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.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7891562PMC
http://dx.doi.org/10.1590/0037-8682-0762-2020DOI Listing

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