Fast Inference for Network Models of Infectious Disease Spread |
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| Authors: | Razvan G. Romanescu Rob Deardon |
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| Affiliation: | 1. Department of Mathematics and StatisticsUniversity of Guelph;2. Department of Production Animal Health, Department of Mathematics and StatisticsUniversity of Calgary |
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| Abstract: | Models of infectious disease over contact networks offer a versatile means of capturing heterogeneity in populations during an epidemic. Highly connected individuals tend to be infected at a higher rate early during an outbreak than those with fewer connections. A powerful approach based on the probability generating function of the individual degree distribution exists for modelling the mean field dynamics of outbreaks in such a population. We develop the same idea in a stochastic context, by proposing a comprehensive model for 1‐week‐ahead incidence counts. Our focus is inferring contact network (and other epidemic) parameters for some common degree distributions, in the case when the network is non‐homogeneous ‘at random’. Our model is initially set within a susceptible–infectious–removed framework, then extended to the susceptible–infectious–removed–susceptible scenario, and we apply this methodology to influenza A data. |
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| Keywords: | count data degree distribution epidemics identifiability inference influenza network models susceptible– infectious– removed– susceptible |
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