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