation of all variables that involve the macroeconomic black holes all the time. Finally, when we finish build our macroeconomic black hole, it is possible to start to evaluate the impact of the economic leaking outflow from the original GDP to the final GDP of any country. Therefore, the final GDP is equal to the GDP from last year minus ELOCircumference follow by expression 10. GDP final = (GDP last-year) – (ELO-Circumference) (11) According to the possible results from expression 11, we have three possible results: First result, if the ELO-Circumference is large then exist high possibility to have poor performance of the GDP. Second result, if the ELO-Circumference is equal to zero then can exist high possibility to have better performance of the GDP. And third result is that if the ELO-Circumference is small then exist high possibility to have a good performance of the GDP. Our basic premise is that the size if the ELO-Circumference originated from COVID-19 15 can be controlled by strict health controls and a possible uses of an effective vaccine. The main idea here is that the active participation of the civil society, government and private sector in the control of the COVID-19 expansion and less outflow on the final GDP. According to the economic leaking outflow circumference (ELO-Circumference) originated from COVID-19 crisis is equal to 0.60. Therefore, the final GDP for year 2019/2020 is predicted a growth rate of 1.3% (World Bank, 2020). Fig. 5. The Macroeconomic Black Hole Structure Source: Author 16 Fig. 6. The Effect of the Macroeconomic Black Holes from the COVID-19 Crisis Source: Author 2.5.Conclusion This chapter shows three basic conclusions: The first conclusion shows that to generate enough economic energy to against the COVID-19 crisis, we needed a low inflation and constant employment absorption (part-times). The second conclusion is that a continuous increment of the production in different sectors such as agriculture, industry, and services. The interaction of all these sectors can generate a strong production gravity attraction between the traditional and new production sectors. Finally, the third conclusion is that the size of the "the economic leaking outflow circumference (ELO-Circumference)" plays an essential role in the macroeconomic black holes' final size to evaluate the impact of the COVID-19 crisis on the final GDP in any country. References Einstein, A. (1952). Relativity: The Special and the General Theory. New York: Three Rivers Press. Ruiz Estrada, M.A. (2011). “Policy Modeling: Definition, Classification and Evaluation”. Journal of Policy Modeling, 33(4); 523-536. Ruiz Estrada, M.A., (2017). “An alternative graphical modeling for economics: Econographicology”, Quality and Quantity, 51(5): 2115–2139 Ruiz Estrada, M.A. and Park, D. (2018). “The Past, Present, and Future of Policy Modeling”. Journal of Policy Modeling, 40(1). 1-15. Wheeler, J. A. (1962). Geometry Dynamics. Academic Press, New York. World Bank (2020). General Information and database statistics. http://www.wb.org World Health Organization (WHO). General data. www.who.org 17 CHAPTER III How Much Chaos is making the COVID-19 Crisis among Us? Mario Arturo Ruiz Estrada 3.1. Introduction This research presents how much chaos COVID-19 is generating economically, socially, and politically. We assume that COVID-19 was the breaking-point to change the full people behavior globally. The world is changing so fast that many people cannot perceive the new challenges worldwide. However, the concept of uncertainty and chaos is common in our daily vocabulary. The present COVID-19 crisis is generating a significant impact on our daily life's behavior from all aspects, such as economic, social, political, and health. The COVID-19 shows how weak globalization is and forces us to reinvent a new socio-political-economic life model with its unique characteristics and mechanisms. Moreover, we experience that the world is moving from Globalization to Deglobalization in unexpected ways. The world is undergoing a profound transformation under new environments, and challenges originated from the COVID-19 crisis. Something is probed it. The world never is going to be the same as before it. We move from a global village under free trade agreements globally, the firm's trans-nationalization under considerable mobility of massive Foreign Direct Investment FDI) worldwide, and a gigantic cultural transnationalization under the use of the internet. On the other hand, deglobalization presents small villages with a centralized domestic production under the priority of the food security priority, support to small and medium domestic firms, and reinforce domestic culturalization. The world's quantitative and qualitative transformations forced us to change our minds and lifestyle to accept and adapt to a new sociopolitical-economic scheme to survive this turmoil new world. According to this research, people's adaptability in the post-COVID-19 crisis will be directly connected to people's uncertainty levels. The world's challenges also involve a new way of thinking and necessary skills from people to adapt to the new socio-economic-polit ical system. The new socio-economic-political system, we want to call "The Socio-Economic Hybrid Model." The Socio-Economic Hybrid Model is mixing capitalism and socialism to rebuild the world from the Post-COVID-19 crisis recovery faster. 18 3.2. An Introduction to Chaos Theory and Its Application to COVID-19 Since the COVID-19 appears at the end of 2019, we can observe rapid social, economic, political, and technological transformations. The study of these transformations requests a new quantitative analytical tool. This research strongly supports the application of non-linear dynamics modeling and multi-dimensional graphs under the 3-Dimensions concept to evaluate how chaotic is the COVID-19 situation worldwide? Our main objective is to elaborate a new mathematical and graphical model to explain it. On the other hand, this research analyzes different views about chaos. According to Nagashima and Baba (1992), chaos is a state without order or a disorganized state, in contrast with an ordered state. For Lorenz (1993), chaos is the absence of some order that ought to be present. The core idea presented here is that the study of the COVID-19 crisis should encompass more than one isolated linear equation and conceptually go beyond a 2-Dimensional graphical analysis revolving around one specific socio-economic problem. We can observe that chaos theory literature is enough to explain extreme dynamic, complex, and illogical phenomena. In our case, we want to apply the chaos theory in the study of the COVID-19 crisis. According to this research, to complement the chaos theory study, we need to study non-linear equations, random and fuzzy data analysis, the Lorenz model, artificial intelligence (AI), and graphical models under the uses of 3-Dimensional format deeply. The chaos theory is a powerful numerical and graphical analytical tool for simultaneous ly visualizing different dynamic rational and irrational scenarios. The main uses of controlled and non-controlled parametric in the general system request dynamical systems. Therefore, always the general system is running a large amount of data randomness. Additionally, we need to include qualitative data under a binary system's (0 or 1). The election of 0 or 1 is according to our parameters to classify our net qualitative data efficiently. This research suggests that the COVID-19 crisis in the context of chaos theory can be studied using three approaches: the butterfly theory, turbulence theory, and Econographicology (Ruiz Estrada, 2017). In this part of this research, it is essential to mention that work on chaos theory based on the random data analysis approach may be grouped into two large areas of study: real data and forecasting data. Firstly, historical data reveal different stages of COVID19 under different scenarios. Secondly, it is the unpredictability of COVID-19 that indicate possible chaos. Many of the fundamental concepts in the behavior of real data or forecasting data in COVID-19 can be represented by graphs. However, these graphs are not always plotting values of a single variable against the values of another. We are often interested in the simultaneous values of several variables that require charts in more than two dimensions. According to this research, we can observe that the classic 2-Dimensions graphs used to represent COVID-19 are inadequate in most chaos simulations. Nevertheless, this research requests that the evaluation of the COVID-19 crisis requests multidimensional graphical approach particularly easy to visualize. This chapter would like to suggest using Econographicology to study the COVID-19 situation in the context of chaos 19 theory. Econographicology offers a different type of multidimensional Cartesian spaces to demonstrate chaotic patterns. Econographicology is defined as a multidimensional graphical method to facilitate meta-database storage and multi-variable data behavior visualization. It stems from the necessity to generate an alternative and specialized multidimensional visual approach to evaluate complex and dynamic phenomena. Therefore, Econographicology aims to maximize the use of multidimensional graphs to minimize difficulties in storing arbitrary meta-data and visualize a large number of variables' behavior simultaneously. The new Cartesian spaces comprise Multidimensional Cartesian coordinates systems under linear and non-linear systems. In this research, we consider the application of the 4-Dimensional Coordinate Space. The 4-D Coordinate Space is constructed based on the traditional 3-D space concept, but they represent 5-Dimensions. The multiple-dimension representations are to facilitate an easier understanding of chaos in the COVID-19 crisis. In the quantitative part of the study of the COVID-19 crisis, several pertinent points may be noted. Application of traditional linear, non-linear equations, and 2-Dimensional graphs comprised 99% of cases, while linear equations were observed in the evaluation of the COVID-19 crisis by 92% of the total research chapters. The standard models used to evaluate the impact of the COVID-19 turmoil is based on the uses of the elasticity approach (ex-ante), the general equilibrium models (ex-ante), and different regression and correlation approaches (ex-ante), general equilibrium or dynamic models’ analysis were applied in 77% in various research chapters by different academic journals. In the last year, few academics have used chaos theory to evaluate the COVID-19 crisis in studying its general behavior. These few applications of chaos theory have significantly contributed to the study of the COVID-19 problem. A large part of this work comprises COVID-19 comparative studies to explain the link between uncertainty and expectations. Many of these studies are sensitive to initial conditions, and certain initial conditions specified may be less realistic. Without disturbances such as human uncertainty to the future, if only we would stop interfering with the system, in short, if it were not subjected to controlled and non-controlled forces. Some chaos analysts are now proposing that the world is chaotic as a dynamical system, and the COVID-19 crisis is inevitable at irregular intervals. The transparency of information about the COVID-19 real situation can reduce uncertainty, but more likely. It would merely shorten some trouble and lengthening others. In a nutshell, this research maintains that the application of chaos theory to COVID-19 faces limitations in studying the effects of multivariables using standard graphical approaches, representing merely one part of the complicated puzzle, which is the chaos of the COVID-19. On this account, this study further maintains that the study of disorder in COVID-19 requires a multi-dimensional analysis (both mathematical and graphical). We suggest introducing the 4-Dimensional coordinate space to apply chaos theory to the study of the COVID-19 crisis. The 4-D coordinate space offers a multidimensional view of the COVID-19 situation. 20 3.3. An Introduction to Four Dimension Coordinate Space (4-D Coordinate Space) The Four Dimension Coordinate Space (4-D Coordinate Space) by Ruiz Estrada (2017) consists of five axes ([X1:i, X2:i, X3:i, X4:i], Yi). The quadrant represented by “j” can be 1, 2, 3 or 4 and the axis value is any number from 0 to ∞. The 4-D Coordinate Space represents four independent variables “X1:i”, ”X2:i”, “X3:i” and “X4:i” and one dependent variable “Yi” respectively. Each “Xj:i” variable (X1:i, X2:i, X3:i, X4:i) and “Yi” variable has its individual axis which is a vertical line with both positive and negative range. The positive and negative values are represented by ([(X1:i or -X1:i), (X2:i or -X2:i), (X3:i or -X3:i) (X4:i or -X4:i)], (Yi or -Yi)] on the 4D Coordinate Space (See figure 1 and 2). Here it is extremely important to think “outside of the box” and not to constrain the vertical axis to the representation of only the standard Y variable. The key argument is that we must necessarily work within a 3-dimensional system yet attempt to represent multidimensional structures. What this system posits is that the x-axes are also represented by vertical lines that stand upright at some point along the horizontal spokes (similar to the spokes of an imaginary horizontal wheel). Given a particular Xi value, the point in question will be positioned at a distance (Xi) either above or below the horizontal plane depending on whether X is positive or negative. We suggest that the given X-vertical line be placed at a distance (Xi) from the core-Y axis. Representing the dependent variable, the fifth axis, “Yi” is positioned in the center of the graph (among the other four axes). “Yi” has a positive range and negative range. It is the convergent point of all the other four spokes (X1:i, X2:i, X3:i and X4:i). In other words, all “Xj:i” spokes converge at the “Yi” axis. The result is a figure represented by a structure with 5 vertices. The function to be used by the 4-D Coordinate Space is equal to Yi = ƒ(Xj:i), where X j:i < +∞ and -∞ < Yi < +∞. Finally, the 4-D coordinate space applied the Omnia Mobilis Assumption according to Ruiz Estrada (2011). 21 Fig. 1 The 4-D Coordinate Space P o Source: (Ruiz Estrada, 2017) Fig. 2 The 4-D Coordinate Space Plotting Example Source: (Ruiz Estrada, 2017) 22 3.4. An Introduction to the Levels of Uncertainty by the COVID-19 index (TLOUCIndex) The construction of uncertainty by the COVID-19 index (TLOUC-Index) is divided into two large sections: Basic calculations and the graphical representation. The basic calculations of each growth rates in the TLOUC-Index follow a standard formula (see Expression 1) that we applied on (i) the total COVID-19 infected cases growth rate (ΔX1) (see Expression 2); (ii) the total COVID-19 active cases growth rate (ΔX2) (see Expression 3); (iii) the total COVID-19 deaths growth rate (ΔX3) (see Expression 4); the total COVID-19 recovered cases growth rate (ΔX4) (see Expression 5). Finally, the TLOUC-Index calculation follows Expression 6. The common formula was applied in each growth rate is followed by expression 1. ΔXi = Xi(t+1) - Xi(to)/ Xi(to) (1) -> Xi=> i = {1, 2, 3, 4} Where ΔXi = growth rate, Xi(t+1) = present month data, and Xi(to) = last month data. ΔX1 = X1(t+1) – X1(to)/ X1(to) (2) ΔX2 = X2(t+1) – X2(to)/ X2(to) (3) ΔX3 = X3(t+1) – X3(to)/ X3(to) (4) ΔX4 = X4(t+1) – X4(to)/ X4(to) (5) In the graphical representation of the TLOUC-Index, each growth rate was plotted in each axis around, and the final result in the middle axis of the 4-D Coordinate Space see Figure 1. After plotting five points in the five axes, we procced to join with straight lines until we build a small surface. We try to analyze TLOUC-Index according to the position of the small surface into the 4-D coordinate space. We classified in three broad categories of uncertainty levels by the COVID-19: higher, mix, and lower (see Figure 3). For demonstration purposes, the following database: 1. The total COVID-19 infected cases monthly. 2. The total COVID-19 active cases monthly. 3. The total COVID-19 deaths monthly. 4. The total COVID-19 recovered cases monthly. 