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Non-Markovian epidemics

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Mathematics of Epidemics on Networks

Part of the book series: Interdisciplinary Applied Mathematics ((IAM,volume 46))

Abstract

Early studies of non-Markovian epidemics focused on SIR dynamics on fully connected networks, or homogeneously mixing populations, with the infection process being Markovian but with the infectious period taken from a general distribution [8, 278, 292, 293]. These approaches use probability theory arguments and typically focus on characterising the distribution of final epidemic sizes for finite populations, or on the average size in the infinite population limit. Similarly, the quasi-stationary distribution in a stochastic SIS model, again in a fully connected network, has been the subject of many studies [66, 230, 231]. More recently, it has been shown that one can readily apply results from queueing [19] or branching process [233] theory, or use martingales [65] to cast the same questions within a different framework and obtain results more readily.

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Authors and Affiliations

  1. Department of Mathematics, University of Sussex, Falmer, Brighton, UK

    István Z. Kiss

  2. Applied Mathematics, Institute for Disease Modeling, Bellevue, WA, USA

    Joel C. Miller

  3. Institute of Mathematics, Eötvös Loránd University, Budapest, Hungary

    Péter L. Simon

Authors
  1. István Z. Kiss
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  2. Joel C. Miller
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  3. Péter L. Simon
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Kiss, I.Z., Miller, J.C., Simon, P.L. (2017). Non-Markovian epidemics. In: Mathematics of Epidemics on Networks. Interdisciplinary Applied Mathematics, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-319-50806-1_9

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