Capturing the time-varying drivers of an epidemic using stochastic dynamical systems

Joseph Dureau, Konstantinos Kalogeropoulos () and Marc Baguelin

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: Epidemics are often modeled using non-linear dynamical systems observed through partial and noisy data. In this paper, we consider stochastic extensions in order to capture unknown influences (changing behaviors, public interventions, seasonal effects, etc.). These models assign diffusion processes to the time-varying parameters, and our inferential procedure is based on a suitably adjusted adaptive particle Markov chain Monte Carlo algorithm. The performance of the proposed computational methods is validated on simulated data and the adopted model is applied to the 2009 H1N1 pandemic in England. In addition to estimating the effective contact rate trajectories, the methodology is applied in real time to provide evidence in related public health decisions. Diffusion-driven susceptible exposed infected retired-type models with age structure are also introduced.

Keywords: Bayesian inference; particle MCMC; population epidemic model; time-varying parameters (search for similar items in EconPapers)
JEL-codes: I1 (search for similar items in EconPapers)
Date: 2013-07
New Economics Papers: this item is included in nep-ecm and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Published in Biostatistics, July, 2013, 14(3), pp. 541-555. ISSN: 1465-4644

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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:41749

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