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Bayesian Framework for Multi-Wave COVID-19 Epidemic Analysis Using Empirical Vaccination Data

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  • Jiawei Xu

    (Department of Statistics, East China Normal University, Shanghai 200062, China
    KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai 200062, China
    Department of Statistics, University of Haifa, Haifa 3498838, Israel
    ECNU-UH Joint Translational Science and Technology Research Institute, Shanghai 200241, China)

  • Yincai Tang

    (Department of Statistics, East China Normal University, Shanghai 200062, China
    KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai 200062, China)

Abstract

The COVID-19 pandemic has highlighted the necessity of advanced modeling inference using the limited data of daily cases. Tracking a long-term epidemic trajectory requires explanatory modeling with more complexities than the one with short-time forecasts, especially for the highly vaccinated scenario in the latest phase. With this work, we propose a novel modeling framework that combines an epidemiological model with Bayesian inference to perform an explanatory analysis on the spreading of COVID-19 in Israel. The Bayesian inference is implemented on a modified SEIR compartmental model supplemented by real-time vaccination data and piecewise transmission and infectious rates determined by change points. We illustrate the fitted multi-wave trajectory in Israel with the checkpoints of major changes in publicly announced interventions or critical social events. The result of our modeling framework partly reflects the impact of different stages of mitigation strategies as well as the vaccination effectiveness, and provides forecasts of near future scenarios.

Suggested Citation

  • Jiawei Xu & Yincai Tang, 2021. "Bayesian Framework for Multi-Wave COVID-19 Epidemic Analysis Using Empirical Vaccination Data," Mathematics, MDPI, vol. 10(1), pages 1-22, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2021:i:1:p:21-:d:707966
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    References listed on IDEAS

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