Socio-economic impact on COVID-19 cases and deaths and its evolution in New Jersey

Dhammika Amaratunga (), Javier Cabrera (), Debopriya Ghosh (), Michael N. Katehakis (), Jin Wang () and Wenting Wang ()
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Dhammika Amaratunga: Princeton Data Analytics
Javier Cabrera: Rutgers, The State University of New Jersey
Debopriya Ghosh: Janssen Research and Development LLC
Michael N. Katehakis: Rutgers, The State University of New Jersey
Jin Wang: Jinan University
Wenting Wang: Rutgers, The State University of New Jersey

Annals of Operations Research, 2022, vol. 317, issue 1, No 2, 5-18

Abstract: Abstract Socio-economic factors could impact how epidemics spread. In this study, we investigated the possible effect of several local socio-economic factors on the case count and time course of confirmed Covid-19 cases and Covid-19-related deaths across the twenty one counties of New Jersey. Socio-economic and geographic factors considered included population, percentage of elders in the population, percentage of low-income households, access to food and health facilities and distance to New York. We found that the counties could be clustered into three groups based on (a) the case totals, (b) the total number of deaths, (c) the time course of the cases and (d) the time course of the deaths. The four groupings were very similar to one another and could all be largely explained by the county population, the percentage of low-income population, and the distance of the county from New York. As for food and health factors, the numbers of local restaurants and pharmacies significantly influenced the total number of cases and deaths as well as the epidemic’s evolution. In particular, the number of cases and deaths showed a decrease with the number of McDonald’s within the county in contrast to other fast-food or non-fast food restaurants. Overall, our study found that the evolution of the epidemic was influenced by certain socio-economic factors, which could be helpful for the formulation of public health policies.

Keywords: COVID-19; Time course data; Cluster analysis; Simpson’s rule dissimilarity; Multidimensional scaling; Linear model (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10479-021-03941-4

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