Accounting for non-stationarity in epidemiology by embedding time-varying parameters in stochastic models.

ABSTRACT: The spread of disease through human populations is complex. The characteristics of disease propagation evolve with time, as a result of a multitude of environmental and anthropic factors, this non-stationarity is a key factor in this huge complexity. In the absence of appropriate external data sources, to correctly describe the disease propagation, we explore a flexible approach, based on stochastic models for the disease dynamics, and on diffusion processes for the parameter dynamics. Using such a diffusion process has the advantage of not requiring a specific mathematical function for the parameter dynamics. Coupled with particle MCMC, this approach allows us to reconstruct the time evolution of some key parameters (average transmission rate for instance). Thus, by capturing the time-var

SUBMITTER: Cazelles B 

PROVIDER: S-EPMC6110518 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature