Capturing the time-varying drivers of an epidemic using stochastic dynamical systems
AbstractEpidemics 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.
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Bibliographic InfoPaper provided by London School of Economics and Political Science, LSE Library in its series LSE Research Online Documents on Economics with number 41749.
Date of creation: Jul 2013
Date of revision:
Publication status: Published in Biostatistics, July, 2013, 14(3), pp. 541-555. ISSN: 1465-4644
Bayesian inference; particle MCMC; population epidemic model; time-varying parameters;
Find related papers by JEL classification:
- I1 - Health, Education, and Welfare - - Health
This paper has been announced in the following NEP Reports:
- NEP-ALL-2014-03-08 (All new papers)
- NEP-ECM-2014-03-08 (Econometrics)
- NEP-ORE-2014-03-08 (Operations Research)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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