Short-term forecasting of confirmed daily COVID-19 cases in the Southern African Development Community region.
Afr Health Sci
; 22(4): 534-550, 2022 Dec.
Artículo
en Inglés
| MEDLINE | ID: covidwho-2202269
ABSTRACT
Background:
The coronavirus pandemic has resulted in complex challenges worldwide, and the Southern African Development Community (SADC) region has not been spared. The region has become the epicentre for coronavirus in the African continent. Combining forecasting techniques can help capture other attributes of the series, thus providing crucial information to address the problem.Objective:
To formulate an effective model that timely predicts the spread of COVID-19 in the SADC region.Methods:
Using the Quantile regression approaches; linear quantile regression averaging (LQRA), monotone composite quantile regression neural network (MCQRNN), partial additive quantile regression averaging (PAQRA), among others, we combine point forecasts from four candidate models namely, the ARIMA (p, d, q) model, TBATS, Generalized additive model (GAM) and a Gradient Boosting machine (GBM).Results:
Among the single forecast models, the GAM provides the best model for predicting the spread of COVID-19 in the SADC region. However, it did not perform well in some periods. Combined forecasts models performed significantly better with the MCQRNN being the best (Theil's U statistic=0.000000278).Conclusion:
The findings present an insightful approach in monitoring the spread of COVID-19 in the SADC region. The spread of COVID-19 can best be predicted using combined forecasts models, particularly the MCQRNN approach.Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
COVID-19
Tipo de estudio:
Estudio observacional
/
Estudio pronóstico
Límite:
Humanos
Idioma:
Inglés
Revista:
Afr Health Sci
Asunto de la revista:
Medicina
/
Servicios de Salud
Año:
2022
Tipo del documento:
Artículo
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