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Early warning of vulnerable counties in a pandemic using socio-economic variables

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  • Ruck, Damian J.
  • Bentley, R. Alexander
  • Borycz, Joshua

Abstract

In the U.S. in early 2020, heterogenous and incomplete county-scale data on COVID-19 hindered effective interventions in the pandemic. While numbers of deaths can be used to estimate actual number of infections after a time lag, counties with low death counts early on have considerable uncertainty about true numbers of cases in the future. Here we show that supplementing county-scale mortality statistics with socioeconomic data helps estimate true numbers of COVID-19 infections in low-data counties, and hence provide an early warning of future concern. We fit a LASSO negative binomial regression to select a parsimonious set of five predictive variables from thirty-one county-level covariates. Of these, population density, public transportation use, voting patterns and % African-American population are most predictive of higher COVID-19 death rates. To test the model, we show that counties identified as under-estimating COVID-19 on an early date (April 17) have relatively higher deaths later (July 1) in the pandemic.

Suggested Citation

  • Ruck, Damian J. & Bentley, R. Alexander & Borycz, Joshua, 2021. "Early warning of vulnerable counties in a pandemic using socio-economic variables," Economics & Human Biology, Elsevier, vol. 41(C).
    • Handle: RePEc:eee:ehbiol:v:41:y:2021:i:c:s1570677x21000125
    DOI: 10.1016/j.ehb.2021.100988
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    References listed on IDEAS

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    1. R Maria del Rio-Chanona & Penny Mealy & Anton Pichler & François Lafond & J Doyne Farmer, 2020. "Supply and demand shocks in the COVID-19 pandemic: an industry and occupation perspective," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 36(Supplemen), pages 94-137.
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    3. R Maria del Rio-Chanona & Penny Mealy & Anton Pichler & François Lafond & J Doyne Farmer, 0. "Supply and demand shocks in the COVID-19 pandemic: an industry and occupation perspective," Oxford Review of Economic Policy, Oxford University Press, vol. 36(Supplemen), pages 94-137.
    4. Bentley, R. Alexander & Ormerod, Paul, 2010. "A rapid method for assessing social versus independent interest in health issues: A case study of 'bird flu' and 'swine flu'," Social Science & Medicine, Elsevier, vol. 71(3), pages 482-485, August.
    5. Michael Bailey & Rachel Cao & Theresa Kuchler & Johannes Stroebel & Arlene Wong, 2018. "Social Connectedness: Measurement, Determinants, and Effects," Journal of Economic Perspectives, American Economic Association, vol. 32(3), pages 259-280, Summer.
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    Cited by:

    1. Albani, Viviana & Welsh, Claire E. & Brown, Heather & Matthews, Fiona E. & Bambra, Clare, 2022. "Explaining the deprivation gap in COVID-19 mortality rates: A decomposition analysis of geographical inequalities in England," Social Science & Medicine, Elsevier, vol. 311(C).

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