Feasibility of very short-term forecast models for COVID-19 hospital-based surveillance
Rev. Soc. Bras. Med. Trop
;
54: e07622020, 2021. tab, graf
Article
in English
| LILACS
| ID: biblio-1155525
ABSTRACT
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.
Full text:
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Index:
LILACS (Americas)
Main subject:
Coronavirus Infections
Type of study:
Prognostic study
/
Risk factors
/
Screening study
Limits:
Humans
Language:
English
Journal:
Rev. Soc. Bras. Med. Trop
Journal subject:
Tropical Medicine
Year:
2021
Type:
Article
Affiliation country:
Brazil
Institution/Affiliation country:
Universidade de São Paulo/BR
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