23 Fig. 3 The Levels of Uncertainty According to TLOUC-Index Level 1: High Uncertainty The TLOUC-Index is located in the positive quadrant Level 2: Mix of Uncertainty The TLOUC-Index is located between the positive and negative quadrant Level 3: Low Uncertainty The TLOUC-Index is located in the negative quadrant or closed to 0 Source: (Ruiz Estrada, 2017) In the analysis based on the TLOUC-Index, we apply the concept of the attractor. This consists of an infinite number of surfaces or higher-dimension manifolds –generalizations of surfaces to multi-dimensional space- often occurring in parallel sets, with a gap between any two surfaces of the set called a "strange attractor." The name was introduced in the early 1970s by David Ruelle and Floris Takensin in a chapter in which they proposed that turbulence is an example of what we now call chaos. We assume that the strange attractor in the COVID-19 crisis in the initial stage is the zero plane. In the study with the TLOUC-Index, we apply the 24 Lorenz attractor (Lorenz, 1998) to assume that the system never precisely repeats itself. The trajectory never intersects space; as the system changes, the point's motion represents the continuously changing variables. In our case, the TLOUC-Index never precisely repeats itself. The changes between the initial and final points in the COVID-19 crisis never intersects surfaces within the same Cartesian space. The rapid changes of the TLOUC-Index in different stages originate from the fluctuations or "pandemic crisis turbulence." This pandemic crisis turbulence stems from the fast expansion in a large number of infected cases. We assume that the governments only totally control the TLOUC-Index with strong participation in applying strict measures controls and a possible national vaccination campaign. High pandemic crisis turbulence intensity consists of an uncontrolled situation of expanding infected cases by any pandemic. On the other hand, a relatively low pandemic crisis turbulence intensity from a partial control of the pandemic. 3.5. The Application of the Levels of Uncertainty by the COVID-19 index (TLOUCIndex) Worldwide We can observe that the COVID-19 crisis behavior under the application of the TLOUC-Index in the last past twelve months, and we are using bimonthly periods of analysis: Period-I (January-February 2020); Period-II (March-April 2020); Period-III (May-June 2020); Period-IV (July-August 2020); Period-V (September-October 2020); Period-VI (NovemberDecember 2020) (see Figure 4). If we observe the TLOUC-Index worldwide bi-monthly (from January to December 2020) under 365 micro-simulations successively, then we can watch how TLOUC-Index graphically shows different displacements within the 4-D Cartesian space frequently. According to figure 4, Period-I (January-February 2020), we find a "mix of uncertainty" according to the 4-Dimensional coordinate space's surface position. The X4 shows that the total COVID-19 recovered cases drop considerably compared to X1, X2, X3. The last three results always are keeping in a constant expansion without a possibility to drop its development in the short run. Period-II (February-March 2020) and Period-IV (July-August 2020) show the same Period I trend (see Figure 4). In the case of Period-III (April-May 2020), Period-V (September-October 2020), Period-VI (November-December 2020), we can start to see a "high uncertainty," we can observe that X1, X2, X3 show large growth rates most enormous than X4. People's high uncertainty is panic, hysteria, speculation, myths, phobia, and paranoia worldwide. The graphical representation of the TLOUC-Index can show clearly, how these three surfaces with high uncertainty according to the Period-III, Period-V, and Period-VI results are located on top of the 4-D coordinate space (see Figure 4). The unique characteristic of high uncertainty is the faster expansion of X1, X2, X3 compared to X4. We can confirm that two successive bi-months results under high uncertainty can open the possibility of full chaos very soon. The TLOUCIndex probes that if the COVID-19 crisis continues longer around us until we have an effective COVID-19 vaccine worldwide. The main issue here is that the COVID-19 vaccine doesn't 25 probe jet its effectiveness until now. The TLOUC-Index assumes that the virus is always experiencing a constant mutation through time continuously. On the other hand, there is a high possibility that people who applied the COVID-19 vaccine can get the COVID-19 virus. If people with COVID vaccination don't consider its sanitary precautions and health control measures (mask, sanitizer, and social distancing), then the COVID-19 virus can continue for long around us. Having the COVID-19 vaccine is not the final solution against the COVID-19 virus because people always are at high risk everywhere and anytime. The real answer is to adapt our immunological system to COVID-19 until we have easy access to the correct and standardized COVID-19 vaccine worldwide. This research remarks that the COVID-19 vaccine helps reinforce our immunological system to fight this virus. Therefore, the COVID19 vaccine is not a medicine to eliminate the COVID-19 virus from our body. It is mostly impossible for the COVID-19 vaccine to help patients infected by the virus in advanced stages. The final result of the TLOUC-Index predicts that if we cannot stop the COVID-19 crisis until the middle of June 2021, we can experience full health, social, economic, and political chaos with irreparable damages. Fig. 4 The Application of TLOUC-Index Worldwide from January 2020 to December 2020 Period I: January-February 2020 (Mix of Uncertainty) Period II: March-April 2020 (Mix of Uncertainty) 26 Period III: May-June 2020 (High Uncertainty) Period IV: July-August 2020 (Mix of Uncertainty) Period V: September-October 2020 (High Uncertainty) Period VI: November-December 2020 (High Uncertainty) Source: WHO (2020) 27 3.6.Conclusion This chapter concludes that “The Levels of Uncertainty by the COVID-19 index (TLOUCIndex)” can evaluate how much health, economic, and social uncertainty can COVID-19 crisis generate among us. Having the COVID-19 vaccine is not the final solution against the COVID19 virus because people always are at high risk everywhere and anytime. The primary strategy against the COVID-19 virus is to adapt our immunological system to COVID-19 until we have easy access to the correct and standardized COVID-19 vaccine worldwide. This research remarks that the COVID-19 vaccine only helps to reinforce our immunological system to fight this virus. In fact, the COVID-19 vaccine is not a medicine to eliminate the COVID-19 virus from our body. It is mostly impossible for the COVID-19 vaccine to help patients infected by the virus in advanced stages. The last conclusion is that if we cannot stop the COVID-19 virus expansion until the middle of June 2021, then the world can experience a full health, social, economic, political chaos with irreparable damages. References Gleick, James.1987. Chaos: Making a New Sciences. Penguin books press. pp.1-350. Lorenz, Edward.1998. The Essence of Chaos. UCL press. pp.1-230. Nagashima N. and Baba Y. 1992. Introduction to Chaos. IOP Press. pp. 1-170. Ruiz Estrada, M.A. (2011). “Policy Modeling: Definition, Classification and Evaluation”. Journal of Policy Modeling, 33(4); 523-536. Ruiz Estrada, M.A., (2017). “An alternative graphical modeling for economics: Econographicology”, Quality and Quantity, 51(5): 2115–2139 World Health Organization (WHO). General data. www.who.org 28 CHAPTER IV How Long more the World Economy can Resist the COVID-19 Crisis Pressure? Mario Arturo Ruiz Estrada 4.1. Introduction The COVID-19 is testing the world economy structures vulnerability from a domestically, regionally, and globally anytime and anywhere without a selective preference in the socio-economic development stage in countries and regions. The damage is incalculable from the losses of human lives and material (economically). According to WHO (2020), until November 2020, we have sixty million infected cases and 1.5 million of death people worldwide. In the case of economic losses by COVID-19, we can observe that the GDP worldwide drop by 60%, the unemployment rate increase by 21%, and inflation rise by 17%, according to the World Bank in the first ten months of the year 2020. Therefore, this research proposes an alternative economic analytical tool to evaluate how fragile the economy is under the fast expansion of a global pandemic such as COVID-19. Hence, the new economic analytical tool is entitled "The Economic Structures Endurance Levels under COVID-19 Simulator (TESELUCOVID-19 Simulator)." We like to include the importance of the measurement of economic endurance using different economic structures. We assume that all economic structures always interact to find its balance or momentum according to certain favorable conditions and stability. In this research, we omit the classic perception of equilibrium (general or partial), and Ceteris Paribus replaced by the dynamic imbalance state (Ruiz Estrada and Yap, 2013) under the application of the Omnia Mobilis assumption (Ruiz Estrada, 2011). Therefore, any economy always keeps in a permanent dynamic imbalance state (DIS). The economy is still exposed to a crisis and risk permanent risk to collapse the entire economy anytime from internal or external factors shortly. Many economists dominate economic failure(s) represented by economic depression, economic recession, and financial crisis, respectively. All these economic failures have a common dominator as unemployment, inflation, poverty as collateral effects. Economists focus their attention on the study or analysis of the impact but never on the leading causes of the economic failure(s) without including alternative holistic approaches such as sociological, historical, political, technological aspects together into its studies and research. The economic failure(s) survey also implies incorporating quantitative and qualitative elements in its research to consolidate an in-depth analysis and possible solutions through policy modeling (Ruiz Estrada and Park, 2018). But our main concern in this research is to analyze how an economic structure(s) can be affected considerably in different levels of damage. In any case, each economic structure has different levels of risk and vulnerability. We can confirm that if an 29 economic structure collapses immediately, the entire economy starts to feel the impact rapidly because all economic structures are interconnected directly or indirectly. Finally, we applied the economic structures endurance levels under the COVID-19 simulator (TESELUCOVID-19 Simulator) to U.S., EU, China, Japan, and ASEAN economy in the year 2020. Our main objective is to observe from a global perspective how fragile or strong is the world economy in confront COVID-19 and its possible implications for the world economy in the short run. This simulator has and keeps running five primary variables and 1,150 quantitative and qualitative (binary) sub-variables, micro-variables, and Nano-variables with its parameters each one. We are running all these variables together randomly using our algorithm using artificial intelligence at the same time. We are using Mathematica from Wolfram version 12. The TESELUCOVID-19 Simulator can elaborate a serial of calculations simultaneously. For example, the first primary variable (V1) is the maximum of COVID-19 infected cases and death casualties’ cases in percentages that any country can handle to avoid a full collapse of a country (7 Sub-variables and 350 Micro-variables). The second primary variable (V2) is how long a country can keep closed up under a massive quarantine (days and months) (7 Sub-variables and 150 Micro-variables). The third primary variable (V3) is the small and medium business endurance to survive without business (7 Sub-variables and 500 Microvariables). The fourth primary variable (V4) is the government strangeness to support citizens from COVID-19 (subsidies, medical care, and social welfare programs) (7 Sub-variables 100 Micro-variables). The fifth primary variable (V5) is the economic rebuilt timing framework (years) (7 Sub-variables and 50 Micro-variables). However, the concept of variable in our simulator is nominated as structure substantially. 4.2. The Economic Structures Endurance Levels unde r COVID-19 Simulator (TESELUCOVID-19 Simulator): Theoretical Framework The Economic Structures Endurance Levels under COVID-19 Simulator (TESELUCOVID-19 Simulator) has two general objectives. The first objective is to evaluate the points of strength and weakness of different macro-structures in the same graphical space and time. The second objective is to forecast different macro-structures (or scenarios) simultaneously. The TESELUCOVID-19 Simulator is based on the application of the structures coordinate system (Ruiz Estrada, 2017). The structures coordinate system opens up the possibility of generating a multidimensional visual effect to show the vulnerability of many macro-structures (or scenarios) in the same graph, and the same time. Each macro-structure (or scenario) is formed by a large number of general structures, sub-structures and mini-structures that are located on different axes, levels, and structures by size and color (see Fig. 2). However, the detailed analysis of each structure by axes, levels, perimeters and structures, by size and color depends upon the parameters established in our research. Finally, all these general structures, substructures and mini-structures apply the global structural imbalance principle under the 30 application of the Omnia Mobilis assumption (Ruiz Estrada, 2011 & Ruiz Estrada and Park, 2018). The TESELUCOVID-19 Simulator is formed by an infinite number of general axes (A0, A1 ,…, A∞), where each axis shows different levels (L0, L1 ,…, L∞), perimeters (P 0, P 1, P 2…P ∞), and structures with different sizes and colours (C0/β, C1/β… C∞/β). Therefore, the coordinate system of the structures coordinate system is represented by SA:L:P:C = (Ai, Lj, P k, Cs/β) respectively, where i, j, k and s represent different values between 0 and ∞…. , and β represents the different colours of each cube at different levels (L 0, L1 ,…, L∞). All these structures (Cs/β) with different sizes and colours in the same axis under the same level (L0, L1 ,…, L∞) and different perimeters (P 0, P 1, P 2…P ∞) will be joined together, based on the application of the concept called “links structures” represented by the symbol “@”. The structures coordinate system is shown in Expression 1 and Fig. 2. Level P0 @………………. @ Level Pn A0: S 0:0:0:C(0/β) = (A0,L0, P0, C0/β) @ ……. .. @ S0:0:λ:C(α/β) = (A0,L0, Pλ, Cα/β) @ @ S 0:1:0:C(0/β) = (A0,L1, P0, C0/β) @ ………... @ S 0:1:λ:C(α/β) = (A0,L1, Pλ, Cα/β) . . @ @ @ @ S 0:θ:λ:C(α/β) = (A0,Lθ, Pλ, Cα/β) @ ………... @ S 0:1:λ:C(α/β) = (A0,L1, Pλ, Cα/β) A1: S 1:0:0:C(α/β) = (A1,L0, P0, C0/β) @ ……. .. @ S1:0:λ:C(α/β) = (A1,L0, Pλ, Cα/β) @ @ S 1:1:1:C(α/β) = (A1,L1, P0, C0/β) @ ………... @ S 1:θ:λ:C(α/β) = (A1,L1, Pλ, Cα/β) @ @ . . @ @ S 1:θ:λ:C(α/β) = (A1,Lθ, Pλ, Cα/β) @ ………... @ S 1:θ:λ:C(α/β) = (A1,Lθ, Pλ, Cα/β) @ @ An: S n:0:0:C(α/β) = (An,L0, P0, C0/β) @ ……. .. @ Sn:0:λ:C(α/β) = (An,L0, Pλ, Cα/β) @ @ S n:1:1:C(α/β) = (An,L1, P0, C0/β) @ ………... @ S n:1:λ:C(α/β) = (An,L1, Pλ, Cα/β) . . . @ @ . S n: θ: λ: C: α/β = (An,Lθ, Pλ, Cα/β) @ ……………... @ S n+1:θ+1:λ+1:C:α+1/β = (An+1,Lθ+1, Pλ+1, Cα+1/β) (1) n = {1,2,3…∞} θ = {1,2,3…∞} λ = {1,2,3…∞} α = {1,2,3…∞} Note: S = Macroeconomic Structure, A = Axis, L = Level, P = Perimeter, C = Cube and β = Colours 31 Finally, the structures coordinate system shows a general function “Yg” that is the result of the interconnection of all the macro-structures (S0, S1 ,…, Sn) under different axes (A1, A2 ,…, An), levels (L1, L2 ,…, Ln), perimeters (P 0, P 1, P 2…P n) and structures with different sizes and colours (C0/β, C1/β… Cn/β) respectively (see Expression 2). Yg = ƒ (Ao <ΣS0╬ S1╬…S∞> ╬ A1 <ΣS0╬ S1╬…S∞> ╬ …╬ A∞<ΣS0╬ S1╬…S∞> …) (2) Note: Yg = T he General Variable, ╬ = Interconnection, A i = Axis and Si= General Structures. However, the size of all structures by level(s) is based on the parameters we establish in our research. The parameters are fixed according to intervals of growth rates. Any changes in structure size depend on the constant changes between two periods of time. On the other hand, if we assume that all the structures at different levels are changing constantly in real time, then all the structures can experience an expansion, contraction or stagnation at any time. The changes in structure size depend on the constant changes in its growth rates respectively. We propose that all the structures at level zero (mini-structures) are smaller than the structures at level one (sub-structures). In the same way, the structures at level one (sub-structures) are smaller than the structures at level 2 (general structure) (see Fig. 1 and Fig. 2). The five classifications of structures by size and value are shown below: Fig. 1: Structure Parameters Level cero Structure-1 Structure-2 Level one Structure-3 Level two Structure-n Level-n 0 ≥ TVi ≤ V1 V1 ≥ TVi ≤ V2 V2 ≥ TVi ≤ V3 Nano-Variable Micro-Variable Sub-Variable V3 ≥ TVn ≤ Vn Primary Variable Note: T V = T otal Value, V = Value and “n” is equal to any value between 1 and ∞… 32 Fig. 2: The Structure Coordinate System Note: Yg = The General Variable, ╬ = Interconnection, Ai = Axis and Si= General Structures. However, the size of all structures by level is under the uses of parameters that we establish in our researcher. The parameters we fix in this simulator directly depend on the amounts of money or number of units. The changes of all structures size depend on the constant changes in the volume of money or the number of units between n-periods of time. On the other hand, if we assume that all the structures in different levels are changing constantly in real time, then all the structures can experience an expansion, contraction or stagnation. The changes of the structures size depend on the constant changes into its growth rates respectively. We propose that all the structures in the level cero (mini-structures) are smaller than the structures in the level one (sub-structures) and finally the structures in the level one (sub-structures) is smaller than the structures in the level 2 (general structure). 