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.
Highlights
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
Coronaviruses are a large family of viruses which may cause respiratory infections ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS)
The growth of COVID-19 in South Africa appears to be rapid until 27 March 2020 where a total of 243 daily new cases were observed, followed by a decline in the rate of new cases
Summary
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. The South African government declared a national state of disaster on 15 March 2020 and commenced a state of lockdown from 26 March 2020 in an effort to reduce COVID-19 transmission in the country [2] During this period all international and inter-provincial borders were closed, as well as all schools and several economic sectors in the country. Based on assumptions about the disease process, public health policies, demographic and mixing patterns among individuals in the population a set of differential equations governing how individuals in the population transition from one compartment to another, are defined and solved These models are useful in understanding the effect of different factors on the transmission process and possible intervention strategies, they are sensitive to the assumptions made and require a deep understanding of the disease being modelled. The South African National COVID-19 Modeling Consortium [4], for example, assumed the following in their SEIR model: 75% of infected individuals are asymptomatic, the time from onset to infectiousness is 4 days (2∙0–9∙0), a 5 day duration of infectiousness from onset of symptoms;
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