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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2021

An Innovative Approach to Modeling VIPER Rover Software Life Cycle Cost

NASA’s “Volatiles Investigating Polar Exploration Rover” (VIPER) will be the first robotic mission to prospect for water ice near the south pole of the Moon in late 2023 on a 100-Earth-day mission. The information that the VIPER rover provides will help improve understanding of the composition, distribution, and accessibility of Lunar polar volatiles and will help determine how the Moon’s resources can support future human space exploration. VIPER, however, represents a radical departure from the way that NASA has traditionally developed planetary robotic missions. A key consequence of these differences is that estimating the cost of VIPER’s rover software is challenging and complex.For example, VIPER is being developed using management procedures typically applied to NASA research and technology projects, rather than space flight programs. In addition, key portions of the rover’s software are being designed as ground software to run on mission control computers (rather than on-board the rover as flight software as with prior planetary missions) taking advantage of continuous, interactive data communications between the Moon and Earth and higher performance computing available on the ground. Moreover, the rover’s software is being engineered using Agile software development practices and incorporates a significant amount of open-source, rather than following traditional (spiral, waterfall, etc.) development methods and in-house code. In this paper, we present an innovative process to estimate the life cycle cost of VIPER’s rover software. We first describe how we modeled the architecture and code counts for three software elements: Rover Flight Software (RFSW), Rover Ground Software (RGSW), and Rover Simulation Software (RSIM). We then discuss key challenges and unique aspects of our approach, such as the lack of Lunar rover analogies, the need to integrate and test large open source software, and the strategies developed to account for use of non-space flight management practices and the impact of the COVID-19 pandemic. We conclude with a summary of our results, including cumulative distribution, nearest neighbors and cluster analysis, as well as heuristics used to confirm the reasonableness of the cost estimate.

Utz, Hans↗
NASA NTRS2021

Tonlé Sap Food Security and Agriculture II: Evaluating Changes in Ecosystem Vitality and Freshwater Healthin the Tonlé Sap Basin using Remotely Sensed Data

The Tonlé Sap Lake and river basin in central Cambodia provide critical ecosystem services to the region, including fisheries, agricultural irrigation, hydropower, and biodiverse habitats. Deforestation, increased pumping for farming, and effects of climate change such as droughts and forest fires threaten the health of the lake and food security in the region. This project built upon the previous term through a partnership with Conservation International (CI), the Cambodian Ministry of Water Resources and Meteorology, and the Tonlé Sap Authority to assess ecosystem vitality and implement CI’s Freshwater Health Index (FHI) tool, in an effort to prioritize resource expenditure and highlight areas of concern. Due to the COVID-19 pandemic and related travel restrictions, partners had not been able to readily collect in situ data for the past year, which make up the majority of FHI inputs. To help fill this data gap, we developed a methodology for using Gravity Recovery and Climate Experiment (GRACE) satellite data to calculate groundwater storage depletion, and a Python Application Programming Interface for processing and formatting remotely-sensed data for the Soil and Water Assessment Tool (SWAT) model. We then used SWAT to model nutrient flows and of phosphorous, nitrogen and suspended sediments amounts in the basin from October 2000 to December 2020. These outputs served as inputs for the FHI and provided policy makers with robust monitoring information to aid decision-making in the area and safeguard the lake’s vital fisheries and biodiversity.

Justine Spore↗
DOE OSTI2022

SNAPSHOT USA 2020: A second coordinated national camera trap survey of the United States during the COVID-19 pandemic

Managing wildlife populations in the face of global change requires regular data on the abundance and distribution of wild animals, but acquiring these over appropriate spatial scales in a sustainable way has proven challenging. Here, in this study, we present the data from Snapshot USA 2020, a second annual national mammal survey of the USA. This project involved 152 scientists setting camera traps in a standardized protocol at 1485 locations across 103 arrays in 43 states for a total of 52,710 trap-nights of survey effort. Most (58) of these arrays were also sampled during the same months (September and October) in 2019, providing a direct comparison of animal populations in 2 years that includes data from both during and before the COVID-19 pandemic. All data were managed by the eMammal system, with all species identifications checked by at least two reviewers. In total, we recorded 117,415 detections of 78 species of wild mammals, 9236 detections of at least 43 species of birds, 15,851 detections of six domestic animals and 23,825 detections of humans or their vehicles. Spatial differences across arrays explained more variation in the relative abundance than temporal variation across years for all 38 species modeled, although there are examples of significant site-level differences among years for many species. Temporal results show how species allocate their time and can be used to study species interactions, including between humans and wildlife. These data provide a snapshot of the mammal community of the USA for 2020 and will be useful for exploring the drivers of spatial and temporal changes in relative abundance and distribution, and the impacts of species interactions on daily activity patterns. There are no copyright restrictions, and please cite this paper when using these data, or a subset of these data, for publication.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021

During the COVID-19 pandemic there has been a strong interest in forecasts of the short-term development of epidemiological indicators to inform decision makers. In this study we evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland for the period from January through April 2021. We evaluate probabilistic real-time predictions of confirmed cases and deaths from COVID-19 in Germany and Poland. These were issued by 15 different forecasting models, run by independent research teams. Moreover, we study the performance of combined ensemble forecasts. Evaluation of probabilistic forecasts is based on proper scoring rules, along with interval coverage proportions to assess calibration. The presented work is part of a pre-registered evaluation study. We find that many, though not all, models outperform a simple baseline model up to four weeks ahead for the considered targets. Ensemble methods show very good relative performance. The addressed time period is characterized by rather stable non-pharmaceutical interventions in both countries, making short-term predictions more straightforward than in previous periods. However, major trend changes in reported cases, like the rebound in cases due to the rise of the B.1.1.7 (Alpha) variant in March 2021, prove challenging to predict. Multi-model approaches can help to improve the performance of epidemiological forecasts. However, while death numbers can be predicted with some success based on current case and hospitalization data, predictability of case numbers remains low beyond quite short time horizons. Additional data sources including sequencing and mobility data, which were not extensively used in the present study, may help to improve performance.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Impact of changes in protective behaviors and out-of-household activities by age on COVID-19 transmission and hospitalization in Chicago, Illinois

Even with an efficacious vaccine, protective behaviors (social distancing, masking) are essential for preventing COVID-19 transmission and could become even more important if current or future variants evade immunity from vaccines or prior infection. Here we created an agent-based model representing the Chicago population and conducted experiments to determine the effects of varying adult out-of-household activities (OOHA), school reopening, and protective behaviors across age groups on COVID-19 transmission and hospitalizations. From September-November 2020, decreasing adult protective behaviors and increasing adult OOHA both substantially impacted COVID-19 outcomes; school reopening had relatively little impact when adult protective behaviors and OOHA were maintained. As of November 1, 2020, a 50% reduction in young adult (age 18-40) protective behaviors resulted in increased latent infection prevalence per 100,000 from 15.93 (IQR 6.18, 36.23) to 40.06 (IQR 14.65, 85.21)and 19.87 (IQR 6.83, 46.83) to 47.74 (IQR 18.89, 118.77) with 15% and 45% school reopening. Increasing adult (age ≥18) OOHA from 65% to 80% of pre-pandemic levels resulted in increased latent infection prevalence per 100,000 from 35.18 (IQR 13.59, 75.00) to 69.84 (IQR 33.27, 145.89) and 38.17 (IQR 15.84, 91.16) to 80.02 (IQR 30.91, 186.63) with 15% and 45% school reopening. Similar patterns were observed for hospitalizations. In areas without widespread vaccination coverage, interventions to maintain adherence to protective behaviors, particularly among younger adults and in out-of-household settings, remain a priority for preventing COVID-19 transmission.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

29 th International Training Course on the Physical Protection of Nuclear Materials and Nuclear Facilities (Project Summary)

Sandia National Laboratories (Sandia) has hosted the International Training Course on the Physical Protection of Nuclear Materials and Nuclear Facilities since 1978. This course is the flagship training course of the International Atomic Energy Agency (IAEA). On behalf of the National Nuclear Security Administration (NNSA), Sandia manages, develops, and coordinates all course materials, and works closely with the IAEA to arrange all logistical details for the course ITC-29 incorporated some updates to the facility models, based on feedback received in ITC-28 and the ITC-29 dry run. In addition, all the graphics were refreshed after ITC-28 and implemented in the posters, data handbooks, etc. As in ITC-28, the ITC-29 staff worked closely with the IAEA on course materials, including a peer review process to ensure all course materials were aligned with Nuclear Security Series No. 13 and other relevant international guidance documentation from the IAEA. Due to the COVID-19 pandemic, ITC-29 was postponed twice and eventually took place in the late summer/early fall of 2022. Due to ongoing COVID-19 restrictions and safety requirements, the number of participants was reduced by about 31 percent from 59 participants in ITC-28 to 41 in ITC-29. As a result, the number of subgroups also decreased from eight in ITC-28 to six for ITC-29. This enabled the Sandia team to implement more opportunities for social distancing, particularly in the large classrooms. Despite the challenges, ITC-29 still passed a significant milestone, with the one-thousandth participant of the international training course attending during this event. Finally, the ITC staff continued the use of its improved method of evaluation to capture participants’ satisfaction with the updated course and to gather feedback concerning future improvements. This document provides a brief description of ITC-29, including a summary of lessons learned and key recommendations for future development efforts.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

Q4FY20 Quarterly Report. LL20-AM-sensor

Tardigrade is an effort to create tunable thermal expansion materials for use in a ruggedized lens housing. This requires new computational algorithms to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing. Project is 13 months into the 30 month on complete project plan. We are continuing effort on 3 main technical thrusts: algorithm development, multi-material additive manufacturing process exploration, and sensor/optics design and engineering. In FY20 we underspent the initial budget, partly due to work restrictions from Covid-19 pandemic, but are working to make up lost time

42 ENGINEERING↗
DOE OSTI2021

Offline Arterial Signal Timing Optimization based on Virtual Phase Link Model - A Real-world Case Study

Conventional signal timing for arterial usually takes bottom-up approaches. Engineers optimize each individual intersection first and then coordinate them by adjusting the offsets. This work is based on the Virtual Phase-Link (VPL) model, a street traffic model designed for online traffic model predictive control, to obtain a top-down offline arterial signal timing. We Studied the Shallowford Rd. in Chattanooga, TN and found that the inconsistency in intersection capacities along the arterial could lead to some intersections becoming bottlenecks. Signal timing is a significant factor that affect the intersection capacities. We realized that the VPL-based model can guarantee the consistency in intersections along an arterial. We therefore adopted the VPL-based model and developed an offline signal timing optimization approach. The proposed timing derived from the VPL-based offline signal timing optimization showed very good results in simulation. The Chattanooga Department of Transportation adopted the optimized timing obtained from the proposed approach and gave positive feedbacks to the research team. We also collected field experiment data, which demonstrated overall energy reductions and speed improvements on some sections of the Shallowford Rd. arterial. We will continue the experiment when the COVID-19 pandemic impact subsides to have a more robust quantitative evaluation.

Wang, Qichao↗
DOE OSTI2021

Isolation and characterization of cross-neutralizing coronavirus antibodies from COVID-19+ subjects

SARS-CoV-2 is one of three coronaviruses that have crossed the animal-to-human barrier and caused widespread disease in the past two decades. The development of a universal human coronavirus vaccine could prevent future pandemics. We characterize 198 antibodies isolated from four COVID-19+ subjects and identify 14 SARS-CoV-2 neutralizing antibodies. One targets the N-terminal domain (NTD), one recognizes an epitope in S2, and 11 bind the receptor-binding domain (RBD). Three anti-RBD neutralizing antibodies cross-neutralize SARS-CoV-1 by effectively blocking binding of both the SARS-CoV-1 and SARS-CoV-2 RBDs to the ACE2 receptor. Using the K18-hACE transgenic mouse model, we demonstrate that the neutralization potency and antibody epitope specificity regulates the in vivo protective potential of anti-SARS-CoV-2 antibodies. All four cross-neutralizing antibodies neutralize the B.1.351 mutant strain. Thus, our study reveals that epitopes in S2 can serve as blueprints for the design of immunogens capable of eliciting cross-neutralizing coronavirus antibodies.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

Cabin Crew Alertness and Performance During Long-Haul Flights

INTRODUCTION: -Sleep loss and circadian disruption pose a significant risk in aviation. -Previous literature has shown that inflight rest facilities influence alertness and performance among pilots, but few studies have evaluated cabin crew. -The aim of this research was to assess alertness and performance among cabin crew members sleeping in different rest locations during a long-haul out-and-back trip. METHODS: -Twenty-nine cabin crewmembers flew the same long-haul route (outbound and inbound). -Participants were randomly assigned to fly on an aircraft with a bunk in both directions or to fly an aircraft with a bunk in one direction and with a high comfort jump seat (HCJS) in the other direction. -Throughout the study they completed a Karolinska Sleepiness Scale (KSS) and a 5-minute Psychomotor Vigilance Task (PVT) at the beginning and at the end of each flight. RESULTS: -A series of mixed-effects models were performed to assess the changes in KSS and PVT when crewmembers slept in the bunk during both directions of flight (bunk-only) compared to when sleep was obtained in the HCJS during one direction and bunk in the other (bunk + HCJS). -There was no significant difference in KSS alertness between the two conditions. - There were no significant differences in PVT response speed or lapses between the two conditions. CONCLUSIONS: -Limitations: small number of participants. Our study stopped abruptly because of the COVID-19 pandemic which limited our sample size. -Further research is needed to understand how other factors such as duty start time and workload might influence the sleep of cabin crewmembers during long-haul flights.

long-haul↗
DOE OSTI2023

Data-Driven Whole-Genome Clustering to Detect Geospatial, Temporal, and Functional Trends in SARS-CoV-2 Evolution

Current methods for defining SARS-CoV-2 lineages ignore the vast majority of the SARS-CoV-2 genome. We develop and apply an exhaustive vector comparison method that directly compares all known SARS-CoV-2 genome sequences to produce novel lineage classifications. We utilize data-driven models that (i) accurately capture the complex interactions across the set of all known SARS-CoV-2 genomes, (ii) scale to leadership-class computing systems, and (iii) enable tracking how such strains evolve geospatially over time. We show that during the height of the original Omicron surge, countries across Europe, Asia, and the Americas had a spatially asynchronous distribution of Omicron sub-strains. Moreover, neighboring countries were often dominated by either different clusters of the same variant or different variants altogether throughout the pandemic. Analyses of this kind may suggest a different pattern of epidemiological risk than was understood from conventional data, as well as produce actionable insights and transform our ability to prepare for and respond to current and future biological threats.

Jacobson, Daniel↗
DOE OSTI2024

Enhancing Biopreparedness through a Model System to Understand the Molecular Mechanisms that Lead to Pathogenesis and Disease Transmission: NW-BRaVE

The science of biopreparedness to counter biological threats hinges on understanding the fundamental principles and molecular mechanisms that lead to pathogenesis and disease transmission. Our vision to address this challenge is to create a powerful and user-friendly platform to elucidate the fundamental principles of how molecular interactions drive pathogen-host relationships and host shifts. We will enable groundbreaking discoveries by integrating a wide range of structural, genomics, proteomics, and other advanced omics measurements, along with evolutionary and artificial intelligence predictions. To make sure the system is applicable to real-world problems, we will develop it in the context of a tractable model system, the small, abundant, and accessible photosynthetic cyanobacteria and their constantly co-adapting viral pathogens, cyanophages. This model will maintain the system’s applicability to real-world problems and techniques, but the overall focus will be on elucidating general principles of detecting, assessing, and surveilling molecular interaction, adaptation, and coevolution that are system agnostic and therefore extensible to other viral-host interactions. Our overall objectives are to (1) identify the molecular complexes that comprise the cyanobacteria redox macromolecular subsystem and how they dynamically change with bacteriophage infection in situ, using cryo-electron tomography; (2) profile regulatory changes during infection using proteomics, multiomics, and experimental validation, and integrate the data with in situ structures; (3) use genomics and metagenomics to determine environmental and population factors across time scales that impact the interactions between marine cyanobacteria and their cyanophage parasites, predicting the evolutionary origins of in situ structural and functional interactions, convergence and coevolution; and (4) develop a data integration and transformation platform that facilitates the integration of in situ, proteomic, and evolutionary measurements of molecular interactions to surveil diverse hosts and parasites in various environmental contexts. These objectives address Focus Area 2 Reveal Molecular Interactions Across Biological Scales for Design of Targeted Interventions. Our powerful and user-friendly platform will enhance connections between the often-siloed fields of structure, molecular phenotype, and evolutionary genomics that are key to biopreparedness, but in need of integration (Figure 1). We will build an integrated navigation tool to facilitate the effective use of globally distributed experimental data for integrated analysis and predictive modeling. The project will develop, implement, and test a platform to assess host-pathogen molecular interactions, adaptation to hosts and host shifts, and coevolution between hosts and pathogens, successfully impacting the research community by revolutionizing abilities to study any host-pathogen interaction, encourage diverse community contributions, and gain fundamental insights into how proteins adapt to new contexts. This ability will be critical for designing early interventions to address future threats. We will build surveillance training capability, aiming for a fair and equitable response to future pandemics and biothreats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

National Virtual Biotechnology Laboratory: Report on Rapid R&D Solutions to the COVID-19 Crisis

With funding from the CARES Act, the U.S Department of Energy (DOE) established the National Virtual Biotechnology Laboratory (NVBL) in March 2020 to address key challenges associated with the COVID-19 crisis. NVBL brought together the broad scientific and technical expertise and resources of DOE’s 17 national laboratories to help tackle medical supply short ages, discover potential drugs to fight the virus, develop and validate COVID-19 testing methods, model disease spread and impact across the nation, and understand virus transport in buildings and the environment. National laboratory resources leveraged for this effort include a suite of world-leading user facilities broadly available to the research community, such as light and neutron sources, nanoscale science research centers, sequencing and biocharacterization facilities, and high-performance computing facilities. Within months, NVBL teams produced innovations in materials and advanced manufacturing that mitigated shortages in test kits and personal protective equipment (PPE), creating nearly 1,000 new jobs. They used DOE’s high-performance computers and light and neutron sources to identify promising candidates for antibodies and antivirals that universities and drug companies are now evaluating. NVBL researchers also developed new diagnostic targets and sample collection approaches, and supported U.S. Food and Drug Administration (FDA), Centers for Disease Control and Prevention (CDC), and U.S. Department of Defense (DoD) efforts to establish national guidelines used in administering millions of tests. Researchers used artificial intelligence and high-performance computing to produce near-real-time data analysis to forecast disease transmission, stress on public health infrastructure, and economic impact, which supported decision-makers at the local, state, and national levels. NVBL teams also studied how to control indoor virus movement to minimize uptake and protect human health. NVBL’s accomplishments demonstrate not only the powerful resource represented by DOE’s national laboratories working together to meet national needs, but also the effectiveness of the integrated NVBL framework for rapidly responding to emergencies with research and development (R&D) solutions. As the fight against COVID continues, sustained efforts are needed to confront this pandemic as well as future threats. Examples include: 1) Establishing “supply chains on demand” to meet emergency production needs by leveraging the materials and manufacturing expertise of DOE national laboratories and developing advances in electronics, sensing, robotics, and automation capabilities; 2) Improving the speed and robustness of drug discovery by integrating experimental platforms with DOE’s computational and experimental user facilities, which provide unique resources to support the discovery of high-potential therapeutic agents; 3) Protecting public, environmental, and animal health by developing new testing protocols and instrumentation adaptable to diverse sample types (both physiological and environmental) to quickly detect a wide range of pathogens and monitor other biorisks; 4) Supporting near-real-time data needs of decision-makers at the local, regional, state, and national levels by advancing data curation, analysis, and modeling using artificial intelligence and new data science tools for managing and evaluating large diverse datasets; 5) Harnessing DOE’s expertise in environmental modeling to design rooms and air handling for offices, classrooms, restaurants, and other structures to minimize biorisk transmissions. Going forward, NVBL is poised to apply the unique capabilities and expertise of the national laboratory complex to future national and international emergencies, both natural and engineered. Through this framework, the Office of Science will continue to be an integral component of agency wide efforts to prepare for and respond to biorisks and other crises.

