Feasibility of very short-term forecast models for COVID-19 hospital-based surveillance
Rev. Soc. Bras. Med. Trop
;
54: e07622020, 2021. tab, graf
Artículo
en Inglés
| 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.
Texto completo:
Disponible
Índice:
LILACS (Américas)
Asunto principal:
Infecciones por Coronavirus
Tipo de estudio:
Estudio pronóstico
/
Factores de riesgo
/
Estudio de tamizaje
Límite:
Humanos
Idioma:
Inglés
Revista:
Rev. Soc. Bras. Med. Trop
Asunto de la revista:
Medicina Tropical
Año:
2021
Tipo del documento:
Artículo
País de afiliación:
Brasil
Institución/País de afiliación:
Universidade de São Paulo/BR
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