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

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    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.

    Original languageEnglish
    Article number101397
    JournalSocio-Economic Planning Sciences
    Volume84
    DOIs
    StatePublished - Dec 2022

    Keywords

    • COVID-19
    • Coronavirus
    • Data For Good
    • Mobility
    • OxCGRT
    • Pandemic
    • Severity metrics
    • Socioeconomic data
    • Vulnerability

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