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

25Citations
Citations of this article
94Readers
Mendeley users who have this article in their 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. © 2013 The Author. Published by Oxford University Press. All rights reserved.

Cite

CITATION STYLE

APA

Dureau, J., Kalogeropoulos, K., & Baguelin, M. (2013). Capturing the time-varying drivers of an epidemic using stochastic dynamical systems. Biostatistics, 14(3), 541–555. https://doi.org/10.1093/biostatistics/kxs052

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free