ArticleBMC medical research methodology2021
Short-term real-time prediction of total number of reported COVID-19 cases and deaths in South Africa: a data driven approach.
Article in BMC medical research methodology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Endemic-epidemic modeling of the COVID-19 pandemic in South Africa.BMC infectious diseases · 2026Article
- Developing predictive models for COVID-19 positive tests based on the XGBoost and random forest algorithms with internet search data.BMC public health · 2025Article
- A reliable emergency logistics network for COVID-19 considering the uncertain time-varying demands.Transportation research. Part E, Logistics and transportation review · 2023Article
- Modeling the positive testing rate of COVID-19 in South Africa using a semi-parametric smoother for binomial data.Frontiers in public health · 2023Article
- Article
- Estimating the risk of SARS-CoV-2 deaths using a Markov switching-volatility model combined with heavy-tailed distributions for South Africa.BMC public health · 2022Article
- Virtual healthcare services and digital health technologies deployed during coronavirus disease 2019 (COVID-19) pandemic in South Africa: a systematic review.Global health journal (Amsterdam, Netherlands) · 2022Review
- Inter-Country COVID-19 Contagiousness Variation in Eight African Countries.Frontiers in public health · 2022Article
- A Spatial Analysis of COVID-19 in African Countries: Evaluating the Effects of Socio-Economic Vulnerabilities and Neighbouring.International journal of environmental research and public health · 2021Article
- Predictive models for COVID-19 cases, deaths and recoveries in Algeria.Results in physics · 2021Article
- The role of airborne particles and environmental considerations in the transmission of SARS-CoV-2.Geoscience frontiers · 2021Review
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
methodsIn this paper we apply the previously validated approach of phenomenological models, fitting several non-linear 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.
resultsWe observed that all models for cases provided accurate and similar short-term forecasts for a period of 5 days 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 additional 145-437 COVID-19 deaths in 5 days and an additional 243-947 deaths in 10 days.
conclusionsBy 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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