COVID-19 IN IRISH WORKPLACES AND COMMUNITIES - MODELLING OUTBREAKS FROM INFECTION DATA , 2022
During 2020-2021, the government of Ireland in line with international recommendations imposed th... more During 2020-2021, the government of Ireland in line with international recommendations imposed the closure of non-essential trades, services, and commerce. Food plant factories, meat processing plants among others were deemed essential and remained open. During that time, many workers were exposed to outbreaks in their workplaces. Some of the questions arising included if workers will adapt to new safety measures, if those measures could prevent and mitigate workplace outbreaks and , if an outbreak occur in a closed facility, if it will impact community transmission. The most vulnerable workplaces were typically front-line industries, with healthcare and food processing facilities among the hardest hit by Covid-19 infections. To complete the core aims, statistical models were developed for WP1. These models could accurately predict the scale of an outbreak in a meat processing plant based on the infection transmission in the community in the weeks preceding the outbreak and account for patterns in infection spread in both Ireland and worldwide using a ‘behavioural response’ mechanism. In addition to this, vaccine effectiveness was calculated using a method that made use of surveillance data. This demonstrated the strength and limitations of surveillance data. One clear aspect of behaviour in the COVID-19 pandemic has been people’s focus on, and response to, reported or observed infection numbers in their community. WP1 developed a simple model of infectious disease spread in a pandemic situation where people’s behaviour is influenced by the current risk of infection and where this behavioural response acts homeostatically to return infection risk to a certain preferred level. Analysis of worldwide COVID-19 data confirmed the model predictions at both an overall and an individual country level. Building on the findings of the infectious disease spread model, the research team aimed to investigate how individuals adapted their behaviours throughout the pandemic at an individual level, using the number of community cases and the number of contacts reported by cases to the contact-tracing program as a proxy for behavioural response. This work is ongoing at this time. In addition to this, estimations on vaccine effectiveness were calculated using a method that made use of surveillance data. This demonstrated the strength and limitations of surveillance data. There were significant challenges in completing WP1, primarily caused by a difficulty in accessing the required data, however, the primary aims and goals of the work package were achieved and a meaningful body of research was produced on disease spread in specific, controlled environments and among the general population.Our work will certainly inform future pandemics. The main messages are 1) that community transmission can predict the occurrence of outbreaks -suggesting that managers and Public Health officials should work together to reinforce surveillance during peaks of community transmission and 2) high risk settings -like meat factories- can reduce or mitigate outbreaks if they introduce timely protective measures.
One clear aspect of behaviour in the COVID-19 pandemic has been people's focus on, and response t... more One clear aspect of behaviour in the COVID-19 pandemic has been people's focus on, and response to, reported or observed infection numbers in their community. We describe a simple model of infectious disease spread in a pandemic situation where people's behaviour is influenced by the current risk of infection and where this behavioural response acts homeostatically to return infection risk to a certain preferred level. This model predicts that the reproduction rate R will be centered around a median value of 1, and that a related measure of relative change in the number of new infections will follow the standard Cauchy distribution. Analysis of worldwide COVID-19 data shows that the estimated reproduction rate has a median of 1, and that this measure of relative change calculated from reported numbers of new infections closely follows the standard Cauchy distribution at both an overall and an individual country level.
A number of recent theories have suggested that the various systematic biases and fallacies seen ... more A number of recent theories have suggested that the various systematic biases and fallacies seen in people\u27s probabilistic reasoning may arise purely as a consequence of random variation in the reasoning process. The underlying argument, in these theories, is that random variation has systematic regressive effects, so producing the observed patterns of bias. These theories typically take this random variation as a given, and assume that the degree of random variation in probabilistic reasoning is sufficiently large to account for observed patterns of fallacy and bias; there has been very little research directly examining the character of random variation in people\u27s probabilistic judgement. In this thesis, 4 experiments are described that investigate the degree, level, and characteristic properties of random variation in people\u27s probability judgement. They show that the degree of variance is easily large enough to account for the occurrence of two central fallacies in probabilistic reasoning (the conjunction fallacy and the disjunction fallacy), and that level of variance is a reliable predictor of the occurrence of these fallacies. In addition, it is demonstrated that random variance in people\u27s probabilistic judgement follows a particular mathematical model from frequentist probability theory: the binomial proportion distribution. This result supports a model in which people reason about probabilities in a way that follows frequentist probability theory but is subject to random variation or noise
This paper investigates a fundamental conflict in the literature on people’s probability estimati... more This paper investigates a fundamental conflict in the literature on people’s probability estimation. Research on ‘perception’ of probability shows that people are accurate in their estimates of probability of various simple events from samples. Equally, however, a large body of research shows that people’s probability estimates are fundamentally biased, and subject to reliable and striking fallacies in reasoning. We investigate this conflict in an experiment that examines the occurrence of the conjunction fallacy in a probability perception task where people are asked to estimate the probability of simple and conjunctive events in a presented set of items. We find that people’s probability estimates are accurate, especially for simple events, just as seen in previous studies. People’s estimates also show high rates of occurrence of the conjunction fallacy. We show how this apparently contradictory result is consistent with a recent model of probability estimation, the probability th...
