Short-term real-time prediction of total number of reported COVID-19 cases and deaths in South Africa: a data driven approach
SOURCE: BMC Medical Research Methodology
OUTPUT TYPE: Journal Article
PUBLICATION YEAR: 2021
TITLE AUTHOR(S): T.Reddy, Z.Shkedy, C.Janse van Rensburg, H.Mwambi, P.Debba, K.Zuma, S.Manda
KEYWORDS: COVID-19, DATA ANALYSIS, FORECASTING, MORTALITY, PATIENTS
DEPARTMENT: Public Health, Societies and Belonging (HSC)
Print: HSRC Library: shelf number 11834
HANDLE: 20.500.11910/15878
URI: http://hdl.handle.net/20.500.11910/15878
OUTPUT TYPE: Journal Article
PUBLICATION YEAR: 2021
TITLE AUTHOR(S): T.Reddy, Z.Shkedy, C.Janse van Rensburg, H.Mwambi, P.Debba, K.Zuma, S.Manda
KEYWORDS: COVID-19, DATA ANALYSIS, FORECASTING, MORTALITY, PATIENTS
DEPARTMENT: Public Health, Societies and Belonging (HSC)
Print: HSRC Library: shelf number 11834
HANDLE: 20.500.11910/15878
URI: http://hdl.handle.net/20.500.11910/15878
If you would like to obtain a copy of this Research Output, please contact Hanlie Baudin at researchoutputs@hsrc.ac.za.
Abstract
The rising burden of the ongoing COVID-19 epidemic in South Africa has motivated the application of modeling strategies to predict the COVID-19 cases and deaths. Reliable and accurate short and long-term forecasts of COVID-19 cases and deaths, both at the national and provincial level, are a key aspect of the strategy to handle the COVID-19 epidemic in the country. In this paper we apply the previously validated approach of phenomenological models, fitting several nonlinear growth curves (Richards, 3 and 4 parameter logistic, Weibull and Gompertz), to produce short term forecasts of COVID-19 cases and deaths at the national level as well as the provincial level. Using publicly available daily reported cumulative case and death data up until 22 June 2020, we report 5, 10, 15, 20, 25 and 30-day ahead forecasts of cumulative cases and deaths. All predictions are compared to the actual observed values in the forecasting period. We observed that all models for cases provided accurate and similar short-term forecasts for a period of 5days ahead at the national level, and that the three and four parameter logistic growth models provided more accurate forecasts than that obtained from the Richards model 10 days ahead. However, beyond 10 days all models underestimated the cumulative cases. Our forecasts across the models predict an additional 23,551-26,702 cases in 5 days and an additional 47,449-57,358 cases in 10 days. While the three parameter logistic growth model provided the most accurate forecasts of cumulative deaths within the 10 day period, the Gompertz model was able to better capture the changes in cumulative deaths beyond this period. Our forecasts across the models predict an additional145-437 COVID-19 deaths in 5 days and an additional 243-947 deaths in 10 days. : By comparing both the predictions of deaths and cases to the observed data in the forecasting period, we found that this modeling approach provides reliable and accurate forecasts for a maximum period of 10 days ahead.-
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