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Modeling mobility, risk, and pandemic severity during the first year of COVID

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  • Gilgur, Alexander
  • Ramirez-Marquez, Jose Emmanuel

Abstract

During the COVID-19 pandemic, most US states have taken measures of varying strength, enforcing social and physical distancing in the interest of public safety. These measures have enabled counties and states, with varying success, to slow down the propagation and mortality of the disease by matching the propagation rate to the capacity of medical facilities. However, each state’s government was making its decisions based on limited information and without the benefit of being able to look retrospectively at the problem at large and to analyze the commonalities and the differences among the states and the counties across the country. We developed models connecting people’s mobility, socioeconomic, and demographic factors with severity of the COVID pandemic in the US at the County level. These models can be used to inform policymakers and other stakeholders on measures to be taken during a pandemic. They also enable in-depth analysis of factors affecting the relationship between mobility and the severity of the disease. With the exception of one model, that of COVID recovery time, the resulting models accurately predict the vulnerability and severity metrics and rank the explanatory variables in the order of statistical importance. We also analyze and explain why recovery time did not allow for a good model.

Suggested Citation

  • Gilgur, Alexander & Ramirez-Marquez, Jose Emmanuel, 2022. "Modeling mobility, risk, and pandemic severity during the first year of COVID," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    • Handle: RePEc:eee:soceps:v:84:y:2022:i:c:s0038012122001914
    DOI: 10.1016/j.seps.2022.101397
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    References listed on IDEAS

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    1. Cooper, Ian & Mondal, Argha & Antonopoulos, Chris G., 2020. "A SIR model assumption for the spread of COVID-19 in different communities," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
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    4. Kathy Leung & Joseph T. Wu & Gabriel M. Leung, 2021. "Real-time tracking and prediction of COVID-19 infection using digital proxies of population mobility and mixing," Nature Communications, Nature, vol. 12(1), pages 1-8, December.
    5. Shelby R. Buckman & Reuven Glick & Kevin J. Lansing & Nicolas Petrosky-Nadeau & Lily Seitelman, 2020. "Replicating and Projecting the Path of COVID-19 with a Model-Implied Reproduction Number," Working Paper Series 2020-24, Federal Reserve Bank of San Francisco.
    6. Lina Díaz-Castro & Héctor Cabello-Rangel & Kurt Hoffman, 2021. "The Impact of Health Policies and Sociodemographic Factors on Doubling Time of the COVID-19 Pandemic in Mexico," IJERPH, MDPI, vol. 18(5), pages 1-13, February.
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    Cited by:

    1. Borges, Ana & Carvalho, Mariana & Maia, Miguel & Guimarães, Miguel & Carneiro, Davide, 2023. "Predicting and explaining absenteeism risk in hospital patients before and during COVID-19," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).

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