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Mathematical Biosciences and Engineering

2007, Volume 4, Issue 4: 675-686. doi: 10.3934/mbe.2007.4.675
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Final and peak epidemic sizes for SEIR models with quarantine and isolation

  • 1.
    Department of Mathematics, Purdue University, West Lafayette, IN 47907-1395
  • Received: 01 July 2007 Accepted: 29 June 2018 Published: 01 August 2007

  • MSC : 92D25, 92D30, 34C60.

  • Two SEIR models with quarantine and isolation are considered, in which the latent and infectious periods are assumed to have an exponential and gamma distribution, respectively. Previous studies have suggested (based on numerical observations) that a gamma distribution model (GDM) tends to predict a larger epidemic peak value and shorter duration than an exponential distribution model (EDM). By deriving analytic formulas for the maximum and final epidemic sizes of the two models, we demonstrate that either GDM or EDM may predict a larger epidemic peak or final epidemic size, depending on control measures. These formulas are helpful not only for understanding how model assumptions may affect the predictions, but also for confirming that it is important to assume realistic distributions of latent and infectious periods when the model is used for public health policy making.

    Citation: Z. Feng. Final and peak epidemic sizes for SEIR models with quarantine and isolation[J]. Mathematical Biosciences and Engineering, 2007, 4(4): 675-686. doi: 10.3934/mbe.2007.4.675

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  • Two SEIR models with quarantine and isolation are considered, in which the latent and infectious periods are assumed to have an exponential and gamma distribution, respectively. Previous studies have suggested (based on numerical observations) that a gamma distribution model (GDM) tends to predict a larger epidemic peak value and shorter duration than an exponential distribution model (EDM). By deriving analytic formulas for the maximum and final epidemic sizes of the two models, we demonstrate that either GDM or EDM may predict a larger epidemic peak or final epidemic size, depending on control measures. These formulas are helpful not only for understanding how model assumptions may affect the predictions, but also for confirming that it is important to assume realistic distributions of latent and infectious periods when the model is used for public health policy making.


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