A Spatio-Temporal Modeling Framework for Surveillance Data of Multiple Infectious Pathogens with Small Laboratory Validation Sets.

ABSTRACT: Many surveillance systems of infectious diseases are syndrome-based, capturing patients by clinical manifestation. Only a fraction of patients, mostly severe cases, undergo laboratory validation to identify the underlying pathogen. Motivated by the need to understand transmission dynamics and associate risk factors of enteroviruses causing the hand, foot and mouth disease (HFMD) in China, we developed a Bayesian spatio-temporal modeling framework for surveillance data of infectious diseases with small validation sets. A novel approach was proposed to sample unobserved pathogen-specific patient counts over space and time and was compared to an existing sampling approach. The practical utility of this framework in identifying key parameters was assessed in simulations for a range of realisti

SUBMITTER: Tang X 

PROVIDER: S-EPMC6959856 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature