Bayesian sequential data assimilation for COVID-19 forecasting.

ABSTRACT: We introduce a Bayesian sequential data assimilation and forecasting method for non-autonomous dynamical systems. We applied this method to the current COVID-19 pandemic. It is assumed that suitable transmission, epidemic and observation models are available and previously validated. The transmission and epidemic models are coded into a dynamical system. The observation model depends on the dynamical system state variables and parameters, and is cast as a likelihood function. The forecast is sequentially updated over a sliding window of epidemic records as new data becomes available. Prior distributions for the state variables at the new forecasting time are assembled using the dynamical system, calibrated for the previous forecast. Epidemic outbreaks are non-autonomous dynamical systems d

SUBMITTER: Daza-Torres ML 

PROVIDER: S-EPMC9023479 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature