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A Statistical Model of COVID-19 Infection Incidence in the Southern Indian State of Tamil Nadu

Author

Listed:
  • Tanmay Devi

    (Department of Computing and Data Sciences, FLAME University, Pune 412115, India
    Current address: Department of Social Sciences, Rice University, Houston, TX 77005, USA.
    These authors contributed equally to this work.)

  • Kaushik Gopalan

    (Department of Computing and Data Sciences, FLAME University, Pune 412115, India
    These authors contributed equally to this work.)

Abstract

In this manuscript, we present an analysis of COVID-19 infection incidence in the Indian state of Tamil Nadu. We used seroprevalence survey data along with COVID-19 fatality reports from a six-month period (1 June 2020 to 30 November 2020) to estimate age- and sex-specific COVID-19 infection fatality rates (IFR) for Tamil Nadu. We used these IFRs to estimate new infections occurring daily using the daily COVID-19 fatality reports published by the Government of Tamil Nadu. We found that these infection incidence estimates for the second COVID wave in Tamil Nadu were broadly consistent with the infection estimates from seroprevalence surveys. Further, we propose a composite statistical model that pairs a k-nearest neighbours model with a power-law characterisation for “out-of-range” extrapolation to estimate the COVID-19 infection incidence based on observed cases and test positivity ratio. We found that this model matched closely with the IFR-based infection incidence estimates for the first two COVID-19 waves for both Tamil Nadu as well as the neighbouring state of Karnataka. Finally, we used this statistical model to estimate the infection incidence during the recent “Omicron wave” in Tamil Nadu and Karnataka.

Suggested Citation

  • Tanmay Devi & Kaushik Gopalan, 2022. "A Statistical Model of COVID-19 Infection Incidence in the Southern Indian State of Tamil Nadu," IJERPH, MDPI, vol. 19(17), pages 1-10, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:11137-:d:907343
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    References listed on IDEAS

    as
    1. Andrew T. Levin & William P. Hanage & Nana Owusu-Boaitey & Kensington B. Cochran & Seamus P. Walsh & Gideon Meyerowitz-Katz, 2020. "Assessing the Age Specificity of Infection Fatality Rates for COVID-19: Systematic Review, Meta-analysis, & Public Policy Implications," NBER Working Papers 27597, National Bureau of Economic Research, Inc.
    2. Hani Amir Aouissi & Ahmed Hamimes & Mostefa Ababsa & Lavinia Bianco & Christian Napoli & Feriel Kheira Kebaili & Andrey E. Krauklis & Hafid Bouzekri & Kuldeep Dhama, 2022. "Bayesian Modeling of COVID-19 to Classify the Infection and Death Rates in a Specific Duration: The Case of Algerian Provinces," IJERPH, MDPI, vol. 19(15), pages 1-18, August.
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