Assessment of Retrospective COVID-19 Spatial Clusters with Respect to Demographic Factors: Case Study of Kansas City, Missouri, United States

Hadeel AlQadi, Majid Bani-Yaghoub, Sindhu Balakumar, Siqi Wu and Alex Francisco
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Hadeel AlQadi: Department of Mathematics and Statistics, University of Missouri-Kansas City, Kansas City, MO 64110, USA
Majid Bani-Yaghoub: Department of Mathematics and Statistics, University of Missouri-Kansas City, Kansas City, MO 64110, USA
Sindhu Balakumar: Department of Mathematics and Statistics, University of Missouri-Kansas City, Kansas City, MO 64110, USA
Siqi Wu: Department of Mathematics and Statistics, University of Missouri-Kansas City, Kansas City, MO 64110, USA
Alex Francisco: City of Kansas City Health Department, 2400 Troost Ave, Kansas City, MO 64108, USA

IJERPH, 2021, vol. 18, issue 21, 1-15

Abstract: Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The United States (U.S.) has the highest number of reported COVID-19 infections and related deaths in the world, accounting for 17.8% of total global confirmed cases as of August 2021. As COVID-19 spread throughout communities across the U.S., it became clear that inequities would arise among differing demographics. Several researchers have suggested that certain racial and ethnic minority groups may have been disproportionately impacted by the spread of COVID-19. In the present study, we used the daily data of COVID-19 cases in Kansas City, Missouri, to observe differences in COVID-19 clusters with respect to gender, race, and ethnicity. Specifically, we utilized a retrospective Poisson spatial scan statistic with respect to demographic factors to detect daily clusters of COVID-19 in Kansas City at the zip code level from March to November 2020. Our statistical results indicated that clusters of the male population were more widely scattered than clusters of the female population. Clusters of the Hispanic population had the highest prevalence and were also more widely scattered. This demographic cluster analysis can provide guidance for reducing the social inequalities associated with the COVID-19 pandemic. Moreover, applying stronger preventive and control measures to emerging clusters can reduce the likelihood of another epidemic wave of infection.

Keywords: disease surveillance; gender; race; ethnicity; COVID-19; SatScan; cluster analysis; spatial analysis; pandemic; time series (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (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)

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