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

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  • Hadeel AlQadi

    (Department of Mathematics and Statistics, University of Missouri-Kansas City, Kansas City, MO 64110, USA
    Department of Mathematics, Jazan University, Jazan 45142, Saudi Arabia)

  • 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)

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.

Suggested Citation

  • Hadeel AlQadi & Majid Bani-Yaghoub & Sindhu Balakumar & Siqi Wu & Alex Francisco, 2021. "Assessment of Retrospective COVID-19 Spatial Clusters with Respect to Demographic Factors: Case Study of Kansas City, Missouri, United States," IJERPH, MDPI, vol. 18(21), pages 1-15, November.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:21:p:11496-:d:669880
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    References listed on IDEAS

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    1. Martin Kulldorff, 2001. "Prospective time periodic geographical disease surveillance using a scan statistic," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 61-72.
    2. Sharif, Arshian & Aloui, Chaker & Yarovaya, Larisa, 2020. "COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: Fresh evidence from the wavelet-based approach," International Review of Financial Analysis, Elsevier, vol. 70(C).
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