The conjunction fallacy occurs when people judge a conjunction A&B as more likely than a constitu... more The conjunction fallacy occurs when people judge a conjunction A&B as more likely than a constituent A, contrary to the rules of probability theory. We describe a model where this fallacy arises purely as a consequence of noise and random error in the probability estimation process. We describe an experiment testing this proposal by assessing the relationship between fallacy rates and the average difference between conjunction and constituent estimates (in the model, the smaller this difference, the more likely it is that the conjunction fallacy can occur due to random error), and by assessing the degree of inconsistency in people’s conjunction fallacy responses for repeated presentations of the same probability questions (in the model these responses should tend to be inconsistent due to random error, especially in cases where the average difference in estimates is low). Experimental results support both these predictions.
A number of recent theories have suggested that the various systematic biases and fallacies seen ... more A number of recent theories have suggested that the various systematic biases and fallacies seen in people's probabilistic reasoning may arise purely as a consequence of random variation in the reasoning process. The underlying argument, in these theories, is that random variation has systematic regressive effects, so producing the observed patterns of bias. These theories typically take this random variation as a given, and assume that the degree of random variation in probabilistic reasoning is sufficiently large to account for observed patterns of fallacy and bias; there has been very little research directly examining the character of random variation in people's probabilistic judgement. We describe 4 experiments investigating the degree, level, and characteristic properties of random variation in people's probability judgement. We show that the degree of variance is easily large enough to account for the occurrence of two central fallacies in probabilistic reasoning (the conjunction fallacy and the disjunction fallacy), and that level of variance is a reliable predictor of the occurrence of these fallacies. We also show that random variance in people's probabilistic judgement follows a particular mathematical model from frequentist probability theory: the binomial proportion distribution. This result supports a model in which people reason about probabilities in a way that follows frequentist probability theory but is subject to random variation or noise.
COVID-19 IN IRISH WORKPLACES AND COMMUNITIES - MODELLING OUTBREAKS FROM INFECTION DATA , 2022
During 2020-2021, the government of Ireland in line with international recommendations imposed th... more During 2020-2021, the government of Ireland in line with international recommendations imposed the closure of non-essential trades, services, and commerce. Food plant factories, meat processing plants among others were deemed essential and remained open. During that time, many workers were exposed to outbreaks in their workplaces. Some of the questions arising included if workers will adapt to new safety measures, if those measures could prevent and mitigate workplace outbreaks and , if an outbreak occur in a closed facility, if it will impact community transmission. The most vulnerable workplaces were typically front-line industries, with healthcare and food processing facilities among the hardest hit by Covid-19 infections. To complete the core aims, statistical models were developed for WP1. These models could accurately predict the scale of an outbreak in a meat processing plant based on the infection transmission in the community in the weeks preceding the outbreak and account for patterns in infection spread in both Ireland and worldwide using a ‘behavioural response’ mechanism. In addition to this, vaccine effectiveness was calculated using a method that made use of surveillance data. This demonstrated the strength and limitations of surveillance data. One clear aspect of behaviour in the COVID-19 pandemic has been people’s focus on, and response to, reported or observed infection numbers in their community. WP1 developed a simple model of infectious disease spread in a pandemic situation where people’s behaviour is influenced by the current risk of infection and where this behavioural response acts homeostatically to return infection risk to a certain preferred level. Analysis of worldwide COVID-19 data confirmed the model predictions at both an overall and an individual country level. Building on the findings of the infectious disease spread model, the research team aimed to investigate how individuals adapted their behaviours throughout the pandemic at an individual level, using the number of community cases and the number of contacts reported by cases to the contact-tracing program as a proxy for behavioural response. This work is ongoing at this time. In addition to this, estimations on vaccine effectiveness were calculated using a method that made use of surveillance data. This demonstrated the strength and limitations of surveillance data. There were significant challenges in completing WP1, primarily caused by a difficulty in accessing the required data, however, the primary aims and goals of the work package were achieved and a meaningful body of research was produced on disease spread in specific, controlled environments and among the general population.Our work will certainly inform future pandemics. The main messages are 1) that community transmission can predict the occurrence of outbreaks -suggesting that managers and Public Health officials should work together to reinforce surveillance during peaks of community transmission and 2) high risk settings -like meat factories- can reduce or mitigate outbreaks if they introduce timely protective measures.
One clear aspect of behaviour in the COVID-19 pandemic has been people's focus on, and response t... more One clear aspect of behaviour in the COVID-19 pandemic has been people's focus on, and response to, reported or observed infection numbers in their community. We describe a simple model of infectious disease spread in a pandemic situation where people's behaviour is influenced by the current risk of infection and where this behavioural response acts homeostatically to return infection risk to a certain preferred level. This model predicts that the reproduction rate R will be centered around a median value of 1, and that a related measure of relative change in the number of new infections will follow the standard Cauchy distribution. Analysis of worldwide COVID-19 data shows that the estimated reproduction rate has a median of 1, and that this measure of relative change calculated from reported numbers of new infections closely follows the standard Cauchy distribution at both an overall and an individual country level.
A number of recent theories have suggested that the various systematic biases and fallacies seen ... more A number of recent theories have suggested that the various systematic biases and fallacies seen in people\u27s probabilistic reasoning may arise purely as a consequence of random variation in the reasoning process. The underlying argument, in these theories, is that random variation has systematic regressive effects, so producing the observed patterns of bias. These theories typically take this random variation as a given, and assume that the degree of random variation in probabilistic reasoning is sufficiently large to account for observed patterns of fallacy and bias; there has been very little research directly examining the character of random variation in people\u27s probabilistic judgement. In this thesis, 4 experiments are described that investigate the degree, level, and characteristic properties of random variation in people\u27s probability judgement. They show that the degree of variance is easily large enough to account for the occurrence of two central fallacies in probabilistic reasoning (the conjunction fallacy and the disjunction fallacy), and that level of variance is a reliable predictor of the occurrence of these fallacies. In addition, it is demonstrated that random variance in people\u27s probabilistic judgement follows a particular mathematical model from frequentist probability theory: the binomial proportion distribution. This result supports a model in which people reason about probabilities in a way that follows frequentist probability theory but is subject to random variation or noise
This paper investigates a fundamental conflict in the literature on people’s probability estimati... more This paper investigates a fundamental conflict in the literature on people’s probability estimation. Research on ‘perception’ of probability shows that people are accurate in their estimates of probability of various simple events from samples. Equally, however, a large body of research shows that people’s probability estimates are fundamentally biased, and subject to reliable and striking fallacies in reasoning. We investigate this conflict in an experiment that examines the occurrence of the conjunction fallacy in a probability perception task where people are asked to estimate the probability of simple and conjunctive events in a presented set of items. We find that people’s probability estimates are accurate, especially for simple events, just as seen in previous studies. People’s estimates also show high rates of occurrence of the conjunction fallacy. We show how this apparently contradictory result is consistent with a recent model of probability estimation, the probability th...
The conjunction fallacy occurs when people judge a conjunction A&B as more likely than a constitu... more The conjunction fallacy occurs when people judge a conjunction A&B as more likely than a constituent A, contrary to the rules of probability theory. We describe a model where this fallacy arises purely as a consequence of noise and random error in the probability estimation process. We describe an experiment testing this proposal by assessing the relationship between fallacy rates and the average difference between conjunction and constituent estimates (in the model, the smaller this difference, the more likely it is that the conjunction fallacy can occur due to random error), and by assessing the degree of inconsistency in people’s conjunction fallacy responses for repeated presentations of the same probability questions (in the model these responses should tend to be inconsistent due to random error, especially in cases where the average difference in estimates is low). Experimental results support both these predictions.
A number of recent theories have suggested that the various systematic biases and fallacies seen ... more A number of recent theories have suggested that the various systematic biases and fallacies seen in people's probabilistic reasoning may arise purely as a consequence of random variation in the reasoning process. The underlying argument, in these theories, is that random variation has systematic regressive effects, so producing the observed patterns of bias. These theories typically take this random variation as a given, and assume that the degree of random variation in probabilistic reasoning is sufficiently large to account for observed patterns of fallacy and bias; there has been very little research directly examining the character of random variation in people's probabilistic judgement. We describe 4 experiments investigating the degree, level, and characteristic properties of random variation in people's probability judgement. We show that the degree of variance is easily large enough to account for the occurrence of two central fallacies in probabilistic reasoning (the conjunction fallacy and the disjunction fallacy), and that level of variance is a reliable predictor of the occurrence of these fallacies. We also show that random variance in people's probabilistic judgement follows a particular mathematical model from frequentist probability theory: the binomial proportion distribution. This result supports a model in which people reason about probabilities in a way that follows frequentist probability theory but is subject to random variation or noise.
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could prevent and mitigate workplace outbreaks and , if an outbreak occur in a closed facility, if it will impact community transmission. The most vulnerable workplaces were typically front-line industries, with healthcare and food processing facilities among the hardest hit by Covid-19 infections.
To complete the core aims, statistical models were developed for WP1. These models could accurately predict the scale of an outbreak in a meat processing plant based on the infection transmission in the community in the weeks preceding the outbreak and account for patterns in infection spread in both Ireland and worldwide using a ‘behavioural response’ mechanism. In addition to this, vaccine effectiveness was calculated using a method that made use of surveillance data. This demonstrated the strength and limitations of surveillance data.
One clear aspect of behaviour in the COVID-19 pandemic has been people’s focus on, and response to, reported or observed infection numbers in their community. WP1 developed a simple model of infectious disease spread in a pandemic situation where people’s behaviour is influenced by the current risk of infection and where this behavioural response acts homeostatically to return infection risk to a certain preferred level. Analysis of worldwide
COVID-19 data confirmed the model predictions at both an overall and an individual country level.
Building on the findings of the infectious disease spread model, the research team aimed to investigate how individuals adapted their behaviours throughout the pandemic at an individual level, using the number of community cases and the number of contacts reported by cases to the contact-tracing program as a proxy for behavioural response. This work is
ongoing at this time. In addition to this, estimations on vaccine effectiveness were calculated using a method that made use of surveillance data. This demonstrated the strength and limitations of surveillance data.
There were significant challenges in completing WP1, primarily caused by a difficulty in accessing the required data, however, the primary aims and goals of the work package were achieved and a meaningful body of research was produced on disease spread in specific, controlled environments and among the general population.Our work will certainly inform future pandemics. The main messages are 1) that community transmission can predict the
occurrence of outbreaks -suggesting that managers and Public Health officials should work together to reinforce surveillance during peaks of community transmission and 2) high risk settings -like meat factories- can reduce or mitigate outbreaks if they introduce timely protective measures.
Papers by Rita Howe
could prevent and mitigate workplace outbreaks and , if an outbreak occur in a closed facility, if it will impact community transmission. The most vulnerable workplaces were typically front-line industries, with healthcare and food processing facilities among the hardest hit by Covid-19 infections.
To complete the core aims, statistical models were developed for WP1. These models could accurately predict the scale of an outbreak in a meat processing plant based on the infection transmission in the community in the weeks preceding the outbreak and account for patterns in infection spread in both Ireland and worldwide using a ‘behavioural response’ mechanism. In addition to this, vaccine effectiveness was calculated using a method that made use of surveillance data. This demonstrated the strength and limitations of surveillance data.
One clear aspect of behaviour in the COVID-19 pandemic has been people’s focus on, and response to, reported or observed infection numbers in their community. WP1 developed a simple model of infectious disease spread in a pandemic situation where people’s behaviour is influenced by the current risk of infection and where this behavioural response acts homeostatically to return infection risk to a certain preferred level. Analysis of worldwide
COVID-19 data confirmed the model predictions at both an overall and an individual country level.
Building on the findings of the infectious disease spread model, the research team aimed to investigate how individuals adapted their behaviours throughout the pandemic at an individual level, using the number of community cases and the number of contacts reported by cases to the contact-tracing program as a proxy for behavioural response. This work is
ongoing at this time. In addition to this, estimations on vaccine effectiveness were calculated using a method that made use of surveillance data. This demonstrated the strength and limitations of surveillance data.
There were significant challenges in completing WP1, primarily caused by a difficulty in accessing the required data, however, the primary aims and goals of the work package were achieved and a meaningful body of research was produced on disease spread in specific, controlled environments and among the general population.Our work will certainly inform future pandemics. The main messages are 1) that community transmission can predict the
occurrence of outbreaks -suggesting that managers and Public Health officials should work together to reinforce surveillance during peaks of community transmission and 2) high risk settings -like meat factories- can reduce or mitigate outbreaks if they introduce timely protective measures.