2012/03/27 by Joseph Dureau, Κωνσταντίνος Καλογερόπουλος, Dureau, Joseph +4 · 5 citations
Decision Sciences · Mathematics · Medicine · Social Sciences · #Applications (stat.AP) #COVID-19 epidemiological studies #Computation (stat.CO) #FOS: Computer and information sciences #Health disparities and outcomes #Mathematical and Theoretical Epidemiology and Ecology Models #Methodology (stat.ME) #demographic modeling and climate adaptation
paper · pdf · doi:10.48550/arxiv.1203.5950
openalex publication_date 2012/03/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Epidemics are often modelled using non-linear dynamical systems observed\nthrough partial and noisy data. In this paper, we consider stochastic\nextensions in order to capture unknown influences (changing behaviors, public\ninterventions, seasonal effects etc). These models assign diffusion processes\nto the time-varying parameters, and our inferential procedure is based on a\nsuitably adjusted adaptive particle MCMC algorithm. The performance of the\nproposed computational methods is validated on simulated data and the adopted\nmodel is applied to the 2009 H1N1 pandemic in England. In addition to\nestimating the effective contact rate trajectories, the methodology is applied\nin real time to provide evidence in related public health decisions. Diffusion\ndriven SEIR-type models with age structure are also introduced.\n