A sliding window approach to optimize the time-varying parameters of a spatially-explicit and stochastic model of COVID-19.

ABSTRACT: The implementation of non-pharmaceutical public health interventions can have simultaneous impacts on pathogen transmission rates as well as host mobility rates. For instance, with SARS-CoV-2, masking can influence host-to-host transmission, while stay-at-home orders can influence mobility. Importantly, variations in transmission rates and mobility patterns can influence pathogen-induced hospitalization rates. This poses a significant challenge for the use of mathematical models of disease dynamics in forecasting the spread of a pathogen; to create accurate forecasts in spatial models of disease spread, we must simultaneously account for time-varying rates of transmission and host movement. In this study, we develop a statistical model-fitting algorithm to estimate dynamic rates of SARS-Co

SUBMITTER: Ratnavale S 

PROVIDER: S-EPMC10021528 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature