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DOE OSTI2024

Congruity of genomic and epidemiological data in modelling of local cholera outbreaks

Cholera continues to be a global health threat. Understanding how cholera spreads between locations is fundamental to the rational, evidence-based design of intervention and control efforts. Traditionally, cholera transmission models have used cholera case-count data. More recently, whole-genome sequence data have qualitatively described cholera transmission. Integrating these data streams may provide much more accurate models of cholera spread; however, no systematic analyses have been performed so far to compare traditional case-count models to the phylodynamic models from genomic data for cholera transmission. Here, we use high-fidelity case-count and whole-genome sequencing data from the 1991 to 1998 cholera epidemic in Argentina to directly compare the epidemiological model parameters estimated from these two data sources. We find that phylodynamic methods applied to cholera genomics data provide comparable estimates that are in line with established methods. Our methodology represents a critical step in building a framework for integrating case-count and genomic data sources for cholera epidemiology and other bacterial pathogens.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Identifying spatiotemporal patterns in opioid vulnerability: investigating the links between disability, prescription opioids and opioid-related mortality

Background: The opioid crisis remains one of the most daunting and complex public health problems in the United States. This study investigates the national epidemic by analyzing vulnerability profiles of three key factors: opioid-related mortality rates, opioid prescription dispensing rates, and disability rank ordered rates. Methods: This study utilizes county level data, spanning the years 2014 through 2020, on the rates of opioid-related mortality, opioid prescription dispensing, and disability. To successfully estimate and predict trends in these opioid-related factors, we augment the Kalman Filter with a novel spatial component. To define opioid vulnerability profiles, we create heat maps of our filter’s predicted rates across the nation’s counties and identify the hotspots. In this context, hotspots are defined on a year-by-year basis as counties with rates in the top 5% nationally. Results: Our spatial Kalman filter demonstrates strong predictive performance. From 2014 to 2018, these predictions highlight consistent spatiotemporal patterns across all three factors, with Appalachia distinguished as the nation’s most vulnerable region. Starting in 2019 however, the dispensing rate profiles undergo a dramatic and chaotic shift. Conclusions: The initial primary drivers of opioid abuse in the Appalachian region were likely prescription opioids; however, it now appears that abuse is sustained by illegal drugs. Additionally, we find that the disabled subpopulation may be more at risk of opioid-related mortality than the general population. Public health initiatives must extend beyond controlling prescription practices to address the transition to and impact of illicit drug use.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Epidemiologic and economic considerations regarding persistently infected cattle during vaccinate-to-live strategies for control of foot-and-mouth disease in FMD-free regions

Development of a foot-and-mouth disease (FMD) carrier state following FMD virus (FMDV) infection is a well-established phenomenon in cattle. However, the proportion of cattle likely to become carriers and the duration of the carrier state at a herd or population-level are incompletely understood. The objective of this study was to examine the epidemiologic and economic impacts of vaccination-to-live strategy in a disease-free region or country. We developed and simulated scenarios of FMD spread and control in the US livestock population, which included depopulation for a limited period, followed by a vaccinate-to-live strategy with strong biosecurity and movement restrictions. Six scenarios of FMD spread and control were simulated in the InterSpread Plus (ISP) modeling tool. Data on the number of infected and depopulated cattle (by operation types) from ISP model runs were used to estimate the monthly number of infected but not depopulated (potential carrier) cattle after the infection. Using available literature data on the FMD carrier state, we estimated the monthly proportion of carrier cattle (from infected but not depopulated cattle) over time following infection. Among the simulated scenarios, the median (25th, 75th percentile) number of infected cattle ranged from 43,217 (42,819, 55,274) head to 148,907 (75,819, 205,350) head, and the epidemic duration ranged from 20 (11, 30) to 76 (38, 136) days. In general, larger outbreaks occurred when depopulation was carried out through longer periods, and the onset of the vaccination was late (p > 0.05). The estimated proportion of surviving cattle, which were infected and not depopulated and had the potential to become persistently infected ranged from 14 to 35% of total infected cattle. Production losses in beef and dairy sectors were higher when outbreaks started in multiple states simultaneously, but production losses were small compared to trade losses and consumer avoidance losses. These results can be used to inform the consideration of a vaccinate-to-live strategy for FMD outbreaks and the development of appropriate post-outbreak management strategies. Furthermore, this output will enable a more detailed examination of the epidemiologic and economic implications of allowing convalescent cattle to survive and remain in production chains after FMD outbreaks in FMD-free regions.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Agent-based Disease Surveillance and Transmission Model

The SARS-CoV-2 pandemic has driven a need for models that can predict transmission among populations and test the effectiveness of proposed interventions to control it such as testing, quarantining, vaccinations, and nonpharmaceutical interventions. Some questions of interest include:1. How rapidly will the virus spread if no action is taken? 2. How frequently should people be tested and quarantined if necessary? 3. Should unvaccinated persons be tested more frequently than the vaccinated? 4. If test results are delayed, how will that affect the spread? 5. How do test false negative rates affect the overall efficacy of testing programs? 6. How do constraints on testing resources impact the overall efficacy of testing programs? 7. How does the patient’s failure to recognize COVID symptoms and quarantine affect the spread? 8. What level of vaccination coverage is needed to suppress the epidemic? 9. What is the risk of a large outbreak (probability exceeding x infections)? 10. What is the cost effectiveness of interventions (dollars per avoided infection)? This report describes the structure of a discrete event simulation model that provides actionable information to address these questions. Parameter inputs and results of analyses have been reported in briefings provided to LLNL management and are not discussed here.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

A Visual Analytic Platform for Interactive Validation of Human Mobility Simulations

Human mobility insights guide domain experts in an array of decisions, including critical infrastructure design, disaster response, epidemic modeling, national security, and policy making. Due to the inherent noise and privacy concerns in real-world individual-level mobility data, it is often preferred to leverage simulators that generate synthetic mobility data instead. However, it is critical to inspect and validate the output of such simulators to ensure the synthetic data is aligned with the characteristics of the population and the area of interest known to domain experts. While there exist many quantitative approaches for validating synthetic data, we argue it is also important to also validate such data qualitatively to capture aspects that are known to domain experts but difficult to quantify. In this work, we demonstrate a visual analytic platform that empowers domain experts to interact with their simulation outputs along spatial and temporal dimensions. By augmenting automated techniques and human skills, our visual analytic platform is a step towards interactive capabilities for model steering and quality control of mobility simulators.

Monadjemi, Shayan↗

DOE OSTI2020

Dynamical model for social distancing in the U.S. during the COVID-19 epidemic

Social distancing has led to a “flattening of the curve” in many states across the U.S. This is part of a novel, massive, global social experiment which has served to mitigate the COVID-19 pandemic in the absence of a vaccine or effective anti-viral drugs. Hence it is important to be able to forecast hospitalizations reasonably accurately. We propose on phenomenological grounds a random walk/generalized diffusion equation which incorporates the effect of social distancing to describe the temporal evolution of the probability of having a given number of hospitalizations. The probability density function is log-normal in the number of hospitalizations, which is useful in describing pandemics where the number of hospitalizations is very high. We used this insight and data to make forecasts for states using Monte Carlo methods. Back testing validates our approach, which yields good results about a week into the future. States are beginning to reopen at the time of submission of this paper and our forecasts indicate possible precursors of increased hospitalizations. However, the trends we forecast for hospitalizations as well as infections thus far show moderate growth. Additionally we studied the reproducibility in New York (Italian strain) and California (Wuhan strain). We find that even if there is a difference in the transmission of the two strains, social distancing has been able to control the progression of COVID 19.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Dysregulation of lung epithelial cell homeostasis and immunity contributes to Middle East respiratory syndrome coronavirus disease severity

Coronaviruses (CoV) emerge suddenly from animal reservoirs to cause novel diseases in new hosts. Discovered in 2012, the Middle East respiratory syndrome coronavirus (MERS-CoV) is endemic in camels in the Middle East and is continually causing local outbreaks and epidemics. While all three newly emerging human CoVs from the past 20 years (SARS-CoV, SARS-CoV-2, and MERS-CoV) cause respiratory disease, each CoV has unique host interactions that drive differential pathogeneses. To better understand the virus and host interactions driving lethal MERS-CoV infection, we performed a longitudinal multi-omics analysis of sublethal and lethal MERS-CoV infection in mice. Significant differences were observed in body weight loss, virus titers, and acute lung injury among lethal and sub-lethal virus doses. Virus-induced apoptosis of type I and II alveolar epithelial cells suggests that loss or dysregulation of these key cell populations was a major driver of severe disease. Omics analysis suggested differential pathogenesis was multi-factorial with clear differences among innate and adaptive immune pathways as well as those that regulate lung epithelial homeostasis. Infection of mice lacking functional T and B cells showed that adaptive immunity was important in controlling viral replication but also increased pathogenesis. In summary, we provide a high-resolution host response atlas for MERS-CoV infection and disease severity. Multi-omics studies of viral pathogenesis offer a unique opportunity to not only better understand the molecular mechanisms of disease but also to identify genes and pathways that can be exploited for therapeutic intervention all of which is important for our future pandemic preparedness.

MERS-CoV↗

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↗