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.
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.
Authors: Juliane F Oliveira; Moreno S Rodrigues; Lacita M Skalinski; Aline E S Santos; Larissa C Costa; Luciana L Cardim; Enny S Paixão; Maria da Conceição N Costa; Wanderson K Oliveira; Maurício L Barreto; Maria Glória Teixeira; Roberto F S Andrade Journal: PLoS One Date: 2020-02-03 Impact factor: 3.240