4.3. The Application of the TESELUCOVID-19 Simulator to U.S., EU, China, Japan, and ASEAN This section applies the TESELUCOVID-19 Simulator to U.S, EU, China, Japan, and ASEAN. The application of the TESELUCOVID-19 Simulator is starting with the calculation of the first primary variable (V 1) is equal to (I%/D%). This variable represents the proportion between the maximum percentage of COVID-19 infected cases (I%) and the percentage of 33 death casualties' (D%). This proportion tries to evaluate how much a country can handle COVID-19. According to preliminary results, V 1 for the U.S. is equal to (15%/2.5%). It means that the U.S. only can support until 15% of its population became infected by COVID-19. If the U.S. passes 15%, then the U.S. cannot handle this crisis anymore. Simultaneously, the U.S. needs to keep a moderate rate of death cases from COVID-19 with 2.5% (maximum losses acceptable is 8 million people). The U.S.'s main problem against COVID-19 is the different health care programs coverage at the federal and national level. If we apply I% on the U.S. population, then we have that the U.S. is not available to attend more than 49.6 million infected cases from COVID-19. Additionally, the population desgrowth (U.S. population growth rate minus the COVID-19 growth rate worldwide) is equal to -1.72. Therefore, the Post-COVID19 population recovery (population desgrowth minus birth growth rate) is similar to 0.05. The second primary variable (V 2) in the TESELUCOVID-19 Simulator is how long a country can keep closed up to stop people's full mobility under the application of full quarantines. A full quarantine in the U.S. is only possible for one week, according to our simulator. This short period for a full quarantine in the U.S. originated from the critical role of the massive consumption in the U.S. to keep its economy active, according to Barro (1976), Solow (1956), Samuelson (1983), and Lucas, respectively. The third primary variable (V3) is the small and medium business endurance to survive without business. The U.S. is only able to keep business closed up only for three weeks. The fourth primary variable (V 4) is the government strangeness support (subsidies, medical care, and social welfare programs) for the U.S. is one year using its full resources to support the American economy. Finally, the fifth primary variable (V 5) for the U.S. to recovery from the COVID-19 crisis is two years. Therefore, the date of economic endurance with COVID-19 for the U.S. is until June 2021. If the U.S. doesn't have the vaccination before June 2021, then the American economy can suffer a catastrophic economic cataclysm ever see before it (see Table 1 and Figure 3). According to preliminary results, V 1 for the European Union (EU) shows (17%/3%). The EU only can help 15% of its total population in case of a massive COVID-19 infected cases wave. Hence, the EU cannot overpass more than 3% of death cases from COVID-19 (maximum losses acceptable is 13 million people). The EU's main problem against COVID-19 is the sizeable old population and different health care services in each member. If we apply I% to the EU population (448 Million), then we have that the EU cannot attend more than 76 million COVID-19 infected cases. Additionally, the population desgrowth of the EU is equal to -2.32. The Post-COVID-19 population recovery for the EU is equivalent to -0.77. The second primary variable (V 2) for EU shows can keep full lockout Europe for one month. The third primary variable (V3) for the EU can only keep business closed up for one month. The fourth primary variable (V 4) for the EU is two years using its full resources from taxpayers and different social welfare schemes. Finally, the fifth primary variable (V 5) shows that the EU needs two years to recover from the COVID-19 crisis fully. The date of economic endurance with COVID-19 for EU is until July 2021. (See Table 1 and Figure 3). 34 Moreover, the final result of V 1 for China is equal to (16%/4.5%). China can only help 16% of its total citizens in the case of a massive COVID-19 wave. Also, China cannot has more than 4.5% of death cases from COVID-19 to avoid population desgrowth (maximum losses acceptable is 63 million people). China's main problem against the COVID-19 is the large population of 1.4 billion people and different health care services disparity in each region and prefecture. If we apply I% to the Chinese community, then we have that China cannot attend more than 224 million infected cases of COVID-19 at the same time. Additionally, China's population desgrowth is equal to -1.82, and the Post-COVID-19 population recovery for China is equivalent to -0.14. The second primary variable (V2) for China only can lockout for two weeks. The third primary variable (V 3) in China's case can only keep business closed up for two weeks because of its large population demand and consumption. The fourth primary variable (V4) in China is one year using its full resources from taxpayers and different social welfare schemes per region. Finally, the fifth primary variable (V 5) shows that China needs six months to recover from the COVID-19 crisis fully, and the date of economic endurance with COVID-19 for China is until March 2021. (See Table 1 and Figure 3). For Japan, the result of V 1 is equal to (20%/2%). Japan can help 20% of its total people in case of a massive COVID-19 wave. Hence, Japan cannot has more than 2% of death cases (maximum losses acceptable is 3 million people). Japan's main problem against the COVID19 is related to its population (126 million) most enormous gap between younger and older population disparity per region and prefectures. If we apply I% to Japan, then we have that this country cannot attend more than 25 million infected cases of COVID-19 in the short run. Additionally, the population desgrowth of Japan is around -2.34. The Post-COVID-19 population recovery for Japan is the largest with -0.91 compared to the U.S., EU, China, and ASEAN. The second primary variable (V 2) for Japan can only lockout for one week. The third primary variable (V3) for Japan can only keep business closed up for one week originated from the high cost of leaving and large population concentration in large cities such as Tokyo, Osaka, Kyoto, Sapporo, Hiroshima, and Nagasaki. The fourth primary variable (V 4) for Japan is three years using its full resources from taxpayers and savings. Finally, the fifth primary variable (V5) Japan needs one year to recover from the COVID-19 crisis fully. Hence, the date of economic endurance with COVID-19 for Japan is until May 2021. (See Table 1 and Figure 3). Finally, the V1 of ASEAN is equal to (11%/5%). ASEAN can only help 11% of its total population in the case of a massive COVID-19 wave. Hence, ASEAN cannot has more than 5% in death cases from COVID-19 (maximum losses acceptable is 32 million people). We can observe that ASEAN can resist COVID-19 more than U.S, EU, China, and Japan. According to our preliminary results, ASEAN's main problem with containing COVID-19 is related to its different health care system, and public health budgets offer by each member. ASEAN show a large population of 647 million people. If we apply I% to ASEAN, then we have that this region cannot attend more than 71 million COVID-19 infected cases in the short term. Additionally, 35 the population desgrowth of ASEAN is equal to -0.72. However, the Post-COVID-19 population recovery for ASEAN is robust with 1.39. The main reason the post-COVIS-19 population recovery shows a positive result; is its young population and high fertility rates. The second primary variable (V2) for ASEAN is that they can only lockout for three weeks. The third primary variable (V3) for ASEAN can only keep business closed up for three weeks for each member's different market structures. The fourth primary variable (V 4) for ASEAN, they can survive for six months under the intensive uses of domestic and external credits. Finally, the fifth primary variable (V 5) for ASEAN shows that this region needs three years to recover from the COVID-19 crisis fully. Hence, the date of economic endurance with COVID-19 for ASEAN is until May 2021. (See Table 1 and Figure 3). Table 1: Final Results from TESELUCOVID-19 Simulator Main Variable U.S. E.U. China Japan ASEAN V1 15%/2.5% 17%/3% 16%/4.5% 20%/2% 11%/5% Population 328 448 1400 126 647 Maximum Losses 49/8 76/13 224/63 25/3 71/32 Population Desgrowth (0.6- 2.32 = -1.72) (0.164- 2.32 = -2.32) (0.5- 2.32 = -1.82) (-0.2- 2.32 = -2.34) (1.6- 2.32 = -0.72) Post-COVID-19 Population Recovery (1.77 - 1.72 = 0.05) (1.55 -2.32 = -0.77) (1.68 - 1.82 = - 0.14) (1.43 - 2.34 = -0.91 (2.11 - 0.72 = 1.39) V2 1 Week 1 Month 2 Weeks 1 Week 3 Weeks V3 3 Weeks 1 Month 2 Weeks 2 Weeks 3 Weeks V4 1 Year 2 Years 1 Year 2 Years 6 Months V5 2 Years 2 Years 6 Months 1 Year 3 Years Date of Economic Endurance with COVID-19 Jun-21 Jul-21 Mar-21 May-21 Feb-21 Source: WHO (2020) and WB (2020) 36 Fig. 3: Final Results from TESELUCOVID-19 Simulator Graphically Source: WHO (2020) and WB (2020) 4.4. Conclusion This research concludes that developed countries such as the U.S., EU, China, and Japan are more vulnerable than developing countries (ASEAN) in the COVID-19 crisis. In the case of population desgrowth, Japan is the most affected, and the second is the EU, the main reason is the large older population sizes (around 60%). In the case of ASEAN, the population desgrowth is lower because of its young population and high fertility rate. However, the Post-COVID-19 population recovery follows ASEAN, U.S., China, EU, and Japan. The maximum of COVID19 infected cases and death casualties' in percentage shows Japan in the first place, subsequently by EU, China, U.S., and ASEAN. About how long a country can keep closed up under a massive quarantine(s), large economies like U.S., Japan, China, and the EU cannot keep an enormous quarantine because they depend on the vast consumption always to keep moving the economy actively. Therefore, in large economies, small and medium businesses cannot survive without transactions in the long run. We can observe government support (subsidies, medical care, and social welfare programs) plays a vital role in helping any economy in a global pandemic. According to our simulator, the economic rebuilt timing framework from the post-COVID-19 is following by U.S. (two years), EU (two years), China (six months), Japan (one year), and ASEAN (three years). We concluded that developing countries would take longer than developed countries according to preliminary results in this research. Finally, the date of economic endurance with COVID-19 shows the next dates: U.S. (June 2021), EU (July 2021), China (March 2021), Japan (May 2021), and ASEAN (February 2021). These results can show each country or region's dateline to fall in an economic and political crisis. 37 References Barro, R. (1976). Rational expectations and the role of monetary policy. J. Monetary Econ. 2: 1–32. Ruiz Estrada, M.A. (2011). Policy modelling: Definition, Classification, and Evaluation. Journal of Policy Modeling, 33 (4), 523-536. Ruiz Estrada, M. A., & Yap, S. F. (2013). The Origins and Evolution of Policy Modeling, Journal of Policy Modeling, 34 (1), 170-182. Ruiz Estrada, M.A. (2017). An Alternative Graphical Modeling for Economics: Econographicology. Quality and Quantity. 51(5): 2115-2139. Ruiz Estrada, M.A. Park, D., (2018). The Past, Present, and Future of Policy Modeling. Journal of Policy Modeling, 40(1), 1-15. Samuelson, P. (1983). Foundations of Economic Analysis. U.S. Harvard University Press, Cambridge. Solow, R. (1956). A contribution to the theory of economic growth. Quart. J. Econ. 70: 65– 94. WHO (2020). Database. https://www.who.int/. Accessed on April 15, 2020. World Bank (2020). Annual report. http://www.wb.org. Accessed on April 1, 2020. 38 Chapter V How much COVID-19 Affects the Conservation of Economic Energy (EE) of the U.S.? Mario Arturo Ruiz Estrada 5.1. Introduction Before explaining how to measure the conservation of economic energy (E E), I would like to give a general summary of the special theory of relativity and how it works. The significant contribution of Professor Albert Einstein (1952) is the establishment and development of the special theory of relativity. It is based on the amalgamation of two fundamentals laws in physics into a single law. This single law is called the special theory of relativity. The law of conservation of energy (C2) and the law of conservation of mass (M) are combined into a single equation identified by E=MC2. According to Albert Einstein: “if followed from special theory of relativity that mass (M) and energy (E) are both but different manifestations of the same thing. A somewhat unfamiliar conception for the average mind: furthermore, the equation energy (E) is equal to MC 2, in which energy is put equal to mass (M), multiplied by the square of the velocity of light shows that very small amounts of mass may be converted into a very large amount of energy and vice-versa. The mass (M) and energy (E) are in fact equivalent according to the formula mentioned before this by Cockcroft and Walton in 1932, experimentally.” (Einstein, 1952) This chapter attempts to use the theory of relativity to measure the conservation of economic energy (EE) and demonstrate that it is possible to apply the formula E=MC2 into the economics analysis. I will use two variables - the unemployment growth rate (ΔU) and the speed of COVID-19 human damage speed (ΔC2) to measure the final conservation of economic energy (EE). The exploration proves that the measurement of conservation energy of economics (EE) can be an alternative approach to analyzing the macroeconomics behavior in any country or region. Introduction to the conservation of economic energy (EE) for a start, we suggest applying the equation E=MC2 to measure the conservation of economic energy (EE). Hence, I replace all the original variables from E=MC 2 with two economic variables to measure the conservation of economic energy (EE). The Energy (E) needs to be replaced by the conservation of economic energy (EE); the mass (M) needs to change to the unemployment growth rate “ΔU” (See Expression 1), and the ΔC2 is necessary to use COVID-19 human damage speed represented by “ΔC2” (See Expression 2). First, we will calculate the final unemployment growth rate for two years. We apply partial differentiation based on time - two periods of time divided by the past year (t) and the next year (t+1). 