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All sourcesNASA NTRSDOE OSTI
NASA NTRS2021

Peru Health & Air Quality: Land Use Change in the Rapidly Developing Peruvian Amazon and Implications on Zoonotic Disease Incidence

In the Madre de Dios region of the Peruvian Amazon, forests are being cleared for mining, timber harvesting, road construction, and hydroelectric dam development. These rapid land use changes are increasing human presence in previously sparsely populated areas, disrupting ecosystems and increasing the proximity of human settlement to zoonotic disease vectors. Dengue fever and leishmaniasis are two neglected tropical diseases which are prevalent in Madre de Dios and have been associated with urbanization and road construction. In partnership with the Peruvian Ministries of Health (MINSA) and the Environment (MINAM) and other in-country collaborators, our team examined Land Use Land Cover (LULC) correlations with reported dengue and leishmaniasis incidence in the Madre de Dios region to help partners understand the spatial relationship between land use change and zoonotic disease incidence. We created a LULC classification script using Google Earth Engine with Landsat 5 Thematic Mapper and Landsat 8 Operational Land Imager imagery to classify land cover in 2010, 2015, and 2020 and evaluate changes over this time period. We then used the quantified results of the LULC assessment in conjunction with reported disease cases to evaluate correlations between disease incidence and key land cover changes across Madre de Dios’s 11 districts. In the second term, the team will use these products to develop more detailed disease incidence risk maps and models. High risk areas will then be classified, using PeruSat-1 that allow for even higher resolution mapping at less than 3 meters. These products will allow the partners to understand hotspots of land cover change in Peru and the relationship with outbreaks to inform public health decision making and environmental policy.

Elizabeth Stapleton↗
DOE OSTI2021

Regional Relative Risk, a Physics-Based Metric for Characterizing Airborne Infectious Disease Transmission

Airborne infectious disease transmission events occur over a wide range of spatial scales and can be an important means of disease transmission. Physics- and biology-based models can assist in predicting airborne transmission events, overall disease incidence, and disease control strategy efficacy. We describe a new theory that extends current approaches for the case in which an individual is infected by a single airborne particle, including the scenario in which numerous infectious particles are present in the air but only one causes infection. A single infectious particle can contain more than one pathogenic microorganism and be physically larger than the pathogen itself. This approach allows robust relative risk estimates even when there is wide variation in (i) individual exposures and (ii) the individual response to that exposure (the pathogen dose-response function can take any mathematical form and vary by individual). Based on this theory, we propose the regional relative risk—a new metric, distinct from the traditional relative risk metric, that compares the risk between two regions. In theory, these regions can range from individual rooms to large geographic areas. In this paper, we apply the regional relative risk metric to outdoor disease transmission events over spatial scales ranging from 50 m to 20 km, demonstrating that in many common cases minimal input information is required to use the metric. Also, we demonstrate that the model predictions are consistent with data from prior outbreaks. Future efforts could apply and validate this theory for other spatial scales, such as transmission within indoor environments. This work provides context for (i) the initial stages of an airborne disease outbreak and (ii) larger-scale disease spread, including unexpected low-probability disease “sparks” that potentially affect remote populations, a key practical issue in controlling airborne disease outbreaks.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1975

Characteristics and tolerances of the pocket mouse and incidence of disease

Studies carried out on the pocket mouse colony on Apollo XVII are reported. They revealed no serological evidence of viral disease, no pathogenic enterobacteria or respiratory Mycoplasma on culture, a 25% incidence of sarcosporidiosis, and a 2% incidence of chronic meningitis or meningoencephalitis. It is concluded that the pocket mouse is a highly adaptive animal and very well-suited to space flight.

