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

Dureau, Joseph, Kalogeropoulos, Konstantinos ORCID: 0000-0002-0330-9105 and Baguelin, Marc (2013) Capturing the time-varying drivers of an epidemic using stochastic dynamical systems. Biostatistics, 14 (3). pp. 541-555. ISSN 1465-4644

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Identification Number: 10.1093/biostatistics/kxs052


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.

Item Type: Article
Official URL:
Additional Information: © 2013 The Authors
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
JEL classification: I - Health, Education, and Welfare > I1 - Health
Date Deposited: 30 May 2013 10:59
Last Modified: 11 Jun 2024 19:18
Funders: Statistics department of the London School of Economics

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