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1. Protocol -- Evaluating the Effect of ACA Medicaid Expansion on Mortality During the COVID-19 Pandemic Using County-level Matching.

   Mann, C.; Hansen, B.; Gaydosh, L.; Lycurgus, T. (January 2021, Observational studies)

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2. The forecast of COVID-19 spread risk at the county level

   https://doi.org/10.1186/s40537-021-00491-1  

   Hssayeni, Murtadha D.; Chala, Arjuna; Dev, Roger; Xu, Lili; Shaw, Jesse; Furht, Borko; Ghoraani, Behnaz (December 2021, Journal of Big Data)

   Abstract The early detection of the coronavirus disease 2019 (COVID-19) outbreak is important to save people’s lives and restart the economy quickly and safely. People’s social behavior, reflected in their mobility data, plays a major role in spreading the disease. Therefore, we used the daily mobility data aggregated at the county level beside COVID-19 statistics and demographic information for short-term forecasting of COVID-19 outbreaks in the United States. The daily data are fed to a deep learning model based on Long Short-Term Memory (LSTM) to predict the accumulated number of COVID-19 cases in the next two weeks. A significant average correlation was achieved ( r =0.83 ( p = 0.005 )) between the model predicted and actual accumulated cases in the interval from August 1, 2020 until January 22, 2021. The model predictions had r > 0.7 for 87% of the counties across the United States. A lower correlation was reported for the counties with total cases of <1000 during the test interval. The average mean absolute error (MAE) was 605.4 and decreased with a decrease in the total number of cases during the testing interval. The model was able to capture the effect of government responses on COVID-19 cases. Also, it was able to capture the effect of age demographics on the COVID-19 spread. It showed that the average daily cases decreased with a decrease in the retiree percentage and increased with an increase in the young percentage. Lessons learned from this study not only can help with managing the COVID-19 pandemic but also can help with early and effective management of possible future pandemics. The code used for this study was made publicly available on https://github.com/Murtadha44/covid-19-spread-risk. 

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3. Association of Limited In-Person Attendance in US National Football League and National Collegiate Athletic Association Games With County-Level COVID-19 Cases

   https://doi.org/10.1001/jamanetworkopen.2021.19621  

   Toumi, Asmae; Zhao, Haoruo; Chhatwal, Jagpreet; Linas, Benjamin P.; Ayer, Turgay (August 2021, JAMA Network Open)
4. Unraveling the dynamic importance of county-level features in trajectory of COVID-19

   https://doi.org/10.1038/s41598-021-92634-w  

   Li, Qingchun; Yang, Yang; Wang, Wanqiu; Lee, Sanghyeon; Xiao, Xin; Gao, Xinyu; Oztekin, Bora; Fan, Chao; Mostafavi, Ali (December 2021, Scientific Reports)

   Abstract The objective of this study was to investigate the importance of multiple county-level features in the trajectory of COVID-19. We examined feature importance across 2787 counties in the United States using data-driven machine learning models. Existing mathematical models of disease spread usually focused on the case prediction with different infection rates without incorporating multiple heterogeneous features that could impact the spatial and temporal trajectory of COVID-19. Recognizing this, we trained a data-driven model using 23 features representing six key influencing factors affecting the pandemic spread: social demographics of counties, population activities, mobility within the counties, movement across counties, disease attributes, and social network structure. Also, we categorized counties into multiple groups according to their population densities, and we divided the trajectory of COVID-19 into three stages: the outbreak stage, the social distancing stage, and the reopening stage. The study aimed to answer two research questions: (1) The extent to which the importance of heterogeneous features evolved at different stages; (2) The extent to which the importance of heterogeneous features varied across counties with different characteristics. We fitted a set of random forest models to determine weekly feature importance. The results showed that: (1) Social demographic features, such as gross domestic product, population density, and minority status maintained high-importance features throughout stages of COVID-19 across 2787 studied counties; (2) Within-county mobility features had the highest importance in counties with higher population densities; (3) The feature reflecting the social network structure (Facebook, social connectedness index), had higher importance for counties with higher population densities. The results showed that the data-driven machine learning models could provide important insights to inform policymakers regarding feature importance for counties with various population densities and at different stages of a pandemic life cycle. 

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5. Moral values predict county-level COVID-19 vaccination rates in the United States.

   https://doi.org/10.1037/amp0001020  

   Reimer, Nils Karl; Atari, Mohammad; Karimi-Malekabadi, Farzan; Trager, Jackson; Kennedy, Brendan; Graham, Jesse; Dehghani, Morteza (September 2022, American Psychologist)