Evaluation of predictive capability of Bayesian spatio-temporal models for Covid-19 spread.

ABSTRACT:

Background

Bayesian models have been applied throughout the Covid-19 pandemic especially to model time series of case counts or deaths. Fewer examples exist of spatio-temporal modeling, even though the spatial spread of disease is a crucial factor in public health monitoring. The predictive capabilities of infectious disease models is also important.

Methods

In this study, the ability of Bayesian hierarchical models to recover different parts of the variation in disease counts is the focus. It is clear that different measures provide different views of behavior when models are fitted prospectively. Over a series of time horizons one step predictions have been generated and compared for different models (for case counts and death counts). These Bayesian SIR models were fitted

SUBMITTER: Lawson AB 

PROVIDER: S-EPMC10422743 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature