Abstract INTRODUCTION: We evaluated the performance of Bayesian vector autoregressive (BVAR) and Holt’s models to forecast the weekly COVID-19 reported cases in six units of a large hospital. METHODS: Cases reported from epidemiologic weeks (EW) 12-37 were selected as the training period, and from EW 38-41 as the test period. RESULTS: The models performed well in forecasting cases within one or two weeks following the end of the time-series, but forecasts for a more distant period were inaccurate. CONCLUSIONS: Both models offered reasonable performance in very short-term forecasts for confirmed cases of COVID-19.
All Keywords
【저자키워드】 COVID-19, coronavirus disease, Epidemiology, Forecasting, statistical models, 【초록키워드】 Bayesian, hospital, confirmed case, selected, performed, reported, evaluated, offered, 【제목키워드】 Surveillance,
【저자키워드】 COVID-19, coronavirus disease, Epidemiology, Forecasting, statistical models, 【초록키워드】 Bayesian, hospital, confirmed case, selected, performed, reported, evaluated, offered, 【제목키워드】 Surveillance,