MEPI Subgroup Contributed Talks

Kiel Corkran

University of Missouri- Kansas City

"An Agent-Based modeling approach to Investigate Pandemic Preparedness of Nursing Homes"

The pandemic preparedness of nursing homes has been a major concern for decades. The COVID-19 pandemic proved that the concerns were valid, as it caused devasting death tolls in nursing home facilities. This study presents an agent-based modeling framework to better understand the dynamics of pandemics within and between nursing homes. This is sharply distinct from many agent-based modeling works that resemble the spread of the infection within a single nursing home. We first calibrate the model of multiple nursing homes using the available COVID-19 data. Then we investigate the effects of shared staff on the efficacy of Covid-19 preventive policies through extensive simulations. It is shown that shared staffing can significantly diminish the efficacy of preventive policies. In conclusion, the nursing workforce is a determining factor for pandemic preparedness.
Additional authors: Jose Pablo Gomez; Majid Bani Yaghoub;

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Sansao Pedro

Eduardo Mondlane University

"An Agent-Based Model for Studying the Spread of COVID-19 in Mozambique: Pandemic Planing Implications of Population Mobility Patterns"

Background: The COVID-19 pandemic has become a new global public health crisis, and to large extent, its capacity to cross natural geographic barriers is attributed to human mobility and contact patterns which vary with time and specific locations. Therefore, an agent-based model (ABM) which relates populations mobility patterns in different locations in compliance with on site COVID-19 control measures is proposed to investigate how opening and closing protocols would have been best implemented in Mozambique. Methods: For spatial dynamics, a survey was carried out in the city of Maputo as a case study to estimate populations mobility patterns and contact matrices among individuals in different locations (home, school, work place, worship place, market and any other place of gathering) during specific periods of the day (morning, afternoon and night) for both week days and weekends. Individuals are explicitly represented by agents associated to disease characteristics and their decision to remain or move to a new place is based on a probability estimated from the survey and on site declared control measures. Results: The results show that at $50%$ of social distancing compliance, complete lockdown of schools, workplaces, worship places with exception of markets is the only scenario that result in the reduction and shift of the peak by $3%$ and 3 days respectively. School closure showed significant effect that at $75%$ and $85%$ of social distancing adherence resulted in the reduction and shift of the peak by $15%$ and 4 days, and $51%$ and 24 days respectively. While closure of worship places rendered little effect due to limited frequency and duration of activities in a given location. Conclusions: This study has demonstrated the use of simulation models to investigate the implementation of opening and closing policies for the control of COVID-19 pandemic at local scale by leveraging between the mobility of individuals and adherence to social distancing.
Additional authors: Alfredo Z. Muxlhanga, Frank T. Ndjomatchoa, Daisuke Takahashi, Chris T. Bauch

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Theresa Sheets

University of Utah

"Forecasting SARS-CoV-2 Hospitalizations in Utah with Multiple Public Health Metrics"

Percent positivity, the ratio of positive SARS-CoV-2 tests to total number of tests, has been used throughout the COVID-19 pandemic as a proxy for the current level of transmission in a community. Simultaneously, wastewater SARS-CoV-2 monitoring has been implemented, but is a highly variable metric whose direct utility has yet to be fully explored. As we transition from pandemic response to endemic management, testing efforts have been reduced and the predictive value of test percent positivity has been called into question. We build a series of models incorporating SARS-CoV-2 test positivity, wastewater SARS-CoV-2 levels, and syndromic surveillance data streams to explore changing transmission dynamics. A county level model is developed to forecast hospitalizations and tested against an ARIMA based on hospitalizations alone. A 21-day forecast is developed with sliding scale cross validation. We validate and quantify uncertainty in commonly used public health metrics and explore differences in model selection between variants. Data from the winter 2022-23 season are reserved as a final test for the model. In this work, we examine how to effectively predict hospitalizations in a changing testing environment.
Additional authors: Joel Skaria Utah Department of Health and Human Services; Nathan LaCross Utah Department of Health and Human Services; Randon Gruninger Utah Department of Health and Human Services; Lindsay Keegan University of Utah Division of Epidemiology

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