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Mean-field models for non-Markovian epidemics on networks.
Sherborne, Neil; Miller, Joel C; Blyuss, Konstantin B; Kiss, Istvan Z.
Afiliação
  • Sherborne N; Department of Mathematics, School of Mathematical and Physical Sciences, University of Sussex, Falmer, Brighton, BN1 9QH, UK.
  • Miller JC; School of Mathematics, Monash University, Melbourne, VIC, Australia.
  • Blyuss KB; School of Biology, Monash University, Melbourne, VIC, Australia.
  • Kiss IZ; MAXIMA, Monash University, Melbourne, VIC, Australia.
J Math Biol ; 76(3): 755-778, 2018 02.
Article em En | MEDLINE | ID: mdl-28685365
This paper introduces a novel extension of the edge-based compartmental model to epidemics where the transmission and recovery processes are driven by general independent probability distributions. Edge-based compartmental modelling is just one of many different approaches used to model the spread of an infectious disease on a network; the major result of this paper is the rigorous proof that the edge-based compartmental model and the message passing models are equivalent for general independent transmission and recovery processes. This implies that the new model is exact on the ensemble of configuration model networks of infinite size. For the case of Markovian transmission the message passing model is re-parametrised into a pairwise-like model which is then used to derive many well-known pairwise models for regular networks, or when the infectious period is exponentially distributed or is of a fixed length.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doenças Transmissíveis / Epidemias / Modelos Biológicos Tipo de estudo: Health_economic_evaluation / Prognostic_studies Limite: Humans Idioma: En Revista: J Math Biol Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doenças Transmissíveis / Epidemias / Modelos Biológicos Tipo de estudo: Health_economic_evaluation / Prognostic_studies Limite: Humans Idioma: En Revista: J Math Biol Ano de publicação: 2018 Tipo de documento: Article