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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Community vulnerability is the key determinant of diverse energy burdens in the United States

Low-income households generally experience a high energy burden; however, the factors influencing energy burdens are beyond socio-economics. This study explores the relationships between the multidimensionality of community vulnerability factors and energy burden across multiple geospatial levels in the United States. Our study found the distribution of energy burden in 2020 showed a great deal of variety, ranging from a minimum of 2.93 % to a maximum of 30.45 % across 3142 counties. The results of non-spatial and spatial regressions showed that the vulnerability ranks of socioeconomic, household composition and disability, minority and language, household type and transportation, and COVID mortality rate are significant predictors of energy burdens at the national level. However, at the regional level, only socioeconomic, minority and language significantly influence energy burdens. Minority and language negatively impact energy burdens except for the South East-Central region. Additionally, our analyses highlight the need to consider community vulnerability indicators' spatial homogeneity and heterogeneity. At the national level, only the epidemiological factors index is a spatially homogeneous predictor; on the regional and state level, the spatially homogeneous predictors such as socioeconomic status, household composition and disability, and household type and transportation vary by region. Such a region-sensitive relationship between energy burden and the predictors indicates spatial heterogeneity. Here this study suggests policy recommendations through the lens of the multidimensionality of community vulnerability factors. Implementing flexible national energy policies while making particular energy assistance policies for the vulnerable population at the regional or state levels is essential.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Particle emissions from mobile sources: Discussion of ultrafine particle emissions and definition

There is no universally agreed upon definition for ultrafine particles (UFP). Commonly used definitions for UFP are either particle number below 100 nm or total particle number, but without an agreed upon lower cut point. For example, a lower cut point of 3 nm compared to 10 nm could result in a substantially higher count. Another definition for UFP is total particle mass but without a commonly agreed upon aerodynamic diameter upper cut point, e.g., below 100 nm, 200 nm, 300 nm, etc. Yet another definition is lung deposited surface area weighted by lung deposition fraction, found mainly in the particle mobility diameter range from 20 to 400 nm. It is clear from these definitions that there are inconsistencies in the way UFP is used and defined in the literature. Sometimes these metrics are well correlated, sometimes not. In this paper we suggest three exposure metrics: UFP-N, UFP-M, and UFP-S, that we believe will add clarity. These metrics represent total number, mass, and surface area below 500 nm, respectively. For surface area and mass, the 500 nm cut point can be either aerodynamic or mobility diameter depending upon measurement methodology. For all metrics, this cut point captures nearly all of the primary particle emissions from mobile sources. Furthermore, UFP-N would include a lower cut point of 3–6 nm and would not require an upper size cut point because there is very little particle number above 500 nm or even above 100 nm. Thus, our definition of UFP-N is consistent with the current definition of ultrafine number except for, importantly, the specification of a lower cut point. These exposure metrics can help facilitate consistency in the characterization of both short- and long-term UFP ambient exposures and associated health effects in epidemiological studies.

42 ENGINEERING↗
DOE OSTI2023

Deep learning uncertainty quantification for clinical text classification

Machine learning algorithms are expected to work side-by-side with humans in decision-making pipelines. Thus, the ability of classifiers to make reliable decisions is of paramount importance. Deep neural networks (DNNs) represent the state-of-the-art models to address real-world classification. Although the strength of activation in DNNs is often correlated with the network’s confidence, in-depth analyses are needed to establish whether they are well calibrated. In this paper, we demonstrate the use of DNN-based classification tools to benefit cancer registries by automating information extraction of disease at diagnosis and at surgery from electronic text pathology reports from the US National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) population-based cancer registries. In particular, we introduce multiple methods for selective classification to achieve a target level of accuracy on multiple classification tasks while minimizing the rejection amount—that is, the number of electronic pathology reports for which the model’s predictions are unreliable. We evaluate the proposed methods by comparing our approach with the current in-house deep learning-based abstaining classifier. Overall, all the proposed selective classification methods effectively allow for achieving the targeted level of accuracy or higher in a trade-off analysis aimed to minimize the rejection rate. On in-distribution validation and holdout test data, with all the proposed methods, we achieve on all tasks the required target level of accuracy with a lower rejection rate than the deep abstaining classifier (DAC). Interpreting the results for the out-of-distribution test data is more complex; nevertheless, in this case as well, the rejection rate from the best among the proposed methods achieving 97% accuracy or higher is lower than the rejection rate based on the DAC. We show that although both approaches can flag those samples that should be manually reviewed and labeled by human annotators, the newly proposed methods retain a larger fraction and do so without retraining—thus offering a reduced computational cost compared with the in-house deep learning-based abstaining classifier.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Individual doses for super cohort members exposed to atmospheric radioiodine from the Mayak releases with an emphasis on prenatal doses

Time-dependent thyroid doses were reconstructed for 45,837 members of the Southern Urals Population Exposed to Radiation Cohort (SUPER-C) living in the region around the Mayak Production Association facilities in Russia from 131I released to the atmosphere from all relevant exposure pathways. The dose calculations are implemented in a Monte Carlo framework that produces best estimates and stochastic realizations of dose time-histories. The mean thyroid dose from 131I for SUPER-C members was 195 mGy; the median was 65 mGy. Overall, 131I-thyroid doses for about 3.6% of SUPER-C members were larger than 1 Gy. For children born in 1940-1950, the dose was about 10% higher than in previous studies because doses during the prenatal period for 9,117 individuals are included in the current work. Half of the individuals born in the region in 1950–1960 who remained in the study domain through 1972 received 9.4% or more of their total dose during the prenatal period. SUPER-C members residing in areas contaminated by discharges of liquid radioactive releases into the Techa River and the Kyshtym Accident in 1957 received 80% of their thyroid dose from airborne 131I emissions. The uncertainty in the 131I dose estimates is low enough for this approach to be used in regional epidemiological studies.

Dose assessment, 131I, airborne radioactivity, thy↗
DOE OSTI2021

Development of a rapid viability RT-PCR (RV-RT-PCR) method to detect infectious SARS-CoV-2 from swabs

Since the rapid onset of the COVID-19 pandemic, its causative virus, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), continues to spread and increase the number of fatalities. To expedite studies on understanding potential surface transmission of the virus and to aid environmental epidemiological investigations, here we developed a rapid viability reverse transcriptase PCR (RV-RT-PCR) method that detects viable (infectious) SARS-CoV-2 from swab samples in <1 day compared to several days required by current gold-standard cell-culture-based methods. The method integrates cell-culture-based viral enrichment in a 96-well plate format with gene-specific RT-PCR-based analysis before and after sample incubation to determine the cycle threshold (C T ) difference (ΔC T ). An algorithm based on ΔC T ≥ 6 representing ~ 2-log or more increase in SARS-CoV-2 RNA following enrichment determines the presence of infectious virus. The RV-RT-PCR method with 2-hr viral infection and 9-hr post-infection incubation periods includes ultrafiltration to concentrate virions, resulting in detection of <50 SARS-CoV-2 virions in swab samples in 17 h (for a batch of 12 swabs), compared to days typically required by the cell-culture-based method. The SARS-CoV-2 RV-RT-PCR method may also be useful in clinical sample analysis and antiviral drug testing, and could serve as a model for developing rapid methods for other viruses of concern.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Bayesian learning with Gaussian processes for low-dimensional representations of time-dependent nonlinear systems

This work presents a data-driven method for learning low-dimensional time-dependent physics-based surrogate models whose predictions are endowed with uncertainty estimates. We use the operator inference approach to model reduction that poses the problem of learning low-dimensional model terms as a regression of state space data and corresponding time derivatives by minimizing the residual of reduced system equations. Standard operator inference models perform well with accurate training data that are dense in time, but producing stable and accurate models when the state data are noisy and/or sparse in time remains a challenge. Another challenge is the lack of uncertainty estimation for the predictions from the operator inference models. Our approach addresses these challenges by incorporating Gaussian process surrogates into the operator inference framework to (1) probabilistically describe uncertainties in the state predictions and (2) procure analytical time derivative estimates with quantified uncertainties. The formulation leads to a generalized least-squares regression and, ultimately, reduced-order models that are described probabilistically with a closed-form expression for the posterior distribution of the operators. The resulting probabilistic surrogate model propagates uncertainties from the observed state data to reduced-order predictions. Furthermore, we demonstrate the method is effective for constructing low-dimensional models of two nonlinear partial differential equations representing a compressible flow and a nonlinear diffusion–reaction process, as well as for estimating the parameters of a low-dimensional system of nonlinear ordinary differential equations representing compartmental models in epidemiology.

Data-driven model reduction↗
DOE OSTI2020

Assessing outdoor air quality and public health impact attributable to residential black carbon emissions in rural China

Black carbon (BC) is a significant component of particulate matter (PM) that relates to air pollution, climate forcing, and further implications for public health. BC is predominantly released from the combustion of solid fuels. Combustion of low-quality fuels in rural China may induce severe respiratory and cardiopulmonary health outcomes for residents, which have however been inadequately assessed. One major reason for the limited understanding is the lack of a high-resolution inventory. An improved method of estimating the BC-associated public health burden is needed. This work quantified premature mortalities due to residential BC emissions in rural China. Domestic BC emissions at 1×1 km resolution were compiled based on previous field investigation, which were further configured for air quality simulation. A chemistry transport model, WRF–CMAQ v5.2, was employed for simulating BC concentrations. The consequent premature mortalities were quantified by a BC-specific concentration-response function (CRF) derived from an epidemiological study. Results show that residential combustion of solid fuel in rural China emitted 648.0 Gg (95%CI: 361.0–965.9) BC in 2014, after dispersion, accounting for 51.8% of annual mean ground-level BC concentration in China. Such impact was most severe in North and Northeast China, and the Sichuan Basin. The further investigation estimated 171,000 (95%CI: 69,000–387,000) premature mortalities that were attributable to exposure to rural residential BC. These findings reveal the major contribution of rural residential BC emissions to air pollution formation and public health impacts. Our findings are anticipated to provide useful information for enacting the next-stage environmental strategy for the residential sector in China.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2022

Long-term trends in urban NO 2 concentrations and associated paediatric asthma incidence: estimates from global datasets

Combustion-related nitrogen dioxide (NO 2 ) air pollution is associated with paediatric asthma incidence. We aimed to estimate global surface NO 2 concentrations consistent with the Global Burden of Disease study for 1990-2019 at a 1 km resolution, and the concentrations and attributable paediatric asthma incidence trends in 13 189 cities from 2000 to 2019. We scaled an existing annual average NO 2 concentration dataset for 2010-12 from a land use regression model (based on 5220 NO 2 monitors in 58 countries and land use variables) to other years using NO 2 column densities from satellite and reanalysis datasets. We applied these concentrations in an epidemiologically derived concentration-response function with population and baseline asthma rates to estimate NO 2 -attributable paediatric asthma incidence. We estimated that 1.85 million (95% uncertainty interval [UI] 0.93-2.80 million) new paediatric asthma cases were attributable to NO 2 globally in 2019, two thirds of which occurred in urban areas (1.22 million cases; 95% UI 0.60-1.8 million). The proportion of paediatric asthma incidence that is attributable to NO 2 in urban areas declined from 19.8% (1.22 million attributable cases of 6.14 million total cases) in 2000 to 16.0% (1.24 million attributable cases of 7.73 million total cases) in 2019. Urban attributable fractions dropped in high-income countries (-41%), Latin America and the Caribbean (-16%), central Europe, eastern Europe, and central Asia (-13%), and southeast Asia, east Asia, and Oceania (-6%), and rose in south Asia (+23%), sub-Saharan Africa (+11%), and north Africa and the Middle East (+5%). The contribution of NO 2 concentrations, paediatric population size, and asthma incidence rates to the change in NO 2 -attributable paediatric asthma incidence differed regionally. Despite improvements in some regions, combustion-related NO 2 pollution continues to be an important contributor to paediatric asthma incidence globally, particularly in cities. Mitigating air pollution should be a crucial element of public health strategies for children.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Role of climate goals and clean-air policies on reducing future air pollution deaths in China: a modelling study

Over 3 million people still die every year from diseases caused by exposure to outdoor PM 2.5 air pollution, and more than a quarter of these premature deaths occur in China. In addition to clean air policies that target pollution emissions, climate policies aimed at reducing fossil-fuel CO 2 emissions (e.g., to avoid 1.5°C of warming) may also dramatically improve air quality and public health. Yet there has been no comprehensive accounting of public health outcomes under different energy pathways and local clean air management decisions in China. In particular, further research is needed to understand the relationships among climate and clean air polices and future health burdens in China, where an aging population will further exacerbate the impacts of air pollution. Using a China-focused integrated assessment model (GCAM-China) and a dynamic emission projection model (DPEC), we project future Chinese air quality in scenarios spanning a range of global climate targets (i.e. 1.5°0C, 2°C, NDC, unambitious, baseline, and 4.5°C) as well as national clean air actions (i.e. 2015-pollution, current-pollution, and ambitious-pollution). We then evaluate the health impacts of PM2·5 air pollution of scenario matrix using the chemical transport model WRF-CMAQ and the latest epidemiological concentration–response (C-R) functions (i.e. GBD2019). We find that, without ambitious climate mitigation (e.g., under current NDC pledges), Chinese deaths related the PM2.5 air pollution do not substantially decrease—and often grow—by mid-century, regardless of clean air policies and air quality improvements. For example, in scenarios that track China’s current NDC pledge and deploy best-available pollution control technologies, PM 2.5 -related deaths in China decrease slightly by 2030 (to 1.2 million per year) but no further by mid-century (Ambitious-pollution-NDC-goals; 1.2 million deaths in 2050) despite substantial and continuous improvements in population-weighted air quality (27.2 to 16.0 µg/m 3 from 2030 to 2050). The contrary trends of improving air quality and increasing PM 2·5 -related deaths in many of our scenarios reveals the extent to which extra efforts are needed to compensate for the established fact of increasing age of China’s population in future. Substantial decreases in China’s PM 2.5 -related deaths and age-standardized death rates (e.g., decreasing by 0.3–0.5 million deaths and 10.2–14.2 per 100,000 population age-standardized death rates per year) thus require the sort of large-scale transition in energy sources entailed by scenarios that meet international climate goals to avoid 1.5°C and 2°C of warming (Ambitious-pollution-2°C- and 1.5°C-goals).

58 GEOSCIENCES↗
DOE OSTI2020

SARS-CoV-2 RNA in Wastewater Settled Solids Is Associated with COVID-19 Cases in a Large Urban Sewershed

Wastewater-based epidemiology may be useful for informing public health response to viral diseases like COVID-19 caused by SARS-CoV-2. Here, we quantified SARS-CoV-2 RNA in wastewater influent and primary settled solids in two wastewater treatment plants to inform the preanalytical and analytical approaches and to assess whether influent or solids harbored more viral targets. The primary settled solids samples resulted in higher SARS-CoV-2 detection frequencies than the corresponding influent samples. Likewise, SARS-CoV-2 RNA was more readily detected in solids using one-step digital droplet (dd)RT-PCR than with two-step RT-QPCR and two-step ddRT-PCR, likely owing to reduced inhibition with the one-step ddRT-PCR assay. We subsequently analyzed a longitudinal time series of 89 settled solids samples from a single plant for SARS-CoV-2 RNA as well as coronavirus recovery (bovine coronavirus) and fecal strength (pepper mild mottle virus) controls. SARS-CoV-2 RNA targets N1 and N2 concentrations correlated positively and significantly with COVID-19 clinically confirmed case counts in the sewershed. Together, the results demonstrate that measuring SARS-CoV-2 RNA concentrations in settled solids may be a more sensitive approach than measuring SARS-CoV-2 in influent.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Using Convex Optimization to Efficiently Apportion Tracer and Pollutant Sources From Point Concentration Observations

Abstract Rivers transport elements, minerals, chemicals, and pollutants produced in their upstream basins. A sample from a river is a mixture of all of its upstream sources, making it challenging to pinpoint the contribution from each individual source. Here, we show how a nested sample design and convex optimization can be used to efficiently unmix downstream samples of a well‐mixed, conservative tracer in a steady state system into the contributions of their upstream sources. Our approach is significantly faster than previous methods. We represent the river's sub‐catchments, defined by sampling sites, using a directed acyclic graph. This graph is used to build a convex optimization problem which, thanks to its convexity, can be quickly solved to global optimality—in under a second on desktop hardware for data sets of ∼100 samples or fewer. Uncertainties in the upstream predictions can be generated using Monte Carlo resampling. We provide an open‐source implementation of this approach in Python. The inputs required are straightforward: a table containing sample locations and observed tracer concentrations, along with a D8 flow‐direction raster map. As a case study, we use this method to map the elemental geochemistry of sediment sources for rivers draining the Cairngorms mountains, UK. This method could be extended to non‐conservative and non‐steady state tracers. We also show, theoretically, how multiple tracers could be simultaneously inverted to recover upstream run‐off or erosion rates as well as source concentrations. Overall, this approach can provide valuable insights to researchers in various fields, including water quality, geochemical exploration, geochemistry, hydrology, and wastewater epidemiology.

Barnes, Richard↗
DOE OSTI2026

Developing Scenario‐Based Strategies for Health, Climate, and Environmental Preparedness: The One Health, One Earth Approach

Climate change amplifies many threats to human health. Despite advances in understanding climate change dynamics and impacts, there remains a critical gap in translating scientific knowledge into equitable, and community-driven health interventions. The inaugural One Earth, One Health workshop sought to explore this gap through human-centered design exercises involving interdisciplinary researchers from climate and Earth sciences, engineering, epidemiology, microbiology, and environmental health. Although participants did not co-develop solutions with affected communities, they used stakeholder role-playing to guide ideation and lay groundwork for actionable plans. Through these methods, participants identified community needs and proposed prototype solutions to alleviate health threats exacerbated by global environmental change. Prototypes were organized around infectious diseases, extreme weather, and air quality, as illustrative themes rather than an exhaustive set of risks. Key solutions included strategies for anticipatory systems and early warning (e.g., integrating environmental signals with health data), inclusive communication and infrastructure needs for responding to extreme weather events, and integrated platforms visualizing air quality trends to support tailored, context-aware guidance beyond one-size-fits-all alerts. The workshop highlighted opportunities such as leveraging machine learning, Earth observation, and real-time surveillance to protect communities, but also noted barriers including data quality, technological redundancy, privacy, and governance challenges. Additionally, participants emphasized the need for interdisciplinary teams capable of collaborating across sectors, breaking down silos and addressing gaps in training and education. Overall, the workshop illustrates how process-driven, human-centered approaches can help surface user needs and generate testable prototype concepts, while underscoring the importance of direct community partnership for implementation.

Abadi, Azar M. [University of Alabama, Birmingham,↗
DOE OSTI2021

Forecasting influenza activity using machine-learned mobility map

Human mobility is a primary driver of infectious disease spread. However, existing data is limited in availability, coverage, granularity, and timeliness. Data-driven forecasts of disease dynamics are crucial for decision-making by health officials and private citizens alike. In this work, we focus on a machine-learned anonymized mobility map (hereon referred to as AMM) aggregated over hundreds of millions of smartphones and evaluate its utility in forecasting epidemics. We factor AMM into a metapopulation model to retrospectively forecast influenza in the USA and Australia. We show that the AMM model performs on-par with those based on commuter surveys, which are sparsely available and expensive. We also compare it with gravity and radiation based models of mobility, and find that the radiation model’s performance is quite similar to AMM and commuter flows. Additionally, we demonstrate our model’s ability to predict disease spread even across state boundaries. Our work contributes towards developing timely infectious disease forecasting at a global scale using human mobility datasets expanding their applications in the area of infectious disease epidemiology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Model-based evaluation of school- and non-school-related measures to control the COVID-19 pandemic

The role of school-based contacts in the epidemiology of SARS-CoV-2 is incompletely understood. We use an age-structured transmission model fitted to age-specific seroprevalence and hospital admission data to assess the effects of school-based measures at different time points during the COVID-19 pandemic in the Netherlands. Our analyses suggest that the impact of measures reducing school-based contacts depends on the remaining opportunities to reduce non-school-based contacts. If opportunities to reduce the effective reproduction number (R e ) with non-school-based measures are exhausted or undesired and R e is still close to 1, the additional benefit of school-based measures may be considerable, particularly among older school children. As two examples, we demonstrate that keeping schools closed after the summer holidays in 2020, in the absence of other measures, would not have prevented the second pandemic wave in autumn 2020 but closing schools in November 2020 could have reduced R e below 1, with unchanged non-school-based contacts.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Trade-offs between individual and ensemble forecasts of an emerging infectious disease

Probabilistic forecasts play an indispensable role in answering questions about the spread of newly emerged pathogens. However, uncertainties about the epidemiology of emerging pathogens can make it difficult to choose among alternative model structures and assumptions. To assess the potential for uncertainties about emerging pathogens to affect forecasts of their spread, we evaluated the performance 16 forecasting models in the context of the 2015-2016 Zika epidemic in Colombia. Each model featured a different combination of assumptions about human mobility, spatiotemporal variation in transmission potential, and the number of virus introductions. We found that which model assumptions had the most ensemble weight changed through time. We additionally identified a trade-off whereby some individual models outperformed ensemble models early in the epidemic, but on average the ensembles outperformed all individual models. Our results suggest that multiple models spanning uncertainty across alternative assumptions are necessary to obtain robust forecasts for emerging infectious diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Testing at scale during the COVID-19 pandemic

Assembly and publication of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genome in January 2020 enabled the immediate development of tests to detect the new virus. This began the largest global testing programme in history, in which hundreds of millions of individuals have been tested to date. The unprecedented scale of testing has driven innovation in the strategies, technologies and concepts that govern testing in public health. In this Review, we describe the changing role of testing during the COVID-19 pandemic, including the use of genomic surveillance to track SARS-CoV-2 transmission around the world, the use of contact tracing to contain disease outbreaks and testing for the presence of the virus circulating in the environment. Despite these efforts, widespread community transmission has become entrenched in many countries and has required the testing of populations to identify and isolate infected individuals, many of whom are asymptomatic. The diagnostic and epidemiological principles that underpin such population-scale testing are also considered, as are the high-throughput and point-of-care technologies that make testing feasible on a massive scale.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

AeDES: a next-generation monitoring and forecasting system for environmental suitability of Aedes-borne disease transmission

Aedes-borne diseases, such as dengue and chikungunya, are responsible for more than 50 million infections worldwide every year, with an overall increase of 30-fold in the last 50 years, mainly due to city population growth, more frequent travels and ecological changes. In the United States of America, the vast majority of Aedes-borne infections are imported from endemic regions by travelers, who can become new sources of mosquito infection upon their return home if the exposed population is susceptible to the disease, and if suitable environmental conditions for the mosquitoes and the virus are present. Since the susceptibility of the human population can be determined via periodic monitoring campaigns, the environmental suitability for the presence of mosquitoes and viruses becomes one of the most important pieces of information for decision makers in the health sector. We present a next-generation monitoring and forecasting system for Aedes-borne diseases’ environmental suitability (AeDES) of transmission in the conterminous United States and transboundary regions, using calibrated ento-epidemiological models, climate models and temperature observations. After analyzing the seasonal predictive skill of AeDES, we briefly consider the recent Zika epidemic, and the compound effects of the current Central American dengue outbreak happening during the SARS-CoV-2 pandemic, to illustrate how a combination of tailored deterministic and probabilistic forecasts can inform key prevention and control strategies .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Evaluating efficacy of indoor non-pharmaceutical interventions against COVID-19 outbreaks with a coupled spatial-SIR agent-based simulation framework

Contagious respiratory diseases, such as COVID-19, depend on sufficiently prolonged exposures for the successful transmission of the underlying pathogen. It is important that organizations evaluate the efficacy of non-pharmaceutical interventions aimed at mitigating viral transmission among their personnel. We have developed a operational risk assessment simulation framework that couples a spatial agent-based model of movement with an agent-based SIR model to assess the relative risks of different intervention strategies. By applying our model on MIT’s Stata center, we assess the impacts of three possible dimensions of intervention: one-way vs unrestricted movement, population size allowed onsite, and frequency of leaving designated work location for breaks. We find that there is no significant impact made by one-way movement restrictions over unrestricted movement. Instead, we find that reducing the frequency at which individuals leave their workstations combined with lowering the number of individuals admitted below the current recommendations lowers the likelihood of highly connected individuals within the contact networks that emerge, which in turn lowers the overall risk of infection. We discover three classes of possible interventions based on their epidemiological effects. By assuming a direct relationship between data on secondary attack rates and transmissibility in the agent-based SIR model, we compare relative infection risk of four respiratory illnesses, MERS, SARS, COVID-19, and Measles, within the simulated area, and recommend appropriate intervention guidelines.

60 APPLIED LIFE SCIENCES↗