Lindberg, R. G.↗
DOE OSTI2023

Bark morphological and chemical features are differentially correlated with disease resistance and yield in hybrid poplar taxa

In the southeastern United States, the establishment of short-rotation intensively cultured plantations of hybrid poplar has been hindered by its susceptibility to stem cankers. We evaluated the tradeoffs between biomass yield and disease tolerance in hybrid poplar genotypes belonging to P. deltoides × P. maximowiczii (DM), P. deltoides × P. nigra (DN), P. trichocarpa × P. maximowiczii (TM), and P. deltoides × P. deltoides (DD) taxa. We hypothesized that canker resistant genotypes will have thicker bark but bark thickness and biomass yield will be negatively correlated. After two growing seasons, the DD genotypes developed thicker bark compared to the genotypes of other taxa and bark thickness was not correlated with biomass yield in the DD genotypes (R 2 = 0.002). However, in the TM, DM, and DN genotypes, bark thickness was negatively correlated with biomass yield (R 2 = 0.33–0.77). Disease incidence studies revealed that the DM genotypes were most susceptible to canker whereas no disease was detected in DD genotypes. Furthermore, bark analysis conducted by Fourier transform infrared spectroscopy coupled with multivariate analysis showed that that DD genotypes to be chemically separate from the three hybrid genotypes and that bark chemistry was correlated with canker disease incidence. Taken together, these results reveal that it is possible to generate hybrid poplar genotypes with thicker bark, disease resistance, and higher biomass yields. This insight should guide further efforts to develop genetically improved hybrid poplar genotypes, both in terms of biomass yield and disease tolerance, for cultivation in the southeastern United States. Hybrid poplar cultivation in southeastern United States is hindered by its susceptibility to stem cankers. We evaluated tradeoffs between yield and canker disease resistance in various hybrid poplar genotypes. After two growing seasons, the DD genotypes showed disease resistance and developed thicker bark that was chemically distinct from the other genotypes. Bark thickness was not correlated with yield in the DD genotypes but was negatively correlated with yield in the other genotypes. These results will guide the development of hybrid poplar genotypes that are both disease resistant and high yielding for cultivation in the southeastern United States.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1997

Remote Sensing the Patterns of Vector-borne Disease in El Nino and non-El Nino Years

The relationship between El Nino and non-El Nino and the patterns of vector-borne disease can be viewed at a variety of spatial and temporal scales. At one extreme are long term predictions of changing precipitation and temperature patterns at continental and global scales. At the opposite extreme are the local or site specific ecological changes associated with the long term events. In order to understand and address the human health consequences of El Nino events, especially the patterns of vector-borne diseases, it is necessary to combine both scales of observation. At a local or regional scale the patterns of vector-borne diseases are determined by temperature, precipitation, and habitat availability. These factors, as well as disease incidence can be altered by El Nino events. Remote sensing data such as that acquired by the NOAA AVHRR and Landsat TM sensors can be used to characterize and monitor changing ecological conditions and therefore predict vector-borne disease patterns. The authors present the results of preliminary work on the analysis of historical AVHRR and TM data acquired during El Nino and nonfatal Nino years to characterize ecological conditions in Peru on a monthly basis. This information will then be combined with disease data to determine the relationship between changes in ecological conditions and disease incidence. Our goal is to produce a sequence of remotely sensed images which can be used to show the ecological and disease patterns associated with long term El Nino events and predictions.

Wood, B. L.↗
DOE OSTI2023

Identification of Colletotrichum aenigma as the new causal agent of leaf blight disease on Aucuba japonica Thunb., and screenings of effective fungicides for its sustainable management

