Additional file 1: Table S1. The total number of presence data points for each Culex species used... more Additional file 1: Table S1. The total number of presence data points for each Culex species used in model development after filtering by the 30-km radial buffer. Table S2. Final model specifications and performance metrics for each species. Table S3. Summary of environmental factors important for each of the seven Culex species, which were obtained from the literature review. Table S4. Percent environmental variable contribution during Maxent model development for each Culex species. Figure S1. Maps of the environmental training area unique to each species used for the Maxent models across North America for a Culex pipiens, b Culex restuans, c Culex salinarius, and d Culex tarsalis. These were created by buffering the data based on the median distance from each presence data point to the centroid of all presence points. Ten thousand background points are randomly sampled from the shaded environmental training area when running Maxent. Figure S2. Maps of the environmental training a...
In this work we demonstrate a method for leveraging high-fidelity, multi-physics simulations of h... more In this work we demonstrate a method for leveraging high-fidelity, multi-physics simulations of high-speed impacts in a particular manufactured material to encode prior information regarding the impactor material's strength properties. Our simulations involve a material composed of stacked cylindrical ligaments impacted by a high-velocity aluminum plate. We show that deep neural networks of relatively simple architecture can be trained on the simulations to make highly-accurate inferences of the strength properties of the impactor material. We detail our neural architectures and the considerations that went into their design. In addition, we discuss the simplicity of our network architecture which lends itself to interpretability of learned features in radiographic observations.
We demonstrate and analyze a new methodology for detecting and characterizing shocks in high ener... more We demonstrate and analyze a new methodology for detecting and characterizing shocks in high energy density physics (HEDP) experiments using simulated radiographs. Our method consists of simulating many variations of an HEDP experiment using a multi-physics modeling code developed at Los Alamos National Laboratory. These simulations are then used to produce synthetic radiographs that emulate the actual experimental data collection setup. Shock contours are defined by the peaks of the density derivative values obtained at each radial coordinate of an x-rayed cylindrical object, giving us a ground truth of shock position that would not be available directly from the observed radiograph. Convolutional neural networks are then trained on our simulations, mapping radiograph to shock structure. We investigate four different state-of-the-art deep convolutional neural networks, Xception, ResNet152, VGG19, and U-Net, for use in regressing the HEDP radiograph to the shock position. It is demonstrated that our neural network approach offers a highly accurate shock locator. We find that the different network architectures are better tuned for locating distinct shock characteristics, equivalent to detecting shockwaves at multiple scales. Differences are quantified by ranking the four architectures by their overall performance accuracy. The regression model based on the Xception architecture is found to yield the highest accuracy. In order to understand limitations of these techniques to external perturbations in the experimental setup we also apply our trained networks to shock location in the presence of Gaussian and Flatfield noise. We find that the network shock-locations are surprisingly robust to noise giving confidence that they will perform well on experimental data.
Background Estimates of the geographical distribution of Culex mosquitoes in the Americas have be... more Background Estimates of the geographical distribution of Culex mosquitoes in the Americas have been limited to state and provincial levels in the United States and Canada and based on data from the 1980s. Since these estimates were made, there have been many more documented observations of mosquitoes and new methods have been developed for species distribution modeling. Moreover, mosquito distributions are affected by environmental conditions, which have changed since the 1980s. This calls for updated estimates of these distributions to understand the risk of emerging and re-emerging mosquito-borne diseases. Methods We used contemporary mosquito data, environmental drivers, and a machine learning ecological niche model to create updated estimates of the geographical range of seven predominant Culex species across North America and South America: Culex erraticus, Culex nigripalpus, Culex pipiens, Culex quinquefasciatus, Culex restuans, Culex salinarius, and Culex tarsalis. Results We...
While the number of human cases of mosquito-borne diseases has increased in North America in the ... more While the number of human cases of mosquito-borne diseases has increased in North America in the last decade, accurate modeling of mosquito population density has remained a challenge. Longitudinal mosquito trap data over the many years needed for model calibration is relatively rare. In particular, capturing the relative changes in mosquito abundance across seasons is necessary for predicting the risk of disease spread as it varies from year to year. We developed a process-based mosquito population model that captures life-cycle egg, larva, pupa, adult stages, and diapause for Culex pipiens and Culex restuans mosquito populations. Mosquito development through these stages is a function of time, temperature, daylight hours, and aquatic habitat availability. The time-dependent parameters are informed by both laboratory studies and mosquito trap data from the Greater Toronto Area. The model incorporates city-wide water-body gauge and precipitation data as a proxy for aquatic habitat. ...
The many respiratory viruses that cause influenza-like illness (ILI) are reported and tracked as ... more The many respiratory viruses that cause influenza-like illness (ILI) are reported and tracked as one entity, defined by the CDC as a group of symptoms that include a fever of 100 degrees Fahrenheit and a cough and/or a sore throat. In the United States alone, ILI impacts 9-49 million people every year. While tracking ILI as a single clinical syndrome is informative in many respects, the underlying viruses differ in their parameters and outbreak properties. Most existing models treat either a single respiratory virus or ILI as a whole. However, there is a need for models capable of comparing several individual ILI viruses. To address this need, here we present a flexible model and simulations of epidemics for influenza, RSV, rhinovirus, seasonal coronavirus, adenovirus, and SARS/MERS, parameterized by a systematic literature review and accompanied by a global sensitivity analysis. We find that for these biological causes of ILI, their parameter values, timing, prevalence, and proport...