39 δUt+1/δUt ≡ ΔU (1) For the construction of the COVID-19 human damage speed (ΔC2) is based on the total sum of three large integrals under the uses of the total sum of COVID-19 infected cases registered (∫Σci) plus the total sum of the COVID-19 death cases (Σ∫dj) plus the total sum of COVID-19 non-recovered cases (∫Σθnr) (See Expression 3). Hence, the variable "t" represents time. In this equation, we calculate time-based on a growth rate between two months. To measure the COVID-19 human damage speed (C2), we applied the original formula of speed equal to distance divided by time (D/t). Still, in this case, we replace distance by the COVID19 patients attended in hospitals daily "δP." We assume that by the COVID-19 patients attended in hospitals daily speed (P 2) is not a constant variable, such as the speed of light explained into the formula E=MC2. We suggest measuring the conservation of economic energy (EE) to keep by the COVID-19 patients attended in hospitals daily speed (P 2). P 2 supports constant challenges according to COVID-19 contagious ways, temperatures, and people's mobility that might generate continuous changes. To treat the expansion of COVID-19 on the application of social distancing, masks, gloves, and uses of sanitizers into society. Secondly, we assume that P 2 requests absolute accuracy. Still, in COVID-19, spread behavior cannot keep constant because the COVID-19 spread depends on social behavior, culture, medical care, social security, transportation systems, quarantine systems, and regulatory frameworks. However, another reason for the COVID-19 human damage speed (ΔC2) to be an exponential square is because we assume that the COVID-19 human damage speed (ΔC2) can generate a double spillover negative effect on the final amount of conservation of economic energy (E E) in the short run. In this part of this model, we insert the application of the partial differentiation into the measure of the δV (velocity of COVID-19 spread) and δT (Periods of COVID-19 incubation) (See Expression 4 and 5). P 2 ≡ (δV/δt)2 (2) ΔC2 = ∫Σci + Σ∫dj + ∫Σθnr (3) δV = δVt+1/ δVt (4) δT = δTt+1/ δVt (5) Finally, the formula of the conservation of economic energy (EE) is equal to: EE=UΔC2 (6) 40 The formula of the conservation of economic energy (EE) can show four possible scenarios as follows (see Figure 1): First, suppose we have a low unemployment rate (U) multiplied by the high COVID-19 human damage speed square (ΔC2). In that case, UΔC2 together can convert into a massive amount of conservation of economic energy (EE). Second, suppose we have a high unemployment rate (U) multiplied by the low COVID-19 human damage speed square (ΔC2). In that case, UΔC2 together can convert into a minimal amount of conservation of economic energy (EE). Third, suppose we have a high unemployment rate (U) multiplied by the high COVID-19 human damage speed square (ΔC2). In that case, UΔC2 together can convert into a tiny amount of conservation of economic energy (EE). Fourth, suppose we have a low unemployment rate (U) multiplied by the low COVID-19 human damage speed square (ΔC2). In that case, UΔC2 together can convert into a minimal amount of conservation of economic energy (EE). Fig. 1. The Behavior of the Conservation of Economic Energy (EE) Source: Author To measure the conservation of economic energy (EE), we apply the application of the Omnia Mobilis Assumption translated from Latin means “everything is moving” (Ruiz Estrada, 2011). The Omnia Mobilis assumption gives the freedom to our equation of conservation of economic energy (EE) to use less ceteris paribus assumption into our equation. Simultaneous ly , 41 we assume also that the market always is in a “Constant Dynamic Imbalanced State” under lack of control and high vulnerability (Ruiz Estrada and Yap, 2013). The concept of equilibrium in economic modeling of conservation of economic energy (E E) is considering as a leak momentum of balance among unemployment growth rate (U) and the COVID-19 human damage speed (ΔC2) that can appear any time. However, we cannot predict when exactly this synchronized balance is going to occur. 5.2. The Application of Conservation of Economic Energy (EE) in the U.S. Applying the conservation of economic energy (EE) to the U.S. economy will give us a better idea of how the model works. We are using database from the World Bank -WB- (2020) and World Health Organization -WHO- (2020). Before we do so, it is useful to look at the local economic data about the U.S from March 2020 to November 2020. Such data includes the unemployment and COVID-19 human damage speed square calculations to find the final conservation of economic energy (EE) of the U.S. economy by regions. We found that the lowest EE in the U.S. was between May and June 2020 for the national level around 0.1008 and the Northeast Coast of the U.S., for example, New York with lowers E E equal to 0.1008, while the U.S. South West Coast has better EE with 0.0721. Therefore, the better EE performance is the middle part of the U.S. that collectively account for 0.0145 of the country's overall output. While between September to October 2020, the Northeast coast's E E shows 0.1115, the South West coast's EE reduces about 0.0957. A similar trend could be observed the EE of the central part of the U.S. keeps relatively higher with 0.0115. Therefore, we can see that the U.S.'s central part keeps relatively large levels of E E to support the U.S. economy. They are these North Middle part with 0.00375. According to this research, the States located in the middle part of the U.S. indicate lower unemployment and the COVID-19 human damage speed in less scale compared to East and West Coast of the U.S., respectively. 5.3. Conclusions COVID-19 has a significant negative impact on the Conservation of economic energy but measuring such impact with any degree of certainty is an impossible task. In this research, we proposed a new indicator is entitled "The Conservation of the economic energy (EE). The underlying presentiment is that the economic impact of economics (E E) depends on a country's keeping lower unemployment rates and lower COVID-19 humans damage speed, which jointly determines the leakage in the economic conservation energy. Hence, the impact on economic growth. We believe that this indicator will contribute to a better and more in-depth understanding of measuring the effects of COVID-19. A more useful measurement of the Conservation of economic energy (EE) is conducive for appropriate policies, both for dealing with better control systems and a possible vaccination for COVID-19 and anticipatory policy measures, which seek to lessen the impact of unemployment before they occur. For example, 42 on the one hand, underestimating the effect may lead to the government cutting tax, subsidies, and new jobs in the public sector for addressing to support for households most affected by unemployment. Determining the appropriate level of public and private investments to limit the impact of future unemployment would benefit from an accurate ex-ante assessment of their impact. At a broader level, results in confirm that the Conservation of economic energy (E E) in the post-COVID-19 crisis. Better measurement allows for more efficient and better-targeted use of monetary and fiscal resources. One interesting direction for future research is to examine the importance of ICT in mitigating unemployment's adverse impact. Therefore, more and better information is likely to reduce unemployment, and looking at the role of ICT would minimize the damage of the post-COVID-19 crisis. Finally, this research concludes that the Conservation of economic energy (EE) shows that U.S. should strive to keep low unemployment growth rates (U) and higher control on the COVID-19 human damage speed (ΔC2) in the short and long term. Better management of ΔC 2 can generate a more considerable amount of Conservation of economic energy (EE) into the U.S. economy. References Einstein, A. (1952). Relativity: The Special and the General Theory, New York: Three Rivers Press. Ruiz Estrada, M.A. (2011). “Policy Modeling: Definition, Classification, and Evaluation.” Journal of Policy Modeling, 33(4): 523-536. Ruiz Estrada, M.A. and Yap, S.F. (2013). “The Origins and Evolution of Policy Modeling.” Journal of Policy Modeling, 35(1): 170-18. WHO (2020). Database. https://www.who.int/. Accessed on April 15, 2020. World Bank (2020). Annual report. http://www.wb.org. Accessed on April 1, 2020. 43 Chapter VI A New Way to Measuring the GDP in the COVID-19 Crisis: The Input-Output Electronic Online Transactions Monitoring System (IOEOTM-System) Mario Arturo Ruiz Estrada 6.1. Introduction The introduction of the input-output model by Professor Wassily Leontief deals with a big question: “What level of output should each of the n industries in an economy produce, so that it will just be sufficient to satisfy the total demand for that product…” (Leontief, 1951). The input-output model proposes a static model under a partial equilibrium. Hence, the input-output model is not showing a general equilibrium or dynamic modeling. Therefore, the main objective of generating the input-output model is always too simplified by three production sectors (agriculture, industry, and services). In the original chapter wrote by Professor Leontief, the services sector appears as householders (Leontief, 1985). The basic structure of the inputoutput model shows a large number of production sectors, but we can observe that almost all the examples are based on three sectors' uses. This model has a serial of assumptions follow by:   First assumption is that each production sector produces a single homogeneous commodity. Second assumption is that the model is working under a fixed input ratio.  Third assumption is that all production sectors work under the constant returns to scale. Maybe the input-output model looks simplistic, but we cannot deny the significant contribution of Professor Leontief to propose a new economic model to study the entire economy as a whole. Hence, his concern was to evaluate how the economy works under different productive sectors such as agriculture, industry, and services sector. But the main objective was to calculate the minimum of output to produce a specific commodity to satisfied the primary demand in any country. Therefore, this chapter proposes an alternative model approach to study input-output analysis from a multi-dimensional perspective. The new model is entitled "The Input-Output Electronic Online Transactions Monitoring System (IOEOTM-System)." The IOEOTM-System tries to incorporate many goods and services "j" strategic sub-sectors "i" and four main strategic sectors in our analysis. Additionally, The IOEOTM-System applies Econographicology, matrix algebra, multi-dimensional partial differentiation, and economic modeling in real-time (Ruiz Estrada, 2017). 44 6.2. The Input-Output Electronic Online Transactions Monitoring System (IOEOTM-System): Theoretical Framework Initially, we have a large number of "j" goods and services generated by "i" large number of strategic sub-sectors (See Expression 1). In our case, we have four main strategic sectors follow by the producers, sellers, logistic, and final consumer(s) (See Expression 2, 4, 6, and 8). These four main strategic sectors' final output depending on the final total outcomes from the interaction among all strategic sub-sectors in each of the four dynamic sectors, respectively (See Expression 2, 4, 6, and 8). For example, the producers' sector (main strategic sector one "S 1 ") exists a large number of electronic transactions online (payments) of "j" number of raw materials by "i" from other producers or suppliers. We propose an infinite strategic sub-sector into the producers' sector or "S1". For example, the consumption of raw materials involves many transactions among producers until the final product arrives in the sellers. Therefore, we need to assume that any commodity or service's production speed is related to the ICT services (internet, phones, computers) and dynamic logistic systems in the market. On the other hand, IOEOTM-System assumes that the competition can generate more innovation of new goods and services under low-cost production in the generation of high demand among final consumers. Additionally, we can also observe that each leading strategic sector can experience an unexpected higher or lower demand or supply of goods and services at any time according to COVID19 infected cases daily (C) (See Expression 1). To use the IOEOTM-System is necessary to assume that the market always keeps in a constant Dynamic Imbalance State (DIS) (Ruiz Estrada, 2013). To support the uses of DIS requests the application of the assumption Omnia Mobilis (Ruiz Estrada, 2011) to include any large or small transaction(s) of commodities or services payments from the final consumer(s) until the primary producers in the same period. ∂Slij = f(∂C) (1) Where i= {0,1,2,…,∞…}; j = {0,1,2,…,∞…}; l = {1,2,3,4}. According to 1 = Producer sector i = strategic sub-sector j = goods and services. In equation 1 exist two premises follow by: First premise, if δSlij increases the number of online transactions, it originates from the higher number of COVID-19 cases. Second premise, if δSlij decreases the number of online transactions, then it is derived from a partial reduction in the number of COVID-19 cases. Sector one (S1 ) represents the producer shows a large number of strategic sub-sectors. Each strategic sub-sector exists the production of an infinite number of goods and services and electronic transactions (payments) online. The final output into the box below each matrix can show the last number of electronic transactions (payments) online daily. After, we can calculate the exchange of goods or services daily. The same situation is possible to observe in the main strategic sector-2 (S2 = sellers), main strategic sector-3 (S3 = logistic), and main strategic sector-4 (S4 = final consumers). 45 Sector S1 : Producers Sector S1 T100 T 101 T 100 T 101 . [δT100:δT101] 0 [δT 101:δT100] T 102 T 102 . . 0 . . . . . . . T 1 TI Σ S 100 Σ S110 . . ... . . Σ S10j 0 .. . Σ S 101 Σ S 100 [δT 101:δT 10j ] ... [δT 10j :δT100] [δT10j :δT101] T 10j T10j S1 TO [δT 100:δT 10j ] . . . . Σ G10j Σ S1 . . . Note: “ T” represents transactions online (2) Σ S1 = ƒ (0 ╦ [δT 100:δT 101] ╦…╦ [δT100 :δT10j] ╦…╦ [δT 10j :δT101 ] ╦ 0) (3) Sector S2 : Sellers or Suppliers Sector S2 T210 T211 T 212 . T 21j T 2 TI T 210 T 211 T 212 . . . . [δT210:δT211] 0 [ΔT 211:δT210] . 0 . . . . ... . [δT 21j :δT210] [δT21j :δT211] Σ S210 . . . Σ S211 T 21j S2 TO [δT 210:δT 21j ] [δT 211:δT 21j ] . Σ S 210 Σ S 211 . ... . . .. . 0 Σ S21j . . . Σ S 21j Σ S2 (4) Σ S2 = ƒ (0 ╦ [ΔT 210 :δT 211 ] ╦…╦ [δT 210:δT 21j ] ╦…╦ [δT21j:δT 211] ╦ 0) (5) 46 Sector S3 : Logistic Sector S3 T320 T320 0 T 321 T 321 [ΔT 320:δT321] [δT 321:δT320] T 322 . 0 . . T 32j [ΔT 32j :δT320] . . . [δT32j :δT 321] Σ S 320 T 32j Σ S 321 . S3 TO [δT 320:δT 32j ] Σ S 320 [δT321:δT32j ] Σ S 321 . . . . T 3 TI T 322 . . . ... . . ... . . .. . 0 Σ S32j Σ S 32j Σ S 3 . . . (6) Σ S3 = ƒ (0 ╦ [δT 320:δT 321 ] ╦…╦ [δT320:δT 32j] ╦…╦ [δT32j:δT 321] ╦ 0) (7) Sector S4 : Final Consumers Sector S4 T4n0 T 4n1 T 4n2 . T 4nj S4 TI T4n0 T 4n1 T 4n2 . . . . [ΔT 4n0:δT 4n1] 0 [δT 4n1:δT4n0] 0 . . . . [δT nj :δTn0] [δTnj :δTn1] Σ S 4n0 Σ S4n1 . . T 4nj S4 TO . . [δT 4n0:δT 4nj ] Σ S 4n0 . [δT 4n1:δT 4nj ] Σ S4n0 ... . ... .. . . . . . . 0 . Σ S 4nj Σ S 40j Σ S 4 (8) Σ S4 = ƒ (0 ╦ [δT 4n0:δT 4n1 ] ╦…╦ [δT4n0:δT 4nj] ╦…╦ [δT4nj:δT 4n1] ╦ 0) (9) 47 In this part of the IOEOTM-System, we suggest plotting each central strategic sector into four different surfaces. To build each surface, we need to use such a reference all strategic sub-sector outputs can be plotted on the surface mapping coordinate system. It can facilitate to make of each multidimensional surface for each main strategic sector. After we need to plot each strategic sub-sector, we join all strategic sub-sectors by straight lines from the same main strategic sector until we can build a single surface. The main idea to create the four multi-dimensional surfaces is to observe the transactions' behavior (payments) online exchange of all strategic sub-sectors "i" by exchanging many goods and services in the same graphical space. We want to remark that the center part of each surface is equal to 0. The reason is that the same sub-sector cannot do its transaction for itself (See Figure 1). Fig. 1: GDP-Surface Source: Author The IOEOTM-System requests the application of the multi-dimensional partial differentiation (See Annex) to observe the changes of two periods of time between the final time (t+1) and the initial time (t). Also, in this part of the model, we suggest the application of the economic modeling in real-time “☼” (Ruiz Estrada, 2017) that consists of the successive application of differentiation to observe the changes into the four strategic sectors simultaneously (See Expression 10). We also suggest applying the inter-link of all production sub-sectors based on using the inter-link coordinate axis condition represented by “╦.” ☼ΣS1 i ≡ ΣS1 ’ = δ ƒ’ (S1)t / δ (S1 )t+1d’S1 ╦ ΣS1’’= δƒ’’(S1)t/δ (S1)t+1 d2S1 ╦ ΣS1∞ = δ ƒ∞(S1 ) / δ (S1)t+1 d∞S1 ☼ΣS2 i ≡ ΣS2 ’ = δ ƒ’ (S2)t / δ (S2)t+1d’S2 ╦ ΣS2 ’’= δƒ’’(S2)t/δ (S2 )t+1 d2 S2 ╦ ΣS2∞ = δ ƒ∞ (S2 ) / δ (S2)t+1 d∞S2 ☼ΣS3 i ≡ ΣS3 ’ = δ ƒ’ (S3)t / δ (S3)t+1d’S3 ╦ ΣS3 ’’= δƒ’’(S3)t/δ (S3 )t+1 d2 S3 ╦ ΣS3∞ = δ ƒ∞ (S3 ) / δ (S3)t+1 d∞S3 48 ☼ΣS4 i ≡ ΣS4 ’ = δ ƒ’ (S4)t / δ (S4)t+1d’S4 ╦ ΣS4 ’’= δƒ’’(S4)t/δ (S4 )t+1 d2 S4 ╦ ΣS4∞ = δ ƒ∞ (S4 ) / δ (S4)t+1 d∞S4 (10) However, the construction and the final analysis of the input-output multi-dimensional analysis consist of plotting all multi-dimensional partial differentiation from each production sector: producers, sellers or suppliers, logistics, and final consumers (See Expression 11) on the four-dimensional physical space coordinate system. (Ruiz Estrada, 2017). To join the four strategic sectors into the same graphical modeling, we suggest inter-link the four production sectors based on applying the inter-link of the general coordinate condition that is represented by “╬.” GDP-Surface ≡ ☼S* ≡ ☼ΣS1 i ╬ ☼ΣS2i ╬ ☼ΣS3i ╬ ☼ΣS4i (11) The final output of the result input-output multi-dimensional analysis, we are calling "GDPSurface." It depends on the last position that the GDP-surface shows into the four-dimensional physical space coordinate system. We have four possible results (See Expression 12, 13, 14, and 15) to analyze the GDP-Surface's behavior according to the speed of transactions online (payments) of goods and services by strategic sector and strategic sub-sectors. ☼S* ≡ ☼ +ΣS1 i ╬ ☼ +ΣS2 i ╬ ☼ +ΣS3 i ╬ ☼ +ΣS4 i (12) {if +☼S* ∩ R+ then the surface ≡ Growth sustainability} ☼S* = 0 ≡ ☼ ΣS1 i = 0 ╬ ☼ ΣS2 i = 0 ╬ ☼ ΣS3 i = 0 ╬ ☼ ΣS4 i (13) {if ☼S* ∩ 0 then the surface ≡ Growth Stagnation} ☼ ±S* ≡ ☼ ±ΣS 1 i ╬ ☼ ±ΣS2i ╬ ☼ ±ΣS3 i ╬ ☼ ±ΣS4i (14) {if ☼S* ∩ R+/- then the surface ≡ Irregular Growth} ☼-S* ≡ ☼ -ΣS1 i ╬ ☼ -ΣS2i ╬ ☼ -ΣS3 i ╬ ☼ -ΣS4 i (15) {if ☼S* ∩ R- then the surface ≡ Growth Inconsistency} 49 Fig. 2: GDP-Mini-Surface Source: Author Finally, the application of IOEOTM-System requests to elaborate a new electronic platform (official government homepage) to connect an extensive database from all producers, sellers (suppliers), logistics, and the final consumers' electronic transactions online daily. Subsequently, we suggest generating a monthly database using all electronic transactions online daily. At the end of the month, we can proceed to quantify the total electronic transactions online monthly. The final step is to sum the twelve months of all electronic transactions online to build the definitive annual database. The main objective of the IOEOTM-System is to collect enough data for one year at the national level to elaborate the GDP by the central bank and the ministry of finance (income tax). COVID-19 forces us to create a new way to evaluate the economy's macroeconomic behavior from a different perspective. 6.3. Conclusion This research concludes that COVID-19 forces us to reinvent a new national account system using electronic online transfers from producers, sellers (suppliers), logistics, and final consumers spending electronically. The main objective is to catch up with the exchange of commodities and services among different strategic sub-sectors in the same strategic sector. The same model also is possible to observe the interaction among the four main strategic sectors (producers, sellers, logistic, and final consumers) by using a multi-dimensional mathematical approach and graphical modeling. Finally, Professor Leontief's contribution was significant, but not enough to explain the dynamic economic behaviors' behavior in our times. References Ruiz Estrada, M.A. (2011). “Policy Modeling: Definition, Classification, and Evaluation.” Journal of Policy Modeling, 33(4): 523-536. Ruiz Estrada, M.A. & Yap, S.F. (2013). “The Origins and Evolution of Policy Modeling.” Journal of Policy Modeling, 35(1): 170-18. 50 Ruiz Estrada, M.A. (2017). An Alternative Graphical Modeling for Economics: Econographicology. Quality and Quantity. 51(5): 2115-2139. Leontief, W. (1951). The Structural of America Economy 1919-1939, Second Edition Input Output Economic. Oxford University Press. 51 Chapter VII The Impact of COVID-19 on the World Oil Prices Mario Arturo Ruiz Estrada 7.1. Introduction In this section, this research brief describes the effects of the COVID-19 crisis on world oil prices. Historically, global oil prices have been subject to a great deal of volatility from any financial crisis (1929 and 2006) until massive pandemics (1920 and 2020). Different historical events depict the trend of global oil prices from the last two centuries. We can observe that the COVID-19 crisis caused a global oil price disruption, from US$17 on 19 April 2020 to -US$35 on 20 April 2020, followed by the next day, 21 April 2020, with -US$6 (see Figure 1 and 4). Fig. 1: Trends in the world oil prices in COVID-19 Crude Oil Price Per Barrel in US$ 18 18 17 17/4/2020 18/4/2020 19/4/2020 15 20/4/2020 21/4/2020 -6 22/4/2020 -35 Source: OPEC (2020) On 20 April 2020, oil prices fall due to the uncontrolled COVID-19 crisis worldwide. The subsequent 22 April 2020, OPEC led to a contraction of the oil production to pull up again the world oil price to US$15. The decline of oil demand worldwide, partly due to a massive world quarantine and a considerable reduction in the massive consumption of products and services, pushed down the world oil prices until red numbers (negative). The main reason why world oil price drops too much on 20 April 2020 was the massive increment of COVID-19 cases, especially in United States (800,000 infected cases and 40,000 deaths), Europe (600,000 infected cases and 80,000 deaths), Asia (300,000 infected cases and 50,000 deaths) according 52 to WHO (2020). Between 20 April 2020 and 21 April 2020, the prices of oil drop to negative rates rapidly due to the labor mobility was lockdown –quarantines-. Systematically, the industrial plants were closed (U.S., China, Asia, Europe), the massive tourism contraction, less demand for extensive transportation services (airways, cruisers, and railways), and the stagnation of a large number of construction projects. Until now, the global oil demand cannot show some recovered in line until the vaccination and quarantine stop in the large economies around the world simultaneously. In April 2020, the global oil prices hit negative values two times in the same month between -US$35 (20/4/2020) and -US$6 (21/4/2020) according to OPEC for the first time since the great recession of the year 1929, mainly due to the most massive pandemics (COVID-19) of the XXI century. In this month, rapidly dropping production of shale oil in the Emirates Arabs, U.S., and large producers started to have symptoms of a possible sizeable economic depression. Since then, global oil prices have been extremely volatile. Global oil prices fell from US$18 per barrel on 19 April 2020 to -US$35 on 20 April 2020, before rebounding to US$15 by 22 April 2020. COVID-19 contagious cases and causalities drove the fluctuation in these specific dates. According to recently, documents from the OPEC member countries try to reduce production is via oil quotas production since lower oil prices. The lower oil prices force OPEC to take drastic measures to stabilize oil prices to maintain relative stability in the world economy. We need to remember that world oil prices also significantly influenced by non-oilproducers such as Japan and some European countries. Non-oil-producers demand for about 60% of world oil production, which has a significant impact on the global oil prices trend. However, the analysis and visualization of oil prices' behavior were carried out with a classical 2-dimensional graphical approach. The 2-dimensional graphical method helps us to visualize the behavior of a simple database in different periods in the same graphical space. Usually, the analysis of oil prices graphically interpret a group of points located in various locations representing a specific period and different levels of prices into the first quadrant of the 2-dimensional Cartesian plane. According to Econographicology, the 2-dimensiona l graphical approach is based on the Cartesian plane coordinate system fixed by X-axis (time) and Y-axis (price). At the same time, both axes help us to visualize the evolution of extended historical data in different periods in absolute values (fixed amounts or average results) or relative values (growth rates). In this chapter, we found that the most common 2-dimensional graphical approach representation of oil price visualization is based on the use of histograms (e.g., bars, scatterplots, lines) in different colours and sizes. Traditionally, the hologram can show the behavior of oil prices superficially from both linear and non-linear perspectives. The limitation of the 2dimensional graphical approach is the limited perception of oil prices as a whole. On the other hand, the Infinity Physical Space (hereafter "I-Physical Space)" proposed here has more flexibility and incorporates a large number of spatial factors, as shown in Table 1. 53 Tab. 1: Difference between 2-Dimensional and Infinity Physical Space Dimension 2-Dimensional Infinity Physical Space (I-Physical Space) Coordinates Spatial Factors Function ( X,Y ) (X,Y) Y = ƒ( X ) [(XC:L:n , PC:L:n , RC:L:n) , YC:L:n] n = {0,1, 2,3…+∞} c = {1,2} L = {0,1, 2,3…+∞} R= {0,1,2,…,360o } YC:L:n = ƒ(XC:L:n , PC:L:n , RC:L:n) Source: Ruiz Estrada, 2017. Besides, this research interested in visualizing the behavior of oil prices from a multidimensional approach. The motivation behind using multidimensional graphs is to observe the vulnerability of oil prices by a pandemic (Ruiz Estrada, 2017). This new multidimensional graphical method is the “I-Physical Space”. In the first instance, we assume that I-Physical Space has N-dimensions and N-sub-dimensions, as demonstrated by Ruiz Estrada (2017). Also, we evaluate the behavior of oil prices in five periods, namely Period-I: December 2019; Period-II: January 2020; Period-III: February 2020; Period-IV: March 2020; and Period-V: April 2020. This method will enable a more precise, accurate, and consistent analysis of the evolution of the behavior of oil prices. Therefore, the proposed multidimensional graphical approach can be useful for policymakers since oil price is one of the most important macroeconomic variables (Ruiz Estrada 2011 and 2013). This rest of the chapter is organized in the following manner. Section 2 presents the theoretical framework. In section 2, we highlight the steps in involved in developing the IPhysical Space, which is a useful tool for visualizing the behavior of oil prices. In Section 3, we report and discuss the findings related to the conduct of oil prices based on the multidimensional graphical analysis. Section 4 concludes the chapter. 7.2. The Infinity Physical Space (I-Physical Space) Theoretical Framework The I-Physical Space (Ruiz Estrada, 2017) consists of a series of n number of sub-cylinders “C” located in the same general cylinder. Each sub-cylinder in the same cylinder is fixed by its level “L” respectively, where L = {1, 2, 3,,, k}, k → ∞… with different “n” values between 1 54 and ∞. Plotting the different sub-cylinders in the same general cylinder is done using the subcylinder location, position, and ratio, where (XC:L:n) is an independent variable in “n” value in sub-cylinder “C” at level “L” lying in position P C: L: n with value RC:L: n. The position locate P C:L: n 0◦ ≤ P C: L:n < 360◦ is the position of XC: L:n in cylinder “C” at level “L.” The location of ratios under the RC: L: n is the radius corresponding to the XC: L: n in cylinder “C” at level “L.” Finally, the YC:L:n is the dependent variable at level “L”. The values of the independent variables XC:L:n affect YC:L: n simultaneously. The I-Physical Space function is given below by: YC:L:n = ƒ(XC:L:n ,PC: L:n , RC: L:n) n = 1, . . . ∞. For example, the value of a specific independent variable at time point 1, say X1:1:1 is plotted as R1:1:1, which is the radius pictured lying on a flat surface at angle P 1:1:1. It is measured from 0◦ line which is used as its reference line. The points from the end of the radius are joined to meet in a single point on the top of each sub-cylinder at height Y1:1 and level “L”. The diameter of the sub-cylinder is twice the maximum radius (see Figure 1: section “b” and Prototype). However, the I-Physical Space is an alternative physical space which allows us to visualize different types of graphs from a different perspective. The I-Physical Space is based on “n” independent variables (X1, X2, X3…Xn; n =1…+∞) and one dependent variable “Y.” But the dependent variable “Y” may have both positive and negative values. The new modality of “Y” is its location in different positions in the circle parametric of the infinity physical space. The construction of the circle parametric built by joining all independent variables i.e. Xi (X1, X 2, X 3…Xn) creates a cylinder. It is assumed that the dependent variable “Y” has high mobility into the circle parametric. We demonstrate the position of “Y” in the circle parametric. And this is the prime motivation behind the analysis carried out in this chapter. The final graph is a cylinder with different levels of time and space dimensions. The graphs in the I-Physical Space can be used to generate different figures, but the analysis of this type of graphs depends on the criteria of the researcher (for example see Figure 2). The function of the I-Physical Space is followed by Y = f (X1, X2, X3... X n); n=1...+∞. The I-Physical Space has the following different characteristics. First, researchers can input any quantity of dependent variables in the I-Physical Space. Second, the dependent variable (Y) can be located in different positions in the I-Physical Space. Third, the new type of graphs is generated in different time and space dimensions (see Figure 2). 55 Fig. 2 the I-Physical Space Section “a” Section “b” Source: Ruiz Estrada, 2017 7.3. How to use the Infinity Physical Space to visualize oil prices? The uses of the I-Physical Space involve the following steps. Step-1 is search for daily oil prices from reliable databases and statistical sources. Step-2 calculates the average oil price. It can be calculated by summing daily oil prices and dividing the sum by 25 days (OPEC daily prices). Step-3 plots each daily average oil price value in each vertical axis in the I-Physical Space. Step-4 confirms the possibility of plotting values in I-Physical Space. For this chapter, we can only plot ten daily sum average oil price values in each I-Physical Space. Step-5 uses Microsoft Movie Maker to join each I-Physical Space in a single video. The purpose of this exercise is to generate multidimensional effect in real time for our analysis. Step-6 analyses the final result of each I-Physical Space with annual sum average oil price values. There are two possible outcomes - stable oil price range and unstable oil price range. In the stable oil price range, all yearly sum average oil price values remain firmly in the oil price range. On the other hand, in the unstable oil price range, some oil prices are located closely to the oil price range while others are located far from the oil price range, as 56 demonstrated in Figure 3 below. In fact, the oil price range calculation is equal to the sum of the higher oil price (maximum oil value) and lower oil price (minimum oil value) divided by two. Fig. 3: Oil price stable and unstable ranges Source: Ruiz Estrada, 2017 7.4 . The multidimensional visualization of oil prices between December 2019 and April 2020 The I-Physical Space (I-Cartesian Space) is applied to five different decades, namely Period-I: December 2019; Period-II: January 2020; Period-III: February 2020; Period-IV: March 2020; and Period-V: April 2020. The main purpose of this exercise is to graphically evaluate oil prices more precisely, accurately and consistently. For each selected period of analysis, we derive its I-Cartesian Space. According to our findings, during Period-I: December 2019 the average price of oil was US$56 per barrel. Oil prices during this month were stable and remained close to the last months October 2019 (US$57) and September (US$52), as highlighted in Figure 4. In Period-II: January 2020, the analysis shows that the average oil price was US$61 per barrel, COVID-19 had so much impact in the Chinese economy. In Period-III: February 2020 the average oil price was US$51 per barrel, the cases of COVID-19 increase considerably in China with a total of 70,000 infected cases. Indeed, this month experienced lower and unstable oil price. The sharp rise of COVID-19 cases affects considerably the oil prices during this month was due to a large number of industrial cities in China and lockdown (quarantine). 57 Period-IV: March 2020 oil prices are in the lower range. The average oil price in the March 2020 was to US$44 per barrel. The oil price shows unstable oil price fluctuations between US$40 and US$45. In the Period-V: April 2020, the average crude oil price was at US$20 per barrel (see Figure 4). This lowers average oil price was caused by the large number of COVID19 worldwide. This is the lowest average price of crude oil during the last filthy years. The main reasons for this decrement in oil prices was the rapid growth of COVID-19 infected and death cases in Europe (Italy, Spain, France, Germany, and UK) and U.S. Fig. 4. The Visualization of Oil Price from a Multidimensional Perspective from November 2019 and April 2020 Source: OPEC (2020) 7.5. Conclusion In this chapter, we proposed an alternative multidimensional graphical approach to visualizing the behavior of oil prices in the COVID-19 crisis. We develop a new graphical method, the “Infinity Physical Space (I-Physical Space)”. The method precisely illustrates that oil prices have been lower and more unstable in the COVID-19 crisis period. In the more recent 20 April 2020 (-US$35) and 21 April 2020 (-US$6), oil prices were subject to a wide range of shocks. The consequent high volatility of oil prices is due to the psychological effect of COVID-19 crisis in governments (public spending) and consumer’s mobility (lockdown and quarantines). To conclude, the proposed multidimensional graphical approach and the use of the proposed approach for the analysis of the behavior of world oil prices in the last past five months of 2020 can be potentially useful to study the impact of any pandemic (COVID-19) on the world oil prices trend. At the same time, the COVID-19 crisis probes to us that the global oil prices are extremely vulnerable in case of any massive global pandemic. More recently, the world economy starts to perceive a deep world recession in the short run (from May 2020) and a 58 possible deep economic depression (from October 2020) according to our calculations, and matter a lot. References OPEC Annual Statistical Bulletin (2020) Available at https://www.opec.org/opec_web/en/76.htm. Ruiz Estrada, M.A. (2011). Policy Modeling: Definition, Classification, and Evaluation. Journal of Policy Modeling, 33(4), 523-536. Ruiz Estrada, M.A. and Yap, S.F. (2013). The Origins and Evolution of Policy Modeling Journal of Policy Modeling, 35(1), 170-182. Ruiz Estrada, M.A. (2017). “An Alternative Graphical Modeling Econographicology.” Quality and Quantity, 51(5):2115-2139. for Economics: Ruiz Estrada, M.A. and Park, D. (2018). The Past, Present, and Future of Policy Modeling. Journal of Policy Modeling, 40(1), 1-15. WHO (2020). Database. https://www.who.int/. Accessed on April 15, 2020. 59 Chapter VIII The Global Economic Smash Effect of COVID-19 Mario Arturo Ruiz Estrada 8.1. How COVID-19 can Affects the World Economy? Many economists and academics can never imagine how a global pandemic suddenly stop the worldwide economy of a few weeks—the damage of the largest world economies such as the U.S., China, E.U., and Japan. The negative results of COVID-19 show terrifying results with 9 million infected cases and 500,000 dead people until June 2020, according to WHO (2020). Economic damage from COVID-19, the consumption stops, investment drops, government spending increase, and international trade stop dramatically. The adverse effects of COVID-19 in these four macro-variables make a definite impact on the GDP automatically. This chapter is interested in evaluating the impact of COVID-19 under the application of a new type of economic modeling. This new type of modeling is called the Global Economic Smash Crisis Effect Simulator (GESCE-Simulator). This new economic simulator could be used to evaluate the impact of COVID-19 on different macroeconomic scenarios simultaneously. Finally, the next section presents this new type of modeling in detail. 8.2. An Introduction to the Global Economic Crisis Smash Effect Simulator (GECSESimulator) The construction of the global economic crisis smash effect simulator (GECSE-Simulator) is based on the application of economic waves modeling. We suggest the simultaneous application of the inter-linkage coordinate space and economic modeling in real-time to build each economic wave in our simulation. Initially, the GECSE-simulator uses n-number of economies “E” in its analysis. Each economy has its general axis; at the same time, each general axis can show a large number of sub-axes. Straight lines interconnect all these sub-axes until they reach the last sub-axis. A reminder: each sub-axis runs with different multi-dimensiona l partial differentiation(s) (∂Y/∂X) in real-time (☼). The idea of applying a large number of partial differentiation(s) successively is to generate an effect of the movement of different economic waves in the same graphical space. According to the GECSE-simulator, each sub-axis is interconnected into the same general axis by the application of the inter-linking sub-axis system “╬”. The function of this is to join each sub-axis into the same general axis. Finally, all general axes and sub-axes are joined at all levels of analysis under the application of the fixed exponential “λ” in different periods (t+1). However, the assumption is that all sub-axes and the general axis are moving under the application of economic modeling in real-time “☼” (see Expression 1). We also suggest the implementation of the Omnia Mobilis assumption (Ruiz 60 Estrada, 2011) and (Ruiz Estrada and Park, 2018) to help in the relaxation of each sub-axis. To reduce the use of the Ceteris Paribus assumption in our simulator. Finally, we observe a large number of surfaces (economic waves) in permanent movement using the GECSE-simulator. The change of these surfaces starts from the epicenter of the inter-linkage coordinate space until its end in the last sub-axis into the same general axis. The real impact of this simulator is located on the previous sub-axis (see Figure 1). The final analysis in the GECSE-simulator is based on the review of different surfaces displayed in different parts of the inter-linkage coordinate space. ☼λt+1 C1 = ☼[∂Yi1-0/∂X i 1-0]*Di ╬ ☼[∂Yi1-1/∂Xi1-1]*Di ╬ ☼[∂Yi1-2/∂Xi1-2]*Di ╬…╬ ☼[∂Yi1-∞/∂Xi1-∞]*Di C2 = ☼[∂Yi2-0/∂X i 2-0]*Di ╬ ☼[∂Yi2-1/∂Xi2-1]*Di ╬ ☼[∂Yi2-2/∂Xi2-2]*Di ╬…╬ ☼[∂Yi2-∞/∂X i2-∞]*Di ☼ s= . C∞ = ☼[∂Yi∞-0/∂X i ∞-0i]*Di ╬ ☼[∂Yi∞-1/∂Xi∞-1]*Di ╬ ☼[∂Yi∞-2/∂Xi∞-2]*Di ╬…╬ ☼[∂Yi∞-∞/∂Xi∞-∞]*Di Partial differentiation: i= {o,1,2,3…∞} and Level: j= {o,1,2,3…∞} (1) Fig. 1. The GECSE-Simulator Coordinate System 8.3. The Application of the GECSE-simulator to Evaluate the Impact of COVID-19 The GECSE-simulator will be applied to five different economies simultaneously. These five economies are preceded by the first one, the United States economy, which is fixed as the epicenter in the GECSE-simulator. Additionally, we include the other four economies distributed into four general axes, respectively; these four economies are the U.S. economy (C1), China (C2), European Union (C3), and Japan (C4) (see Expression 2). The GECSESimulator uses four variables: the consumption growth rate(s) (C), the growth of the investment rate(s) (I), the government spending growth rate(s) (G), net trade growth rate(s), and poverty growth rate (P). Each sub-axis is multiplied by a coefficient that is called the level of COVID19 devastation on the global economy (D i) according to the infected cases growth rate (I). 61 The level of COVID-19 devastation on the global economy (Di) according to the infected cases growth rate (i). The Di is a coefficient that is a discount rate that aids in observing the final impact of COVID-19 globally in different economies. We apply partial differentiation(s) in real-time between the income growth rate(s) (Y) and consumption growth rate(s) (C) on the first sub-axis (∂Y/∂C), interest rate growth rate(s) (ir) and investment growth rate(s) (I) on the second sub-axis (∂ir/∂I), the income tax growth rate(s) (Tx) and the government spending growth rate(s) (G) on the third sub-axis (∂Tx/∂G), the tariff growth rate(s) (Tr) and net trade growth rate(s) (TN) on the fourth sub-axis (∂Tr/∂TN), GDP growth rate(s) (GDP) and poverty growth rate(s) (P) on the fifth sub-axis (∂GDP/∂P) in the same general axis (C). Each partial differentiation(s) is multiplied by the level of COVID-19 devastation on the global economy (Di). This is to generate different scenarios under different levels of impact of COVID-19 on each economy in the analysis simultaneously. At the same time, we suggest applying an exponential of real-time (☼λt+1) to join all partial differentiation(s) in each sub-axis and general axis until we can build a single surface. If we observe this on a large screen, we can see a large number of surfaces moving like waves in the same space and, at the same time, from the epicenter to the last sub-axis in the same general axis. The final objective for using the GECSE-simulator is to show different scenarios and the impact of COVID-19 globally according to the level of COVID-19 devastation on the global economy (Di). Now it is possible to visualize the adverse effects of COVID-19 from a worldwide perspective. Hence, this simulator permits the representation of different scenarios and impacts of COVID-19 on the world economy within the same graphical space and time (see Figure 2). C 1 =☼[∂Y i /∂C 1 i ]*Di ╬☼[∂iri /∂Ir1i ]*Di ╬☼[∂Txi /∂G1i ]*Di ╬☼[∂Tri /∂TN1i ]*Di ╬☼[∂GDPi /∂P1i ]*Di ☼λt+1 C 2 =☼[∂Y i /∂C 2 i ]*Di ╬☼[∂iri /∂Ir2i ]*Di ╬☼[∂Txi /∂G2i ]*Di ╬☼[∂Tri /∂TN2i ]*Di ╬☼[∂GDPi /∂P2i ]*Di ☼s = C 3 =☼[∂Y i /∂C 3 i ]*Di ╬☼[∂iri /∂Ir3i ]*Di ╬☼[∂Txi /∂G3i ]*Di ╬☼[∂Tri /∂TN3i ]*Di ╬☼[∂GDPi /∂P3i ]*Di C 4 =☼[∂Y i /∂C 4 i ]*Di ╬☼[∂iri /∂I4i ]*Di ╬☼[∂Txi /∂G4i ]*Di ╬☼[∂Tri /∂TN4i ]*Di ╬☼[∂GDPi /∂P4i ]*Di (2) 62 Fig. 2. The GECSE-Simulator: Graphical Modeling 8.4. The Application of GECSE-Simulator on U.S., China, EU, and China The level of COVID-19 devastation on the global economy (Di) is classified into ten levels, from Level 1 (low impact) to Level 10 (high impact). We observe in Figure 3 that a Level 10 impact is the highest level of devastation of COVID-19 can bring a global economic depression on the world economy. We can also observe that the standards of income growth rates among the four economies in analysis for the U.S. (C1), China (C2), EU (C3), and Japan (C4). There are between -15% to -25%, and the consumption growth rates are between -2% and -5%; both indicators show lower levels that can bring easily a deep global economic depression anytime and anywhere. The economies which are more affected by the global economic depression at Level 10 are the U.S. and EU economy. The global economic depression is due to a dramatic reduction of goods demand that exists between them and China; the net trade growth rates can present results between -15% and -35%, according to the GECSE-Simulator. However, the government spending growth rates can move so high between 15% to 35%; at the same time, the poverty growth rates can expand between 17% to 25%. The Chinese economy shows that income and consumption growth rates drop considerably, but this is proportionately less than the U.S. and E.U. economy. In China and Japan, the income growth rates are between -10% and -15%, respectively. Still, the investment growth rates for both economies are equal to -35% and -15% (see Figure 3). If we continue analyzing it up to Level 7, we can observe a better performance of the GDP growth rate of the U.S. than at Level 8. At the same time, U.S. can experience poor performance in its consumption growth rate with -1% and an investment growth rate of -3%. The E.U. and Japan cannot show any improvement in income growth rates and consumption growth rates at Level 7 (see Figure 3). 63 At Level 5, it is possible to observe that the GDP growth rate of the U.S. is equal to 0. But the U.S. consumption growth rate is equal to 1%, and the U.S. investment growth rate is 3%. Level 5 also shows a minimum impact on the EU and Japan with an insignificant increment in the consumption and investment growth rates located between -1% and -3%. But in China's case, the impact is less because the consumption and investment growth rates only show 0.5% and 1.5%. We can say that the EU and Japan have a high dependence upon the excellent performance of the government spending growth rate than upon the consumption and investment growth rate (see Figure 3). According to the simulation, Level 6 shows a positive but weak GDP growth rate of the U.S.: the levels of consumption and investment growth rates show a better performance, but are only a little higher, with 1% for government growth rates and 1% for net trade growth rates. China shows a better performance than before, but the government spending growth rates only decrease from 6% to 3%, respectively (see Figure 3). Finally, the simulation at Level 3 and Level 0 shows the lowest level of COVID-19 devastation on the global economy (Di) rates of the global economic depression on the world economy. These levels are exceptional but hard to be aspired in the short run to by the U.S. because we are referring to a possible massive expansion of the GDP growth rates, i.e., between 5% and 7% annually. And the final impact of an enormous increase in the GDP growth rate of the U.S. on the EU and Japan can improve its consumption growth rates to between 7% and 12% and the investment growth rate to levels of 8%. We can observe that among the four economies mentioned, those who receive the most benefit from a higher performance of the GDP growth rate of the U.S. are, in order, the EU, Japan, and China, respectively. Under Level 3 and Level 0, China can decrease their consumption growth rates to between 2% and 7%, but the level of investment growth rates in China can only increase from 3% to 11% (see Figure 3) according to this simulation. In the case of the poverty growth rates can get a considerable contraction between 5% to 7%. Fig. 3. The Global Economic Crisis Smash Effect Simulator at Different Levels Initial Level Level 7 Level 10 Level 5 64 Level 6 Level 1 Source: World Bank (2020) 8.5. Concluding Remarks This chapter offers economists and academics of economics an alternative economic modeling approach to analyze the final impact of COVID-19 on the present global economic crisis from a holistic approach. The GECSE-simulator can generate many scenarios that originated from different levels of COVID-19 devastation on the worldwide economy (Di). The main objective of this is to create different simulations and measure the catastrophic(s) effect(s) of COVIDP-19 on the world economy within the same graphical space. We evaluate the U.S., China, EU, and Japan consumption, investment, government spending, and net trade damage from COVID-19 under different levels according to our preliminary results from each simulation. References Ruiz Estrada, M.A. (2011). “Policy Modeling: Definition, Classification and Evaluation”, Journal of Policy Modeling, 33(4): 523-536. Ruiz Estrada, M.A. (2017). “An Alternative Graphical Modeling for Economics: Econographicology”, Quality and Quantity, 51(5): 2115-2139. Ruiz Estrada, M.A. Park, D., (2018). “The Past, Present, and Future of Policy Modeling”, Journal of Policy Modeling, 40(1): 1-15. WHO. (2020). COVID-2019 Situation Reports. Retrieved from https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/ World Bank (2020). Annual report. http://www.wb.org. Accessed on April 1, 2020. 