Aucuba japonica Thunb is an evergreen woody ornamental plant with significant economic and ecological values. It also produces aucubin, showing a variety of biological activities. It is widely planted in the southwest region of China, including karst landscape areas in Guizhou Province. In January 2022, a serious leaf blight disease was observed on the leaves of A. japonica in the outdoor gardens of Guizhou University, Guiyang, Guizhou, China. The causal agent was identified as Colletotrichum aenigma through amplification and sequencing of the internal transcribed spacer (ITS) region, translation of the chitin synthase ( CHS ) and actin ( ACT ) genes, and morphological characterizations. Koch’s postulates were confirmed by its pathogenicity on healthy leaves, including re-isolation and identification. To our knowledge, this is the first report of C. aenigma causing leaf blight on A. japonica worldwide. To identify pathogen characteristics that could be utilized for future disease management, the effects of temperature and light on mycelial growth, conidia production, and conidial germination, and the effects of humidity on conidial germination were studied. Optimal temperatures for mycelial growth of C. aenigma BY827 were 25–30°C, while 15°C and 35°C were favorable for conidia production. Concurrently, alternating 10-h light and 14-h dark, proved to be beneficial for mycelial growth and conidial germination. Additionally, conidial germination was enhanced at 90% humidity. In vitro screenings of ten chemical pesticides to assess their efficacy in suppressing C. aenigma representative strain BY827. Among them, difenoconazole showed the best inhibition rate, with an EC 50 (concentration for 50% of maximal effect) value of 0.0148 μg/ml. Subsequently, field experiment results showed that difenoconazole had the highest control efficiency on A. japonica leaf blight (the decreasing rate of disease incidence and decreasing rate of disease index were 44.60 and 47.75%, respectively). Interestingly, we discovered that C. aenigma BY827 may develop resistance to mancozeb, which is not reported yet among Colletotrichum spp. strains. In conclusion, our study provided new insights into the causal agent of A. japonica leaf blight, and the effective fungicides evaluated provided an important basis and potential resource for the sustainable control of A. japonica leaf blight caused by C. aenigma in the field.

Fan, Ruidong↗
DOE OSTI2020

RETRO RX

RETRO Rx is an R&D 100 Gold Award-winning suite of tools that provides a new class of analytics in the space between traditional epidemiological models and unanalyzed spreadsheets of data. Unlike traditional models, these tools provide analysts, scientists, practitioners, and decision makers a way to easily and effectively do sophisticated analyses using a simple user interface. AIDO guides users at the early stages of a disease outbreak by comparing the current parameters to historic outbreaks. RED Alert constructs a broad historical picture of disease incidence (recent outbreak included) to detect disease reemergence at its earliest stages, locally and globally. RETRO Rx compiles data and presents analysis in a clear, concise visual format to support disease mitigation and prevention planning. Both are available currently as web-based apps. A prototype of the RED Alert mobile app with enhanced functionality is available for beta testing and AIDO mobile app development is planned.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Advocating for Coccidioidomycosis to Be a Reportable Disease Nationwide in the United States and Encouraging Disease Surveillance across North and South America

Coccidioidomycosis (Valley fever) has been a known health threat in the United States (US) since the 1930s, though not all states are currently required to report disease cases. Texas, one of the non-reporting states, is an example of where both historical and contemporary scientific evidence define the region as endemic, but we don’t know disease incidence in the state. Mandating coccidioidomycosis as a reportable disease across more US states would increase disease awareness, improve clinical outcomes, and help antifungal drug and vaccine development. It would also increase our understanding of where the disease is endemic and the relationships between environmental conditions and disease cases. This is true for other nations in North and South America that are also likely endemic for coccidioidomycosis, especially Mexico. This commentary advocates for US state and territory epidemiologists to define coccidioidomycosis as a reportable disease and encourages disease surveillance in other endemic regions across North and South America in order to protect human health and reduce disease burden.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A Comparison of Infectious Disease Forecasting Methods across Locations, Diseases, and Time

