Journal
BIOSTATISTICS
Volume 14, Issue 3, Pages 541-555Publisher
OXFORD UNIV PRESS
DOI: 10.1093/biostatistics/kxs052
Keywords
Bayesian inference; Particle MCMC; Population epidemic model; Time-varying parameters
Funding
- Statistics department of the London School of Economics
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available