42 ENGINEERING↗
DOE OSTI2022

Nonclassical autophagy activation pathways are essential for production of infectious Influenza A virus in vitro

Autophagy is a critical mechanism deployed by eukaryotic cells in response to stress, including viral infection, to boost the innate antimicrobial responses. However, an increasing number of pathogens hijack the autophagic machinery to facilitate their own replication. Influenza A virus (IAV), responsible for several global pandemics, has an intricate dependence on autophagy for successful replication in mammalian cells. To elucidate key chokepoints in the host stress responses facilitating IAV replication, we constructed a meta-transcriptome of IAV and host gene expression dynamics during early (1–3 hpi), mid (4–6 hpi), and late (8–12 hpi) stages of the viral replication cycle at two multiplicities of infection (MOI): 1 and 5. We supplemented the global transcriptome study with phosphoproteomic analysis of stress-activated protein kinase (SAPK/JNK) signaling in lung carcinoma (predominantly used as an in vitro model of IAV replication) and normal human bronchial epithelial cells. We report significant differences in the activation profiles of autophagy regulating genes upon IAV infection at the two MOI as well as divergent dependence on ULK1 signaling within the normal and cancer cells. Regardless of the cell model, JNK-Thr187 signaling was crucial for the production of infectious viral particles.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Report of the Atmosphere to Electrons Land-Based Mesoscale-to-Microscale Coupling Project (FY2020)

The overall goal of the Mesoscale-to-Microscale Coupling (MMC) project is to improve coupling between mesoscale and microscale simulations via improved guidance and new strategies for setting up simulations and for the development of new tools that can be used across the community. Including the mesoscale forcing is critical to modeling the full energy transfer across scales in the atmosphere. The project-specific objectives include: 1) To apply rigorous verification and validation (V&V) techniques to the new modeling tools that are developed as part of the project to ensure the accuracy of our codes and results and to develop estimates of the relative uncertainty. 2) To improve computational performance of the coupled MMC models through the development of methods that can be used to reduce turbulence spin-up time and hence the size of computational domains. 3) To improve representation of the surface layer in microscale models to enhance simulations of hub-height wind speed. 4) To develop guidance for the community describing the best ways to couple mesoscale and microscale models, including specific spatial scales at which the handoff to the microscale model should occur. 5) To prepare documentation and a suite of software tools that can be used across the community. And 6) To transition MMC research to the offshore environment. Major progress was made in each of these areas during FY20. The land-based portion of the project is reported herein, while the offshore portion will be reported separately. The team continued to advance the MMC tools and methodologies as well as to document their performance in journal papers and conference presentations, although several planned conferences were cancelled due to the COVID-19 pandemic.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A time-varying vulnerability index for COVID-19 in New Mexico, USA using generalized propensity scores

The coronavirus disease (COVID-19) pandemic has highlighted systemic inequities in the United States and resulted in a larger burden of negative social outcomes for marginalized communities. New Mexico, a state in the southwestern US, has a unique population with a large racial minority population and a high rate of poverty that may make communities more vulnerable to negative social outcomes from COVID-19. To identify which communities may be at the highest relative risk, we created a county-level vulnerability index. After the first COVID-19 case was reported in New Mexico on March 11, 2020, we fit a generalized propensity score model that incorporates sociodemographic factors to predict county-level viral exposure and thus, the generic risk to negative social outcomes such as unemployment or mental health impacts. We used four static sociodemographic covariates important for the state of New Mexico—population, poverty, household size, and minority population—and weekly cumulative case counts to iteratively run our model each week and normalize the exposure score to create a time-varying vulnerability index. We found the relative vulnerability between counties varied in the first eight weeks from the initial COVID-19 case before stabilizing. This framework for creating a location-specific vulnerability index in response to an ongoing disaster may be used as a quick, deployable metric to inform health policy decisions such as allocating state resources to the county level.

59 BASIC BIOLOGICAL SCIENCES↗