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Indexing of US Counties with Overdispersed Incidences of COVID-19 Deaths

Author

Listed:
  • Ramalingam Shanmugam

    (School of Health Administration, Texas State University, San Marcos, TX 78666, USA)

  • Lawrence Fulton

    (Applied Analytics, Boston College, Chestnut Hill, MA 02467, USA)

  • Jose Betancourt

    (School of Health Administration, Texas State University, San Marcos, TX 78666, USA)

  • Gerardo J. Pacheco

    (School of Health Administration, Texas State University, San Marcos, TX 78666, USA)

  • Keya Sen

    (School of Health Administration, Texas State University, San Marcos, TX 78666, USA)

Abstract

The number of COVID-19 fatalities fluctuated widely across United States (US) counties. The number of deaths is stochastic. When the average number of deaths is equal to the dispersion, the distribution is the usual Poisson. When the average number of deaths is higher than the dispersion, the distribution is an intervened Poisson. When the average number of deaths is lower than the dispersion, the distribution is an incidence-rate-restricted Poisson (IRRP) type. Because dispersion of COVID-19 fatalities in some counties is higher than the average number of fatalities, the underlying model for the chance-oriented mechanism might be IRRP. Understanding where this overdispersion or volatility exists and the implications of it is the topic of this research. In essence, this paper focuses on the number of COVID-19 fatalities that fluctuated widely across United States (US) counties and develops an incidence-rate-restricted Poisson (IRRP) to understand where this overdispersion or volatility exists and the implications of it.

Suggested Citation

  • Ramalingam Shanmugam & Lawrence Fulton & Jose Betancourt & Gerardo J. Pacheco & Keya Sen, 2023. "Indexing of US Counties with Overdispersed Incidences of COVID-19 Deaths," Mathematics, MDPI, vol. 11(14), pages 1-11, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:14:p:3112-:d:1194186
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    References listed on IDEAS

    as
    1. Amani Almohaimeed & Jochen Einbeck & Najla Qarmalah & Hanan Alkhidhr, 2022. "Using Random Effect Models to Produce Robust Estimates of Death Rates in COVID-19 Data," IJERPH, MDPI, vol. 19(22), pages 1-13, November.
    2. Ramalingam Shanmugam & Lawrence Fulton & Jose Betancourt & Gerardo J. Pacheco, 2022. "Indexing Inefficacy of Efforts to Stop Escalation of COVID Mortality," Mathematics, MDPI, vol. 10(24), pages 1-11, December.
    3. Ting Tian & Jianbin Tan & Wenxiang Luo & Yukang Jiang & Minqiong Chen & Songpan Yang & Canhong Wen & Wenliang Pan & Xueqin Wang, 2021. "The Effects of Stringent and Mild Interventions for Coronavirus Pandemic," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(534), pages 481-491, April.
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