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Feasibility of very short-term forecast models for COVID-19 hospital-based surveillance

Edson Zangiacomi Martinez¹, Afonso Dinis Costa Passos^1,2, Antônio Fernando Cinto²

¹Universidade de São Paulo, Faculdade de Medicina de Ribeirão Preto, Ribeirão Preto, SP, Brasil.

Revista Da Sociedade Brasileira De Medicina Tropical

|February 19, 2021

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.

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