Modelling The Positive Testing Rate of Covid-19 in South Africa Using a Semi-Parametric Smoother for Binomial Data

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Abstract Background: The current outbreak of COVID-19 is a major pandemic that has adversely affected the world economies, societies and also an increase in health burdens within a short time. South Africa has the highest number of confirmed COVID-19 cases in Africa and is the fourteenth most affected country in the world. Understanding the country’s COVID-19 infection rate will help in preventing the spread of the disease. Method: We propose to estimate both the the positive testing rate and the rate at which the positive testing rate changes over time using a flexible semi-parametric model. We used publicly available data collected from March 5th to September 2nd 2020. Results: We found that the positive testing rate was declining from early March when the disease was first observed until early May. In the month of July 2020, the infection reached its peak then its started to decrease again. Conclusion: The observed increase in the positive testing rates between May and July 2020 could imply that the testing algorithm was effective; however its effectiveness declined after the end of July 2020, which coincided with the end of winter season in the country.

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