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 En
| LILACS
| ID: biblio-1155525
Responsible library:
BR1.1
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.Key words
Full text:
1
Index:
LILACS
Main subject:
Coronavirus Infections
Type of study:
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Limits:
Humans
Language:
En
Journal:
Rev. Soc. Bras. Med. Trop
Journal subject:
MEDICINA TROPICAL
Year:
2021
Type:
Article