Accurate infectious disease forecasting can inform efforts to prevent outbreaks and mitigate adverse impacts. This study compares the performance of statistical, machine learning (ML), and deep learning (DL) approaches in forecasting infectious disease incidences across different countries and time intervals. We forecasted three diverse diseases: campylobacteriosis, typhoid, and Q-fever, using a wide variety of features (n = 46) from public datasets, e.g., landscape, climate, and socioeconomic factors. We compared autoregressive statistical models to two tree-based ML models (extreme gradient boosted trees [XGB] and random forest [RF]) and two DL models (multi-layer perceptron and encoder–decoder model). The disease models were trained on data from seven different countries at the region-level between 2009–2017. Forecasting performance of all models was assessed using mean absolute error, root mean square error, and Poisson deviance across Australia, Israel, and the United States for the months of January through August of 2018. The overall model results were compared across diseases as well as various data splits, including country, regions with highest and lowest cases, and the forecasted months out (i.e., nowcasting, short-term, and long-term forecasting). Overall, the XGB models performed the best for all diseases and, in general, tree-based ML models performed the best when looking at data splits. There were a few instances where the statistical or DL models had minutely smaller error metrics for specific subsets of typhoid, which is a disease with very low case counts. Feature importance per disease was measured by using four tree-based ML models (i.e., XGB and RF with and without region name as a feature). The most important feature groups included previous case counts, region name, population counts and density, mortality causes of neonatal to under 5 years of age, sanitation factors, and elevation. This study demonstrates the power of ML approaches to incorporate a wide range of factors to forecast various diseases, regardless of location, more accurately than traditional statistical approaches.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Spaceflight-Induced Changes in Microbial Virulence and the Impact to the Host Immune Response

Many microbial pathogen shave repeatedly exhibited unexpected responses relevant to infectious disease when grown in microgravity and microgravity analogue environments, including changes in final cell concentration, biofilm production, stress resistance, antibiotic sensitivity, gene expression, host-pathogen interactions, and virulence. Notably, the classic foodborne pathogen Salmonella enterica serovar Typhimurium displayed increased virulence in animals when cultured in either the spaceflight analogue or true spaceflight environment. Recently, Serratia marcescens also was shown to increase virulence when cultured in the spaceflight environment. In parallel, astronaut studies have characterized a persistent spaceflight-induced dysregulation of the human immune system at multiple levels, which suggests an increased risk of infectious diseases. Moreover, astronauts have some degree of clinical infectious disease incidence. However, the contribution of the microgravity environment on host-pathogen interactions and potential for clinical disease remains understudied and poorly characterized. The goal of this study is to gain insight into the breadth of other medically significant microbial pathogens that may exhibit altered virulence and pathogenesis-related responses when cultured in space flight analogue conditions. Specifically, we are characterizing the effect of spaceflight analogue culture (Low Shear Modeled Microgravity/LSMMG) on microbial pathogenesis-related stress responses, in vitro host-pathogen interactions, gene expression, and virulence potential in animals using five important model bacterial pathogens, Salmonella enterica Enteritidis, Pseudomonas aeruginosa, Burkholderia cepacia, Streptococcus pneumoniae, and enterohemorrhagic Escherichia coli. Herein, we present data from one of these pathogens, the foodborne bacterium, S. enterica Enteritidis, which is closely related to S. enterica Typhimurium. Phenotypes evaluated included growth profiles, environmental stress responses(acid, oxidative, bile, and thermal stresses),and in vitro colonization of3-D biomimetic cultures of human intestinal tissue containing immune cells. Transcriptomic profiling and virulence studies are ongoing. We show that S. Enteritidis exhibited key alterations in pathogenic responses to LSMMG culture that suggest increased infection risk, including several responses which were different from those observed in the closely related pathovar S. Typhimurium. This information will provide critical mechanistic insight into the potential impact of microgravity on alterations in microbial virulence and associated infectious disease risk to crew health during spaceflight missions.

C M Ott↗
NASA NTRS2024

Spaceflight-Induced Changes in Microbial Virulence and the Impact to the Host Immune Response

Many microbial pathogen shave repeatedly exhibited unexpected responses relevant to infectious disease when grown in microgravity and microgravity analogue environments, including changes in final cell concentration, biofilm production, stress resistance, antibiotic sensitivity, gene expression, host-pathogen interactions, and virulence. Notably, the classic foodborne pathogen Salmonella enterica serovar Typhimurium displayed increased virulence in animals when cultured in either the spaceflight analogue or true spaceflight environment. Recently, Serratia marcescens also was shown to increase virulence when cultured in the spaceflight environment. In parallel, astronaut studies have characterized a persistent spaceflight-induced dysregulation of the human immune system at multiple levels, which suggests an increased risk of infectious diseases. Moreover, astronauts have some degree of clinical infectious disease incidence. However, the contribution of the microgravity environment on host-pathogen interactions and potential for clinical disease remains understudied and poorly characterized. The goal of this study is to gain insight into the breadth of other medically significant microbial pathogens that may exhibit altered virulence and pathogenesis-related responses when cultured in space flight analogue conditions. Specifically, we are characterizing the effect of spaceflight analogue culture (Low Shear Modeled Microgravity/LSMMG) on microbial pathogenesis-related stress responses, in vitro host-pathogen interactions, gene expression, and virulence potential in animals using five important model bacterial pathogens, Salmonella enterica Enteritidis, Pseudomonas aeruginosa, Burkholderia cepacia, Streptococcus pneumoniae, and enterohemorrhagic Escherichia coli. Herein, we present data from one of these pathogens, the foodborne bacterium, S. enterica Enteritidis, which is closely related to S. enterica Typhimurium. Phenotypes evaluated included growth profiles, environmental stress responses(acid, oxidative, bile, and thermal stresses),and in vitro colonization of3-D biomimetic cultures of human intestinal tissue containing immune cells. Transcriptomic profiling and virulence studies are ongoing. We show that S. Enteritidis exhibited key alterations in pathogenic responses to LSMMG culture that suggest increased infection risk, including several responses which were different from those observed in the closely related pathovar S. Typhimurium. This information will provide critical mechanistic insight into the potential impact of microgravity on alterations in microbial virulence and associated infectious disease risk to crew health during spaceflight missions.

C.M. Ott↗
NASA NTRSDate not supplied

Spaceflight-Induced Changes in Microbial Virulence and the Impact to the Host Immune Response

Many microbial pathogens have repeatedly exhibited unexpected responses relevant to infectious disease when grown in microgravity and microgravity analogue environments, including changes in final cell concentration, biofilm production, stress resistance, antibiotic sensitivity, gene expression, host-pathogen interactions, and virulence. Notably, the classic foodborne pathogen Salmonella enterica serovar Typhimurium displayed increased virulence in animals when cultured in either the spaceflight analogue or true spaceflight environment. Recently, Serratia marcescens also was shown to increase virulence when cultured in the spaceflight environment. In parallel, astronaut studies have characterized a persistent spaceflight-induced dysregulation of the human immune system at multiple levels, which suggests an increased risk of infectious diseases. Moreover, astronauts have some degree of clinical infectious disease incidence. However, the contribution of the microgravity environment on host-pathogen interactions and potential for clinical disease remains understudied and poorly characterized. The goal of this study is to gain insight into the breadth of other medically significant microbial pathogens that may exhibit altered virulence and pathogenesis-related responses when cultured in spaceflight analogue conditions. Specifically, we are characterizing the effect of spaceflight analogue culture (Low Shear Modeled Microgravity/LSMMG) on microbial pathogenesis-related stress responses, in vitro host-pathogen interactions, gene expression, and virulence potential in animals using five important model bacterial pathogens, Salmonella enterica Enteritidis, Pseudomonas aeruginosa, Burkholderia cepacia, Streptococcus pneumoniae, and enterohemorrhagic Escherichia coli. The information to date is providing a better understanding into the potential impact of microgravity on alterations in microbial virulence and associated infectious disease risk to crew health during spaceflight missions.

C M Ott↗
DOE OSTI2021

Improving Detection of Disease Re-emergence Using a Web-Based Tool (RED Alert): Design and Case Analysis Study

Background: Currently, the identification of infectious disease re-emergence is performed without describing specific quantitative criteria that can be used to identify re-emergence events consistently. This practice may lead to ineffective mitigation. In addition, identification of factors contributing to local disease re-emergence and assessment of global disease re-emergence require access to data about disease incidence and a large number of factors at the local level for the entire world. This paper presents Re-emerging Disease Alert (RED Alert), a web-based tool designed to help public health officials detect and understand infectious disease re-emergence. Objective: Our objective is to bring together a variety of disease-related data and analytics needed to help public health analysts answer the following 3 primary questions for detecting and understanding disease re-emergence: Is there a potential disease re-emergence at the local (country) level? What are the potential contributing factors for this re-emergence? Is there a potential for global re-emergence? Methods: We collected and cleaned disease-related data (eg, case counts, vaccination rates, and indicators related to disease transmission) from several data sources including the World Health Organization (WHO), Pan American Health Organization (PAHO), World Bank, and Gideon. We combined these data with machine learning and visual analytics into a tool called RED Alert to detect re-emergence for the following 4 diseases: measles, cholera, dengue, and yellow fever. We evaluated the performance of the machine learning models for re-emergence detection and reviewed the output of the tool through a number of case studies. Results: Our supervised learning models were able to identify 82%-90% of the local re-emergence events, although with 18%-31% (except 46% for dengue) false positives. This is consistent with our goal of identifying all possible re-emergences while allowing some false positives. The review of the web-based tool through case studies showed that local re-emergence detection was possible and that the tool provided actionable information about potential factors contributing to the local disease re-emergence and trends in global disease re-emergence. Conclusions: To the best of our knowledge, this is the first tool that focuses specifically on disease re-emergence and addresses the important challenges mentioned above.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Quantitative Resistance Loci to Southern Rust Mapped in a Temperate Maize Diversity Panel

Southern rust is a severe foliar disease of maize (Zea mays) resulting from infection with the obligate biotrophic fungus Puccinia polysora. This disease reduces photosynthetic productivity, which in turn reduces yields, with the greatest yield losses (up to 50%) associated with earlier onset infections. P. polysora urediniospores overwinter only in tropical and subtropical regions but cause outbreaks when environmental conditions favor initial infection. Increased temperatures and humidity during the growing season combined with an increased frequency of moderate winters are likely to increase the frequency of severe southern rust outbreaks in the U.S. Corn Belt. In summer 2020, a severe outbreak of southern rust was observed in eastern Nebraska, United States. We scored a replicated maize association panel planted in Lincoln, NE for disease severity and found that disease incidence and severity showed significant variation among maize genotypes. Genome-wide association studies identified four loci associated with significant quantitative variation in disease severity. These loci were associated with candidate genes with plausible links to quantitative disease resistance. A transcriptome-wide association study identified additional genes associated with disease severity. Together, these results indicate that substantial diversity in resistance to southern rust exists among current temperate-adapted maize germplasm, including several candidate loci that may explain the observed variation in resistance to southern rust. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

Plant Sciences↗
DOE OSTI2025

Evaluation of the 2022 West Nile virus forecasting challenge, USA

Abstract Background West Nile virus (WNV) is the most common cause of mosquito-borne disease in the continental USA, with an average of ~1200 severe, neuroinvasive cases reported annually from 2005 to 2021 (range 386–2873). Despite this burden, efforts to forecast WNV disease to inform public health measures to reduce disease incidence have had limited success. Here, we analyze forecasts submitted to the 2022 WNV Forecasting Challenge, a follow-up to the 2020 WNV Forecasting Challenge. Methods Forecasting teams submitted probabilistic forecasts of annual West Nile virus neuroinvasive disease (WNND) cases for each county in the continental USA for the 2022 WNV season. We assessed the skill of team-specific forecasts, baseline forecasts, and an ensemble created from team-specific forecasts. We then characterized the impact of model characteristics and county-specific contextual factors (e.g., population) on forecast skill. Results Ensemble forecasts for 2022 anticipated a season at or below median long-term WNND incidence for nearly all (> 99%) counties. More counties reported higher case numbers than anticipated by the ensemble forecast median, but national caseload (826) was well below the 10-year median (1386). Forecast skill was highest for the ensemble forecast, though the historical negative binomial baseline model and several team-submitted forecasts had similar forecast skill. Forecasts utilizing regression-based frameworks tended to have more skill than those that did not and models using climate, mosquito surveillance, demographic, or avian data had less skill than those that did not, potentially due to overfitting. County-contextual analysis showed strong relationships with the number of years that WNND had been reported and permutation entropy (historical variability). Evaluations based on weighted interval score and logarithmic scoring metrics produced similar results. Conclusions The relative success of the ensemble forecast, the best forecast for 2022, suggests potential gains in community ability to forecast WNV, an improvement from the 2020 Challenge. Similar to the previous challenge, however, our results indicate that skill was still limited with general underprediction despite a relative low incidence year. Potential opportunities for improvement include refining mechanistic approaches, integrating additional data sources, and considering different approaches for areas with and without previous cases. Graphical Abstract

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

A modified Susceptible-Infected-Recovered model for observed under-reported incidence data

Fitting Susceptible-Infected-Recovered (SIR) models to incidence data is problematic when not all infected individuals are reported. Assuming an underlying SIR model with general but known distribution for the time to recovery, this paper derives the implied differential-integral equations for observed incidence data when a fixed fraction of newly infected individuals are not observed. The parameters of the resulting system of differential equations are identifiable. Using these differential equations, we develop a stochastic model for the conditional distribution of current disease incidence given the entire past history of reported cases. We estimate the model parameters using Bayesian Markov Chain Monte-Carlo sampling of the posterior distribution. We use our model to estimate the transmission rate and fraction of asymptomatic individuals for the current Coronavirus 2019 outbreak in eight American Countries: the United States of America, Brazil, Mexico, Argentina, Chile, Colombia, Peru, and Panama, from January 2020 to May 2021. Our analysis reveals that the fraction of reported cases varies across all countries. For example, the reported incidence fraction for the United States of America varies from 0.3 to 0.6, while for Brazil it varies from 0.2 to 0.4.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Quantification of early nonpharmaceutical interventions aimed at slowing transmission of Coronavirus Disease 2019 in the Navajo Nation and surrounding states (Arizona, Colorado, New Mexico, and Utah)

During an early period of the Coronavirus Disease 2019 (COVID-19) pandemic, the Navajo Nation, much like New York City, experienced a relatively high rate of disease transmission. Yet, between January and October 2020, it experienced only a single period of growth in new COVID-19 cases, which ended when cases peaked in May 2020. The daily number of new cases slowly decayed in the summer of 2020 until late September 2020. In contrast, the surrounding states of Arizona, Colorado, New Mexico, and Utah all experienced at least two periods of growth in the same time frame, with second surges beginning in late May to early June. Here, we investigated these differences in disease transmission dynamics with the objective of quantifying the contributions of non-pharmaceutical interventions (NPIs) (e.g., behaviors that limit disease transmission). We considered a compartmental model accounting for distinct periods of NPIs to analyze the epidemic in each of the five regions. We used Bayesian inference to estimate region-specific model parameters from regional surveillance data (daily reports of new COVID-19 cases) and to quantify uncertainty in parameter estimates and model predictions. Our results suggest that NPIs in the Navajo Nation were sustained over the period of interest, whereas in the surrounding states, NPIs were relaxed, which allowed for subsequent surges in cases. Our region-specific model parameterizations allow us to quantify the impacts of NPIs on disease incidence in the regions of interest.

60 APPLIED LIFE SCIENCES↗