Inference on the dynamics of COVID-19 in the United States.

ABSTRACT: The evolution of the COVID-19 pandemic is described through a time-dependent stochastic dynamic model in discrete time. The proposed multi-compartment model is expressed through a system of difference equations. Information on the social distancing measures and diagnostic testing rates are incorporated to characterize the dynamics of the various compartments of the model. In contrast with conventional epidemiological models, the proposed model involves interpretable temporally static and dynamic epidemiological rate parameters. A model fitting strategy built upon nonparametric smoothing is employed for estimating the time-varying parameters, while profiling over the time-independent parameters. Confidence bands of the parameters are obtained through a residual bootstrap procedure. A key fe

SUBMITTER: Bhattacharjee S 

PROVIDER: S-EPMC8831615 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature