A Likelihood Approach for Real-Time Calibration of Stochastic Compartmental Epidemic Models.

ABSTRACT: Stochastic transmission dynamic models are especially useful for studying the early emergence of novel pathogens given the importance of chance events when the number of infectious individuals is small. However, methods for parameter estimation and prediction for these types of stochastic models remain limited. In this manuscript, we describe a calibration and prediction framework for stochastic compartmental transmission models of epidemics. The proposed method, Multiple Shooting for Stochastic systems (MSS), applies a linear noise approximation to describe the size of the fluctuations, and uses each new surveillance observation to update the belief about the true epidemic state. Using simulated outbreaks of a novel viral pathogen, we evaluate the accuracy of MSS for real-time parameter e

SUBMITTER: Zimmer C 

PROVIDER: S-EPMC5240920 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature