Forecasting Hospitalizations Due to COVID-19 in South Dakota, USA.

ABSTRACT: Anticipating the number of hospital beds needed for patients with COVID-19 remains a challenge. Early efforts to predict hospital bed needs focused on deriving predictions from SIR models, largely at the level of countries, provinces, or states. In the USA, these models rely on data reported by state health agencies. However, predicting disease and hospitalization dynamics at the state level is complicated by geographic variation in disease parameters. In addition, it is difficult to make forecasts early in a pandemic due to minimal data. Bayesian approaches that allow models to be specified with informed prior information from areas that have already completed a disease curve can serve as prior estimates for areas that are beginning their curve. Here, a Bayesian non-linear regression (Wei

SUBMITTER: Wesner JS 

PROVIDER: S-EPMC8088317 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature