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Dynamics of SIR model with heterogeneous response to intervention policy

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  • Rachinskii, Dmitrii
  • Rouf, Samiha

Abstract

In classical epidemic theory, behavior is assumed to be stationary. In recent years, epidemic models have been extended to include behaviors that transition in response to the current state of the epidemic. However, it is widely known that human behavior can exhibit strong history-dependence as a consequence of learned experiences. This history-dependence is similar to hysteresis phenomena that have been well-studied in control theory. To illustrate the importance of history-dependence for epidemic theory, we study dynamics of a variant of the SIRS model where individuals exhibit lazy-switch responses to prevalence dynamics, based on the Preisach hysteresis operator. The resulting model can possess a continuum of endemic equilibrium states characterized by different proportions of susceptible, infected and recovered populations. We consider how the limit point of the epidemic trajectory and the infection peak along this trajectory depend on the degree of heterogeneity of the response. Our approach supports the argument that public health responses during the emergence of a new disease can have fundamental long-term consequences for subsequent management efforts.

Suggested Citation

  • Rachinskii, Dmitrii & Rouf, Samiha, 2022. "Dynamics of SIR model with heterogeneous response to intervention policy," Theoretical Population Biology, Elsevier, vol. 146(C), pages 71-85.
  • Handle: RePEc:eee:thpobi:v:146:y:2022:i:c:p:71-85
    DOI: 10.1016/j.tpb.2022.06.003
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    References listed on IDEAS

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    1. Robert W. Fairlie, 2020. "The Impact of Covid-19 on Small Business Owners: The First Three Months after Social-Distancing Restrictions," CESifo Working Paper Series 8581, CESifo.
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    3. Geoffard, Pierre-Yves & Philipson, Tomas, 1996. "Rational Epidemics and Their Public Control," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 37(3), pages 603-624, August.
    4. Marquioni, Vitor M. & de Aguiar, Marcus A.M., 2020. "Quantifying the effects of quarantine using an IBM SEIR model on scalefree networks," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
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