Recent clinical studies have shown that HIV disease pathogenesis can depend strongly on many fact... more Recent clinical studies have shown that HIV disease pathogenesis can depend strongly on many factors at the time of transmission, including the strength of the initial viral load and the local availability of CD4+ T-cells. In this article, a new within-host model of HIV infection that incorporates the homeostatic proliferation of T-cells is formulated and analyzed. Due to the effects of this biological process, the influence of initial conditions on the proliferation of HIV infection is further elucidated. The identifiability of parameters within the model is investigated and a local stability analysis, which displays additional complexity in comparison to previous models, is conducted. The current study extends previous theoretical and computational work on the early stages of the disease and leads to interesting nonlinear dynamics, including a parameter region featuring bistability of infectious and viral clearance equilibria and the appearance of a Hopf bifurcation within biologically relevant parameter regimes.
As South and Central American countries prepare for increased birth defects from Zika virus outbr... more As South and Central American countries prepare for increased birth defects from Zika virus outbreaks and plan for mitigation strategies to minimize ongoing and future outbreaks, understanding important characteristics of Zika outbreaks and how they vary across regions is a challenging and important problem. We developed a mathematical model for the 2015 Zika virus outbreak dynamics in Colombia, El Salvador, and Suriname. We fit the model to publicly available data provided by the Pan American Health Organization, using Approximate Bayesian Computation to estimate parameter distributions and provide uncertainty quantification. An important model input is the at-risk susceptible population, which can vary with a number of factors including climate, elevation, population density, and socioeconomic status. We informed this initial condition using the highest historically reported dengue incidence modified by the probable dengue reporting rates in the chosen countries. The model indicated that a country-level analysis was not appropriate for Colombia. We then estimated the basic reproduction number, or the expected number of new human infections arising from a single infected human, to range between 4 and 6 for El Salvador and Suriname with a median of 4.3 and 5.3, respectively. We estimated the reporting rate to be around 16% in El Salvador and 18% in Suriname with estimated total outbreak sizes of 73,395 and 21,647 people, respectively. The uncertainty in parameter estimates highlights a need for research and data collection that will better constrain parameter ranges.
Additional file 1: Table S1. The total number of presence data points for each Culex species used... more Additional file 1: Table S1. The total number of presence data points for each Culex species used in model development after filtering by the 30-km radial buffer. Table S2. Final model specifications and performance metrics for each species. Table S3. Summary of environmental factors important for each of the seven Culex species, which were obtained from the literature review. Table S4. Percent environmental variable contribution during Maxent model development for each Culex species. Figure S1. Maps of the environmental training area unique to each species used for the Maxent models across North America for a Culex pipiens, b Culex restuans, c Culex salinarius, and d Culex tarsalis. These were created by buffering the data based on the median distance from each presence data point to the centroid of all presence points. Ten thousand background points are randomly sampled from the shaded environmental training area when running Maxent. Figure S2. Maps of the environmental training a...
In this work we demonstrate a method for leveraging high-fidelity, multi-physics simulations of h... more In this work we demonstrate a method for leveraging high-fidelity, multi-physics simulations of high-speed impacts in a particular manufactured material to encode prior information regarding the impactor material's strength properties. Our simulations involve a material composed of stacked cylindrical ligaments impacted by a high-velocity aluminum plate. We show that deep neural networks of relatively simple architecture can be trained on the simulations to make highly-accurate inferences of the strength properties of the impactor material. We detail our neural architectures and the considerations that went into their design. In addition, we discuss the simplicity of our network architecture which lends itself to interpretability of learned features in radiographic observations.
We demonstrate and analyze a new methodology for detecting and characterizing shocks in high ener... more We demonstrate and analyze a new methodology for detecting and characterizing shocks in high energy density physics (HEDP) experiments using simulated radiographs. Our method consists of simulating many variations of an HEDP experiment using a multi-physics modeling code developed at Los Alamos National Laboratory. These simulations are then used to produce synthetic radiographs that emulate the actual experimental data collection setup. Shock contours are defined by the peaks of the density derivative values obtained at each radial coordinate of an x-rayed cylindrical object, giving us a ground truth of shock position that would not be available directly from the observed radiograph. Convolutional neural networks are then trained on our simulations, mapping radiograph to shock structure. We investigate four different state-of-the-art deep convolutional neural networks, Xception, ResNet152, VGG19, and U-Net, for use in regressing the HEDP radiograph to the shock position. It is demonstrated that our neural network approach offers a highly accurate shock locator. We find that the different network architectures are better tuned for locating distinct shock characteristics, equivalent to detecting shockwaves at multiple scales. Differences are quantified by ranking the four architectures by their overall performance accuracy. The regression model based on the Xception architecture is found to yield the highest accuracy. In order to understand limitations of these techniques to external perturbations in the experimental setup we also apply our trained networks to shock location in the presence of Gaussian and Flatfield noise. We find that the network shock-locations are surprisingly robust to noise giving confidence that they will perform well on experimental data.
Background Estimates of the geographical distribution of Culex mosquitoes in the Americas have be... more Background Estimates of the geographical distribution of Culex mosquitoes in the Americas have been limited to state and provincial levels in the United States and Canada and based on data from the 1980s. Since these estimates were made, there have been many more documented observations of mosquitoes and new methods have been developed for species distribution modeling. Moreover, mosquito distributions are affected by environmental conditions, which have changed since the 1980s. This calls for updated estimates of these distributions to understand the risk of emerging and re-emerging mosquito-borne diseases. Methods We used contemporary mosquito data, environmental drivers, and a machine learning ecological niche model to create updated estimates of the geographical range of seven predominant Culex species across North America and South America: Culex erraticus, Culex nigripalpus, Culex pipiens, Culex quinquefasciatus, Culex restuans, Culex salinarius, and Culex tarsalis. Results We...
While the number of human cases of mosquito-borne diseases has increased in North America in the ... more While the number of human cases of mosquito-borne diseases has increased in North America in the last decade, accurate modeling of mosquito population density has remained a challenge. Longitudinal mosquito trap data over the many years needed for model calibration is relatively rare. In particular, capturing the relative changes in mosquito abundance across seasons is necessary for predicting the risk of disease spread as it varies from year to year. We developed a process-based mosquito population model that captures life-cycle egg, larva, pupa, adult stages, and diapause for Culex pipiens and Culex restuans mosquito populations. Mosquito development through these stages is a function of time, temperature, daylight hours, and aquatic habitat availability. The time-dependent parameters are informed by both laboratory studies and mosquito trap data from the Greater Toronto Area. The model incorporates city-wide water-body gauge and precipitation data as a proxy for aquatic habitat. ...
The many respiratory viruses that cause influenza-like illness (ILI) are reported and tracked as ... more The many respiratory viruses that cause influenza-like illness (ILI) are reported and tracked as one entity, defined by the CDC as a group of symptoms that include a fever of 100 degrees Fahrenheit and a cough and/or a sore throat. In the United States alone, ILI impacts 9-49 million people every year. While tracking ILI as a single clinical syndrome is informative in many respects, the underlying viruses differ in their parameters and outbreak properties. Most existing models treat either a single respiratory virus or ILI as a whole. However, there is a need for models capable of comparing several individual ILI viruses. To address this need, here we present a flexible model and simulations of epidemics for influenza, RSV, rhinovirus, seasonal coronavirus, adenovirus, and SARS/MERS, parameterized by a systematic literature review and accompanied by a global sensitivity analysis. We find that for these biological causes of ILI, their parameter values, timing, prevalence, and proport...
Recent clinical studies have shown that HIV disease pathogenesis can depend strongly on many fact... more Recent clinical studies have shown that HIV disease pathogenesis can depend strongly on many factors at the time of transmission, including the strength of the initial viral load and the local availability of CD4+ T-cells. In this article, a new within-host model of HIV infection that incorporates the homeostatic proliferation of T-cells is formulated and analyzed. Due to the effects of this biological process, the influence of initial conditions on the proliferation of HIV infection is further elucidated. The identifiability of parameters within the model is investigated and a local stability analysis, which displays additional complexity in comparison to previous models, is conducted. The current study extends previous theoretical and computational work on the early stages of the disease and leads to interesting nonlinear dynamics, including a parameter region featuring bistability of infectious and viral clearance equilibria and the appearance of a Hopf bifurcation within biologically relevant parameter regimes.
As South and Central American countries prepare for increased birth defects from Zika virus outbr... more As South and Central American countries prepare for increased birth defects from Zika virus outbreaks and plan for mitigation strategies to minimize ongoing and future outbreaks, understanding important characteristics of Zika outbreaks and how they vary across regions is a challenging and important problem. We developed a mathematical model for the 2015 Zika virus outbreak dynamics in Colombia, El Salvador, and Suriname. We fit the model to publicly available data provided by the Pan American Health Organization, using Approximate Bayesian Computation to estimate parameter distributions and provide uncertainty quantification. An important model input is the at-risk susceptible population, which can vary with a number of factors including climate, elevation, population density, and socioeconomic status. We informed this initial condition using the highest historically reported dengue incidence modified by the probable dengue reporting rates in the chosen countries. The model indicated that a country-level analysis was not appropriate for Colombia. We then estimated the basic reproduction number, or the expected number of new human infections arising from a single infected human, to range between 4 and 6 for El Salvador and Suriname with a median of 4.3 and 5.3, respectively. We estimated the reporting rate to be around 16% in El Salvador and 18% in Suriname with estimated total outbreak sizes of 73,395 and 21,647 people, respectively. The uncertainty in parameter estimates highlights a need for research and data collection that will better constrain parameter ranges.
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Papers by Deborah Shutt