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Analysis of Virus Transmission: A Stochastic Transition Model Representation of Epidemiological Models

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  • Christian Gourieroux
  • Joann Jasiak

Abstract

The growing literature on the transmission of COVID-19 relies on various dynamic SIR-type models (Susceptible-Infected-Recovered). For ease of comparison and specification testing, we introduce a common stochastic representation of the SIR-type epidemiological models. This representation is a discrete time transition model, which allows for classifying the epidemiological models with respect to the number of states (compartments) and their interpretation. Additionally, the (stochastic) transition model eliminates several limitations of the (deterministic) continuous time epidemiological models, which are pointed out in the paper. We show that when data on aggregate compartment counts are available, all discrete time SIR-type models admit a nonlinear (pseudo) state space representation and can be consistently estimated and updated from an extended Kalman filter.

Suggested Citation

  • Christian Gourieroux & Joann Jasiak, 2020. "Analysis of Virus Transmission: A Stochastic Transition Model Representation of Epidemiological Models," Annals of Economics and Statistics, GENES, issue 140, pages 1-26.
  • Handle: RePEc:adr:anecst:y:2020:i:140:p:1-26
    DOI: 10.15609/annaeconstat2009.140.0001
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    Cited by:

    1. M. Hashem Pesaran & Cynthia Fan Yang, 2020. "Matching Theory and Evidence on Covid-19 Using a Stochastic Network SIR Model," CESifo Working Paper Series 8695, CESifo.

    More about this item

    Keywords

    Covid-19; Epidemiological Model; SIR Model; Transition Model; State-Space Representation;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • I10 - Health, Education, and Welfare - - Health - - - General

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