REAL-TIME MECHANISTIC BAYESIAN FORECASTS OF COVID-19 MORTALITY.

ABSTRACT: The COVID-19 pandemic emerged in late December 2019. In the first six months of the global outbreak, the U.S. reported more cases and deaths than any other country in the world. Effective modeling of the course of the pandemic can help assist with public health resource planning, intervention efforts, and vaccine clinical trials. However, building applied forecasting models presents unique challenges during a pandemic. First, case data available to models in real time represent a nonstationary fraction of the true case incidence due to changes in available diagnostic tests and test-seeking behavior. Second, interventions varied across time and geography leading to large changes in transmissibility over the course of the pandemic. We propose a mechanistic Bayesian model that builds upon the

SUBMITTER: Gibson GC 

PROVIDER: S-EPMC11229176 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature