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

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

Economics & Human Biology, 2021, vol. 41, issue C

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.

Keywords: COVID-19; Socioeconomic covariates; Pandemics; County-scale estimation; Social networks; Negative binomial regression; LASSO (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1570677X21000125
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ehbiol:v:41:y:2021:i:c:s1570677x21000125

DOI: 10.1016/j.ehb.2021.100988

Access Statistics for this article

Economics & Human Biology is currently edited by J. Komlos, Inas R Kelly and Joerg Baten

More articles in Economics & Human Biology from Elsevier
Bibliographic data for series maintained by Catherine Liu ().