65 Chapter IX The Application of the Economic Sustainable Accelerators (TESA) in the COVID-19 Crisis Mario Arturo Ruiz Estrada 9.1. Introduction This research focuses its attention on the creation of a new production, distribution, and consumption platform to against COVID-19 crisis. The objective is to integrate many issues can benefit any society together. Therefore, this research proposes an alternative platform that can mix production, distribution, and consumption elements into a single platform. This platform is going to use itself modules of evaluation, before we can implement TESA anywhere and anytime. Additionally, this research strongly supports the idea that any country cannot stop its national production of goods and services in the COVID-19 critical times for national security reasons. Also, the same study supports the argument about production, distribution, and consumption is crucial to keeps the economic welfare of any country. Independently, if we implemented TESA to against COVID-19 crisis. Then it is possible to find a new production, distribution, and consumption to reduce partially or totally the COVID-19 crisis damage. However, the TESA focus more on the production, distribution, and consumption aspects than financial issues concern to the society. The main weaknesses of the TESA are related more to its implementation and improvement in different regions and countries. Besides, this research is considering that the TESA is used as a main pillar for a fast-economic recovery for COVID-19 crisis. Since the economic recovery plans appear in the year 1929 in the document entitled “Employment, Interest, and Money. “by John Maynard Keynes (1936). Where he tries to capture the impact of fiscal policy, private consumption increment, and production from different economic players such as consumers, firms, and government materialized into a single indicator (multiplier) to evaluate the evolution of an economy as a whole. This research finds that Keynes contribution was crucial to assessing the economic growth, but not enough to capture the welfare and progress of the society. Another remarkable piece of research is by Wassily Leontief (1974). They measure for the first time the input-output table to measure the dependency of three sectors (agriculture, industry, and services) interacting and measure its contribution, at the same time, the planning and management of these three sectors simultaneously in critical times. In another hand, we have the statement of Robert E. Lucas Jr. (1977) that support the idea about business cycles, those are inherent part any economy. 66 Moreover, TESA proposes a new production, distribution, and consumption to be implemented in the COVID-19 crisis anytime and anywhere. The Economic Sustainable Accelerators (TESA) defines as an alternative production, distribution, and consumption model to against any pandemic crisis such as the COVID-19 that can affect the social welfare and progress of a whole country. Any pandemic always is continually expanding and contracting any economy dramatically without stop or a logic order (Chaos theory). However, TESA is not going to be applied only in large cities. Also, the TESA is an alternative economic platform can be implemented by different countries. 9.2. An Introduction to the Economic Sustainable Accelerators (TESA) 9.2.1. Introduction This chapter aim introduces an alternative production, distribution, and consumption platform to make a fast recovery from COVID-19 in anytime and anywhere. This new platform is termed the “The Economic Sustainable Accelerators (TESA)." TESA request the implement twelve different modules of evaluation such as: (i) Module-1: COVID-19 infection cases geographical location; (ii) Module-2: Movement Control Order perimeters size; (iii) Module-3: Labour concentration and mobility systems, (iv) Module-4: Production priority plan; (v) Module -5: Transportation systems integral mobility; (vi) Module-6: Suppliers distribution; (vii) Module 7: Sanitation and Prevention strategic points; (viii) Module-8: Agriculture and Food Security; (ix) Module-9: COVID-19 Private and Public Partnership (public transportation controls, free health support, welfare programs, taxation exoneration), government spending controls; (x) Module-10: Industrial restructuration; (xi) Module-11: Services dynamicity; (xii) Module -12: COVID-19 Consumers opening levels mobility. This research encourages to create alternative production, distribution, and consumption models to reduce the damage of any pandemic such as COVID-19 in the short and long run. Hence, TESA could be an essential guide for targeted economic damages from pandemics to address economic failures as a whole. TESA is a single multi-level model to be implemented in any country into a specific period (yearly) and geographical location. Besides, TESA offers different modules of evaluation and its implementation according to the country economic conditions. 9.3. The Economic Sustainable Accelerators (TESA): Definition and Evaluation Modules “The Economic Sustainable Accelerators (TESA)” can be defined as “a specific geographical area that concentrate the full production free of any pandemic or epidemic disease to avoid stop the distribution and consumption of goods and services. TESA involves the interaction of all agricultural, industrial, and services sectors activities together. The main objective is to fully concentrate labor, capital, land, and technology far away from areas heavily infected from any pandemic and epidemic disease until it stops partially or fully. At the same time, a basic condition is the easy mobility of heavy and light transportation systems to carry easily goods and services anywhere and anytime without any restriction, at the same time, the implementation of strict sanitary controls. Finally, TESA also need it a strategic mains points of distribution and retails to facilitate the supply of goods and services to all consumers. The main objective of TESA is to never stop production (profits and jobs), distribution 67 (transportation and channels of distribution), and consumption (low prices) simultaneously. Hence, TESA main argument is that we need to keep always economic growth to reduce the damage of any pandemic or epidemic from beginning to the end.” The modules of requirement for the implementation of TESA, we need to use twelve modules of evaluation before its implementation in any country. These modules of evaluation is follow by: (i) Module-1: COVID-19 infection cases geographical location; (ii) Module-2: Movement Control Order perimeters size; (iii) Module-3: Labor concentration and mobility systems, (iv) Module-4: Production priority plan; (v) Module-5: Transportation systems integral mobilit y; (vi) Module-6: Suppliers distribution; (vii) Module-7: Sanitation and Prevention strategic points; (viii) Module-8: Agriculture and Food Security; (ix) Module-9: COVID-19 Private and Public Partnership (public transportation controls, free health support, welfare programs, taxation exoneration), government spending controls; (x) Module-10: Industrial restructuration; (xi) Module-11: Services dynamicity; (xii) Module-12: COVID-19 Consumers opening levels mobility. 9.4. How Consumers, Private Sector, and Government Behave in Case of Pandemics Crisis? This section of our research, we present three possible scenarios about how consumers, private sector, and government behave in case of pandemics crisis. a. The first scenario: Consumers (see Figure 1) Window-1: C = COVID-19 infected cases daily and M = Movement Control Order time framework (quarantine) If C moves up then M moves up (days of quarantine is imposed from government). Window-2: M = Movement Control Order time framework (quarantine) and L = Leisure If M moves up then L moves up (people relax). Window-3: L = Leisure and P = Productivity If L moves up then P moves up (productivity drops considerably). Window-4: P = Productivity and I = Income If P moves down then I move down (income drops dramatically). Window-5: I = Income and C = Consumption If I move down then C moves down (income drops dramatically). Window-6: C =Consumption and S = Savings If C moves down then S moves down (savings drops dramatically). Window-7: S = Savings and V = Consumers Vulnerability If S moves down then V moves up (consumer vulnerability increase dramatically). Window-8: V = Consumers Vulnerability and F = future consumption If V moves high then F moves down (future consumption can drop rapidly). Window-9: F = future consumption and ID = Income Desgrowth If V moves down then ID moves up (income desgrowth moves faster). Window-10: ID = Income Desgrowth and L = Leaving Standards If ID moves up then L moves down (leaving standards drop considerably). Window-11: L = Leaving Standards and W = Economic welfare If L moves down then W moves down (economic welfare drops dramatically). Window-12: W = Economic welfare and P = Poverty If W moves down then P moves up (poverty can increase considerably). 68 b. The second scenario: Private sector (see Figure 1) Window-1: C = COVID-19 infected cases daily and M = Movement Control Order time framework (quarantine) If C moves up then M moves up (days of quarantine is imposed from government). Window-2: M = Movement Control Order time framework (quarantine) and L = Labor Mobility If M moves up then L moves down (people cannot work). Window-3: L = Labor mobility and P = Production If L moves down then P moves down (production drops considerably). Window-4: P = Production and S = Supply If P moves down then S move down (supply drops dramatically). Window-5: S = Supply and SE = Sells If S move down then SE moves down (sells drops dramatically). Window-6: SE = Sells and I = Firm Income If SE moves down then I move down (firm income drops considerably). Window-7: I = Firm Income and E = Employment If I move down then E moves down (employment scarcity). Window-8: E = Employment and Y = Workers Income If E moves down then Y moves down (workers income can drop rapidly). Window-9: Y = Workers Income and C = Consumption If Y moves down then C moves down (consumption drops faster). Window-10: C = Consumption and S = Savings If C moves down then S moves down (savings drop considerably). Window-11: S = Savings and W = Economic welfare If S moves down then W moves down (economic welfare drops dramatically). Window-12: W = Economic welfare and P = Poverty If W moves down then P moves up (poverty can increase considerably). c. The third scenario: Government (see Figure 1) Window-1: C = COVID-19 infected cases daily and M = Movement Control Order time framework (quarantine) If C moves up then M moves up (days of quarantine is imposed from government). Window-2: M = Movement Control Order time framework (quarantine) and SP = Government Spending If M moves up then SP move up (government spending increase considerably). Window-3: SP = Government Spending and NB = National Budget If SP moves up then NB moves up (National budget not enough). Window-4: NB = National Budget and D = Budget Deficit If NP moves up then D move up (the budget deficit increase dramatically). Window-5: D = Budget Deficit and CL = Domestic and International Credits and Loans If D move up then CL moves up (high demand of domestic and international credits and loans). Window-6: CL = Domestic and International Credits and Loans and TX = Tax If CL moves up then TX move up (tax increment). Window-7: TX = Tax and C = Corruption If I move up then E moves up (Corruption increase considerably). Window-8: C = Corruption and SI = Social Investment If C moves up then SI moves down (social investment decrease rapidly). 69 Window-9: SI = Social and SP = Social Protection If SI moves down then SP moves down (social security drops faster). Window-10: SP = Social Protection and SB = Subsidies If SP moves down then SB moves down (subsidies drop considerably). Window-11: SB = Subsidies and W = Economic welfare If SB moves down then W moves down (economic welfare drops dramatically). Window-12: W = Economic welfare and P = Poverty If W moves down then P moves up (poverty can increase considerably). Fig. 1 How Consumers, Private Sector, and Government Behave in Case of Pandemics Crisis? Source: Author 9.5. The Economic Sustainable Accelerators (TESA): Evaluation The Economic Sustainable Accelerators (TESA) is a new production, distribution, and consumption platform to generate the favorable conditions to reduce the damage of any pandemics such as COVID-19 anywhere and anytime. The basic idea behind to build the TESA is to generate purposeful economic platform by evaluating a large and several numbers of modules of evaluation until we can fix market failures faster and efficiently. The TESA requires the use of following assumptions such as Omnia Mobilis (Ruiz Estrada, 2011) (Ruiz & Park, 2018). In our case, we made a serial of simulations to find practical solutions to solve partially the negative effects of any pandemic. TESA evaluation uses experimental data to input into 250 different equations for its evaluation. Hence, we can proceed to the evaluation and implementation of the TESA anywhere and anytime. Also, TESA evaluation and implementation was adapted and running in Mathematica Wolfram software version 11.0. All equations in this model moves to an algorithm by using Mathematica Wolfram version 11 language programming that allows us to generate a large pool of possible results to the problem 70 at hand. Explicitly, we solve differential equations and perform geometric computations to create a range of different scenarios in different development stages. The implementation of TESA rests on five basic steps:        a. b. c. d. e. Data format design and data collection from different sources. Programming IO-Table in Mathematica Wolfram version 11 algorithm application. Import spread sheet data from EXCEL to Mathematica Wolfram version 11. Final output or results from solving differential equations solving and performing geometric computations Final results, graph production, and analysis of results The main benefit of using Mathematica Wolfram version 11 is greater versatility and efficiency of model results due to modeling and simulating future economic platforms for pandemics in an extended time framework. The data used in our model come from various reliable sources such as central banks, World Bank, International Monetary Fund. All the data were used to build a single extensive database distributed into 250 equations are laid out in our index. Besides, we run nine simulations based on the different cities to identify TESA with the highest city integral sustainable development platform footprint. The main contribution of TESA is the use of new production, distribution, and consumption model approach together, and it can generate a range of different results as well as the likely implemented TESA anywhere and anytime. The advantage of TESA is that it does not impose any restriction on the time framework or geographical space. TESA can be applied anytime and anywhere without any limitation on any country, region, continent, prefecture, state, or city around the world. In the present research, we run our simulation on an extensive database under different possible scenarios to evaluate TESA implementation. In fact, another advantage of TESA is that it is not based solely on an isolated production model but also incorporates several numbers of recovery programs. Such a comprehensive production, distribution, and consumption analysis gives us a more accurate assessment to minimize the impact of a massive pandemic anywhere and anytime without any geographical considerations or development stage of the country in study and external factors can affect the implementation and evaluation of TESA easily. We believe that TESA can help policymakers, rating agencies, and academics to identify a new way to revive any economy in middle of any pandemic such as COVID-19 from a global perspective of analysis, and thus to take necessary action to improve the economic platform in the long run without any problem. Subsequently, TESA evaluation and implementation is under the application of the Macroeconomics Structures Vulnerability 71 Analysis (MSV-Analysis) (Ruiz Estrada, 2017). This alternative mathematical and graphical approach offers a flexible and powerful tool. 9.6. Introduction to TESA: Evaluation Model TESA evaluation and implementation has two general objectives follow by the first aim is to evaluate weaknesses and strengths points of different main structures (or scenarios) in the same graphical space at the same time. The second objective is the forecasting of different Main Structures (or situations) simultaneously. TESA evaluation and implementation is going to apply the Cubes Cartesian Space (Ruiz Estrada, 20017). The Cubes-Cartesian physical space is open the possibility to generate a multi-dimensional visual effect to show the vulnerability of 12 Main-Structures (or scenarios) in the same graph and time. Each Main-Structure (or situations) exposes a large number of Nano-Structures, Micro-Structures, and Sub-Structures in different axes, levels, and cubes by sizes and colors (See Figure 2). However, the detail of analysis of each structure by axes, levels, perimeters, and cubes by sizes and colors, it depends on the parameters are established in our research. Finally, all these Nano-Structures, MicroStructures and Sub-Structures apply the city integral sustainable development platform under the application of the Omnia Mobilis assumption (Ruiz Estrada, 2011). The economic sustainable accelerators evaluation and implementation (Cubes-Cartesian Space) is formed by infinity number of general axes (A 0, A1 ,…, A∞). Where each axis shows different levels (L0, L1 ,…, L∞), perimeters (P0, P 1, P 2…P ∞), and Cubes with different sizes and colours (C 0/β, C1/β… C∞/β). Therefore, the coordinate system of the Cubes-Cartesian space is represented by SA:L:P:C = (Ai, Lj, P k, Cs/β) respectively. Where i, j, k and s represent different values between 0 and ∞…. And β represent the different colours of each cube in different levels (L 0, L1 ,…, L∞). All these cubes (Cs/β) with different sizes and colours in the same axis under the same level (L0, L1 ,…, L∞) and different perimeters (P 0, P 1, P 2…P ∞) will be joined together, it is based on the application of the concept is called “links structures” represented by the symbol “@”. Moreover, the economic sustainable accelerators platform is following by expression 1 and figure 3: Level P0 @………………. @ A1 : S1:0:0:C(0/β) = (A 1,L0, P 0, C0/β) @ ……. .. Level Pn @ S1:0:λ:C(α/β) = (A 1,L0, P λ, Cα/β) @ @ S1:1:0:C( 0/β) = (A 1,L1, P 0, C0/β) @ ………... @ S1:1:λ:C(α/β) = (A 1,L1, P λ, Cα/β ) @ @ . . @ @ S1:θ:λ:C( α/β) = (A 1,Lθ, P λ, Cα/β) @ ………... @ A2 : S2:0:0:C(α/β) = (A 2,L0, P 0, C0/β ) @ (A 1,L1, P λ, Cα/β ) @ @ ……. .. @ S2:1:1:C( α/β) = (A 2,L1, P 0, C0/β) @ S1:1:λ:C(α/β) = @ S2:0:λ:C(α/β) = (A 2,L0, P λ, Cα/β) @ @ ………... @ S2:θ:λ:C( α/β) = (A 2,L1, P λ, Cα/β) @ 72 . . @ @ S2:θ:λ:C( α/β) = (A 2,Lθ, P λ, Cα/β ) ………... @ @ S2:θ:λ:C(α/β) = @ (A 2,Lθ, P λ, Cα/β) @ A10 : S10:0:0:C(α/β) = (A 10,L0, P 0, C0/β ) @ ……. .. @ @ S10:0:λ:C(α/β) = (A 10,L0, Pλ, Cα/β) @ S10:1:1:C(α/β) = (A 10,L1, P0, C0/β) @ ………... @ S10:1:λ:C(α/β) = @ @ . . @ @ S10: θ: λ: C: α/β = (A 10,Lθ, P λ, Cα/β ) (A 10,L1, P λ, Cα/β) @ ……………... @ S10+1:θ+1:λ+1:C:α+1/β = (A 10+1,Lθ+1, P λ+1, Cα+1/β ) (1) n = {1,2,3…∞} θ = {1,2,3…∞} λ = {1,2,3…∞} α = {1,2,3…∞} Note: S = Main Structure, A = Axis, L = Level, P = Perimeter, C = Cube and β = Colours Finally, economic sustainable accelerators platform shows a general function “Ea” that is the result from the interconnection of twelve the main structures (S0, S1 ,…, Sn) under different axes (A1, A2 ,…, An), levels (L1, L2 ,…, Ln), perimeters (P0, P 1, P 2…P n) and cubes with different sizes and colours (C0/β, C1/β… Cn/β) respectively (See Expression 2): Ea = ƒ (Ao <ΣS0╬ S1╬…S∞> ╬ A1 <ΣS0╬ S1╬…S∞> ╬ …╬ A∞<ΣS0╬ S1╬…S∞> …) (2) Note: Ea = The General Variable, ╬ = Interconnection, Ai = Axis and Si = General Structures. However, the size of all cubes structures by level(s) is under the parameters that we establish in our researcher. These parameters keep its intervals of amounts of money or number of units (quantitative) and binary system [0,1] (qualitative). The changes of all cubes size depend on the constant changes according to the quantitative variables (volume of money or the number of units), and qualitative (binary results 1 or 0) changes between “n” periods of time. On the other hand, if we assume that all the cubes in different levels are always changing in real time, then all the cubes can experience an expansion, contraction or stagnation. The changes of the size of the cube depend on the constant changes in its growth rates (first derivative) respectively. We propose three sizes of cubes by size and values (see Figure 2). 73 Fig. 2: Cubes Structures Cube-1 Level cero Cube-2 Level one Cube-3 Level-n 0 ≥ TVi ≤ V1 V1 ≥ TVi ≤ V2 Nano-Structure Micro-Structure V3 ≥ TVn ≤ Vn Sub-Structure Note: TV = Total Value, V = Value and “n” is equal to any value between 1 and ∞… Fig. 3: The Cubes-Cartesian Space Coordinate System The process of forecasting into the city integral sustainable development platform, assuming “n” number of vectors and the “Ea” is to be forecast, we suggest in our model that the predicting of the value of the Ea is equal to the interconnection of “n” number of Sub-Y “Ys.” Therefore, we assume two types of time in the process of forecasting Macroec onomics 74 Structures Vulnerability Analysis (MSV-Analysis). Firstly, the general time speed (☼gt) that is running in Ea and the partial time's speed (☼pt) are running in each Sub-Y. Hence, the general time speed (☼gt) is equal to the synchronization of all partial times speed (☼pt) into the Macroeconomics Structures Vulnerability Analysis (MSV-Analysis). Secondly, the partial time's speed (☼pt) that is running in a separate magnitude of time in each level of analysis, in our case we work on three different levels of analysis from level 0 to level 2. The partial time's speed (☼pt) also depends on various axes and perimeters levels into the Macroeconomics Structures Vulnerability Analysis (MSV-Analysis). The first stage of forecasting condition in the Macroeconomics Structures Vulnerability Analysis (MSV-Analysis) is followed by expression 3. Level P0 ╬ ╬ ………………. Level Pn A0: S 0:0:0:C(α/β) ☼pt = (A0,L0, P0, Cα/β) ☼pt ╬ ……. .. ╬ S 0:0:λ:C(α/β) ☼pt = (A0,L0, Pλ, Cα/β) ☼pt ╬ ╬ S 0:1:0:C(α/β) ☼pt = (A0,L1, P0, Cα/β) ☼pt @ ………... @ S 0:1:λ:C(α/β) ☼pt = (A0,L1, Pλ, Cα/β) ☼pt ╬ ╬ . . ╬ ╬ S 0:θ:λ:C(α/β) ☼pt = (A0,Lθ, Pλ, Cα/β) ☼pt ╬ ………... ╬ S 0:1:λ:C(α/β) ☼pt = (A0,L1, Pλ, Cα/β) ☼pt ╬ ╬ A1: S 1:0:0:C(α/β) ☼pt = (A1,L0, P0, Cα/β) ☼pt ╬ ……. .. ╬ S 1:0:λ:C(α/β) ☼pt = (A1,L0, Pλ, Cα/β) ☼pt ╬ S 1:1:1:C(α/β) ☼pt ╬ = (A1,L1, P0, Cα/β) ☼pt ╬ ………... ╬ S 1:θ:λ:C(α/β) ☼pt = (A1,L1, Pλ, Cα/β) ☼pt ╬ ╬ . . ╬ ╬ S 1:θ:λ:C(α/β) ☼pt = (A1,Lθ, Pλ, Cα/β) ☼pt ╬ ………... ╬ S 1:θ:λ:C(α/β) ☼pt = (A1,Lθ, Pλ, Cα/β) ╬ ╬ An: S n:0:0:C(α/β) ☼pt = (An,L0, P0, Cα/β) ☼pt ╬ ……. .. ╬ S n:0:λ:C(α/β) ☼pt = (An,L0, Pλ, Cα/β) ╬ ╬ S n:1:1:C(α/β) ☼pt = (An,L1, P0, Cα/β) ☼pt ╬ ………... ╬ S n:1:λ:C(α/β) ☼pt = (An,L1, Pλ, Cα/β) ╬ ╬ . . ╬ ╬ S θ: λ: C: (α/β) ☼pt = (An,Lθ, Pλ, Cα/β) ☼pt ╬ ……….. ╬ S n+1:θ+1:λ+1:Cα+1/β) ☼pt = (An+1,Lθ+1, Pλ+1, Cα+1/β) ☼pt (3) 75 n = {1,2,3…∞} θ = {1,2,3…∞} λ = {1,2,3…∞} α = {1,2,3…∞} Note: S = Macroeconomic structure, A = Axis, L = Level, P = Perimeter, C = Cube and β = Colors Finally, we arrive to the general function is followed by expression 4: Ea☼gt = < ╬ S( n+1:θ+1:λ+1:Cα+1/β) ☼pt:Ci > ∩ < ╬ (A n+1,Lθ+1, Pλ+1, Cα+1/β ) ☼pt:Ci> (4) Note: GF = General forecast point; ╬ = Interconnection; Ci = confidence interval; ☼pt = Partial T imes Speed and ☼gt = General T ime Speed Therefore, the confidence interval to predict TESA evaluation and implementation (See Expression 5) is when the general time speed (☼gt) depends on the ☼gt/n degrees of freedom. At the same time, the ☼gt/n is the standard error of prediction using Ŝi as the main reference in its calculation. Nevertheless, the forecast interval became open according to each axis, perimeter, and level into the city integral sustainable development platform that is evaluating TESA implementation. Exist a reliable interconnection of a large number of sub-Y’s. Because always we need to assume that space is Multi-Dimensional. And each-dimension is moving at different speeds of time. Hence, the existence of a general time speed (☼gt) is governed by the synchronization of infinity partial times fixed by different partial times in real time (☼pt). Finally, the forecasting process in economic sustainable accelerators platform shows a different perspective to understand the future behavior of complex economic scenarios from a pandemic, the idea about short, medium and long run is different from the classic conception of time in this research chapter. TESA = ☼gt ŝi n + 1/n + [╬ (An+1,Lθ+1, Pλ+1, Cα+1/β) ☼pt 0 ] – [╬ (An+1,Lθ+1, Pλ+1, Cα+1/β) ☼ptn] [╬ [(An+1,Lθ+1, P λ+1, Cα+1/β) ☼pto ]2 (5.) Note: A = Axis; L = Level; P = Perimeter; C = Cube; β = Colors; ☼pt =Partial T imes Speed and ☼gt = General T ime Speed Finally, the evaluation of the TESA evaluation and implementation is categorized into four different levels of vulnerability (see Expression (6) Level 1: Optimum: 1 – 0.67 Level 2: Acceptable: 0.66 – 0.34 Level 3: Alert: 0.33 – 0 (6) 76 9.7. The TESA Implementation: Experimental Data The evaluation and implementation of the TESA is under the uses of 2500 micro- structures (Nano-variables), 500 sub-structures (sub-variables), and twelve main structures (primary variables). These 12 main modules of evaluation is follow by (i) Module-1: COVID19 infection cases geographical location; (ii) Module-2: Movement Control Order perimeters size; (iii) Module-3: Labour concentration and mobility systems, (iv) Module-4: Production priority plan; (v) Module-5: Transportation systems integral mobility; (vi) Module-6: Suppliers distribution; (vii) Module-7: Sanitation and Prevention strategic points; (viii) Module -8: Agriculture and Food Security; (ix) Module-9: COVID-19 Private and Public Partnership (public transportation controls, free health support, welfare programs, taxation exoneration), government spending controls; (x) Module-10: Industrial restructuration; (xi) Module -11: Services dynamicity; (xii) Module-12: COVID-Consumers opening levels mobility. Subsequently, it is possible to evaluate and implement TESA anywhere and anytime. The implementation of TESA is to evaluate possible scenarios under different magnitudes of risk monthly from the moment the pandemic starting until today. At the same time, TESA evaluation and implementation applies an extended number of sub-partial derivatives and total partial derivatives tested in real time under the uses of average values monthly in the same country. The final results from TESA evaluation and implementation shows three possible parameters need to be considered always: (i) from 0 to 0.33 (Alert); (ii) from 0.34 to 0.66 (Acceptable); (iii) from 0.67 to 1 (Optimum). At the same time, each module keeps the same parameters of evaluation. In our case, we present different acceptable parameters to be considered before to implement TESA anywhere and anytime. For example, (i) If Module1(COVID-19 infection cases geographical location) is located in [0 < 0.33), then we can implement the economic sustainable accelerators immediately; (ii) if Module-2 (Movement Control Order perimeters size) is located in (0.67 > 1], then we can periodize faster our economic sustainable accelerators to minimize the economic impact; (iii) if Module-3 (Labour concentration) is located in [0 < 0.33), then it is easy to implement the economic sustainable accelerators (iv) if Module-4 (Production priority plan) is located in (0.34 >1], then it is urgent to find and stablish rapidly all economic sustainable accelerators in strategic geographical areas; (v) if Module-5 (Transportation systems integral mobility) is located in (0.34 > 1], then can facilitate the economic sustainable accelerators implementation in strategic areas; (vi) if Module-6 (Suppliers distribution) is located in (0.34 > 1], then we need to facilitate the rapid access to economic sustainable accelerators; (vii) if Module-7 (Sanitation and Prevention strategic points) is located in (0.67 > 1], then we can implement the economic sustainable accelerators easily; (viii) If Module-8 ( Agriculture and Food Security) is located in (0.67 > 1], then the economic sustainable accelerators can work more efficiently; (ix) If Module -9 (COVID-19 Private and Public Partnership -public transportation controls, free health support, 77 welfare programs, taxation exoneration, and government spending controls -) is located in (0.67 > 1], then the economic sustainable accelerators can performance in better position; (x) If Module-10 (Industrial restructuration) is located in (0.67 > 1], then the economic sustainable accelerators can work perfectly; (xi) If Module-11 (Services dynamicity) is located in (0.67 > 1], then the economic sustainable accelerators has opportunity to extend its ratio of effectiveness; (xii) If Module-12 (COVID-Consumers opening levels mobility) is located in (0.34 > 1], then the economic sustainable accelerators can performance perfectly all the time. In the case of TESA platform for any country presents a list of parameters to be implemented in any country. Subsequently, TESA evaluation and implementation can show the perfect timing for its implementation, this depend on fill all requirements or modules of evaluation successfully according to figure 3. Fig. 3: The Application of TESA Evaluation and Implementation: Experimental Data 78 Source: Author 9.8. Concluding Observation and Policy Implications In this chapter, we propose a new model, the economic sustainable accelerators (TESA)– a new production, distribution, and consumption platform that can help us to against COVID-19 crisis. The TESA is based on the use of a set of twelve modules of evaluation such as: (i) Module-1: COVID-19 infection cases geographical location; (ii) Module-2: Movement Control Order perimeters size; (iii) Module-3: Labour concentration and mobility systems, (iv) Module-4: Production priority plan; (v) Module-5: Transportation systems integral mobilit y; (vi) Module-6: Suppliers distribution; (vii) Module-7: Sanitation and Prevention strategic points; (viii) Module-8: Agriculture and Food Security; (ix) Module-9: COVID-19 Private and Public Partnership (public transportation controls, free health support, welfare programs, taxation exoneration), government spending controls; (x) Module-10: Industrial restructuration; (xi) Module-11: Services dynamicity; (xii) Module-12: COVID-19 Consumers opening levels mobility. The underlying intuition is that TESA depends on a country’s strangeness to keeps production, distribution and consumption constantly in movement. We hope that this alternative production, distribution, and consumption platform will contribute to a better and more in-depth understanding that any pandemic crisis needs a stronger economic platform to reduce its damage in the short run. A more useful measurement of the economic sustainable accelerators (TESA) is conducive in the generation of appropriate policies, both for dealing with production, distribution, and consumption in large or small cities. It is possible with more suitable and realistic planning with better measures which seek to lessen the impact of pandemics such as COVID-19 on the production, distribution, and consumption platform of any country before they occur. 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