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A Review of Matrix SIR Arino Epidemic Models

Author

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  • Florin Avram

    (Laboratoire de Mathématiques Appliquées, Université de Pau, 64000 Pau, France)

  • Rim Adenane

    (Département des Mathématiques, Université Ibn-Tofail, Kenitra 14000, Morocco)

  • David I. Ketcheson

    (Department of Applied Mathematics and Computational Science, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia)

Abstract

Many of the models used nowadays in mathematical epidemiology, in particular in COVID-19 research, belong to a certain subclass of compartmental models whose classes may be divided into three “ ( x , y , z ) ” groups, which we will call respectively “susceptible/entrance, diseased, and output” (in the classic SIR case, there is only one class of each type). Roughly, the ODE dynamics of these models contains only linear terms, with the exception of products between x and y terms. It has long been noticed that the reproduction number R has a very simple Formula in terms of the matrices which define the model, and an explicit first integral Formula is also available. These results can be traced back at least to Arino, Brauer, van den Driessche, Watmough, and Wu (2007) and to Feng (2007), respectively, and may be viewed as the “basic laws of SIR-type epidemics”. However, many papers continue to reprove them in particular instances. This motivated us to redraw attention to these basic laws and provide a self-contained reference of related formulas for ( x , y , z ) models. For the case of one susceptible class, we propose to use the name SIR-PH, due to a simple probabilistic interpretation as SIR models where the exponential infection time has been replaced by a PH-type distribution. Note that to each SIR-PH model, one may associate a scalar quantity Y ( t ) which satisfies “classic SIR relations”,which may be useful to obtain approximate control policies.

Suggested Citation

  • Florin Avram & Rim Adenane & David I. Ketcheson, 2021. "A Review of Matrix SIR Arino Epidemic Models," Mathematics, MDPI, vol. 9(13), pages 1-14, June.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:13:p:1513-:d:584000
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    References listed on IDEAS

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    1. Fernando E. Alvarez & David Argente & Francesco Lippi, 2020. "A Simple Planning Problem for COVID-19 Lockdown," NBER Working Papers 26981, National Bureau of Economic Research, Inc.
    2. Kurtz, Thomas G., 1978. "Strong approximation theorems for density dependent Markov chains," Stochastic Processes and their Applications, Elsevier, vol. 6(3), pages 223-240, February.
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    Cited by:

    1. Publio Darío Cortés-Carvajal & Mitzi Cubilla-Montilla & David Ricardo González-Cortés, 2022. "Estimation of the Instantaneous Reproduction Number and Its Confidence Interval for Modeling the COVID-19 Pandemic," Mathematics, MDPI, vol. 10(2), pages 1-30, January.

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