Background: Many countries in the world are still struggling to control COVID-19 pandemic. As of April 28, 2020, South Africa reported the highest number of COVID-19 cases in Sub-Sahara Africa. The country took aggressive steps to control the spread of the virus including setting a national command team for COVID-19 and putting the country on a complete lockdown for more than 100 days. Evidence across most countries has shown that, it is vital to monitor the progression of pandemics and assess the effects of various public health measures, such as lockdowns. Countries need to have scientific tools to assist in monitoring and assessing the effectiveness of mitigation interventions. The objective of this study was thus to assess the extent to which a systems dynamics model can forecast COVID-19 infections in South Africa and be a useful tool in evaluating government interventions to manage the epidemic through ‘what if’ simulations.Design and Methods: This study presents a systems dynamics model (SD) of the COVID-19 infection in South Africa, as one of such tools. The development of the SD model in this study is grounded in design science research which fundamentally builds on prior research of modelling complex systems.Results: The SD model satisfactorily replicates the general trend of COVID-19 infections and recovery for South Africa within the first 100 days of the pandemic. The model further confirms that the decision to lockdown the country was a right one, otherwise the country’s health capacity would have been overwhelmed. Going forward, the model predicts that the level of infection in the country will peak towards the last quarter of 2020, and thereafter start to decline. Conclusions: Ultimately, the model structure and simulations suggest that a systems dynamics model can be a useful tool in monitoring, predicting and testing interventions to manage COVID-19 with an acceptable margin of error. Moreover, the model can be developed further to include more variables as more facts on the COVID-19 emerge.

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Maize is a staple crop in South Africa and is mainly grown under rain-fed conditions. Rain-fed agricultural production is heavily reliant on rainfall during the planting season. Information on rainy season characteristics is of utmost importance as it guides farmers in preparing for the upcoming season. The study investigated rainy season characteristics for the Luvuvhu River Catchment with reference to rain-fed maize production. Historical daily rainfall data were obtained from 12 weather stations for the period 1923–2015. Instant+ statistical software was used to compute onset, false onset, cessation and length of the rainy season. The trends in rainy season characteristics were analysed using the Spearman rank correlation test. Onset of the rainy season can be expected from the first week of October to the third week of January, while cessation can be expected from the first week of February to the first week of May. The length of the rainy season ranged from 67 to 203 days. Seasonal rainfall ranged from 182 to 1 535 mm. Phafuri, Sigonde, Phunda Maria and Folovhodwe had a higher probability of false onset. No significant changes in rainy season characteristics at a 5% level of significance were observed. There was a strong correlation between onset and length of the rainy season. Based on rainfall patterns only, Phafuri, Sigonde and Folovhodwe might not be suitable for maize production under the current climate. The most favourable sites for maize production within the catchment are Entabeni, Levubu, Lwamondo, Thathe, Tshiombo and Vreemedeling. The findings of this study have implications on agricultural activities and food security as maize is a staple crop in the Luvuvhu River catchment area. Information on rainy season characteristics may therefore help in strengthening food security.