Starting in the Wuhan province of China at the end of 2019, the Corona virus 2019 (Covid-19) is a pandemic that has hit many countries worldwide including Iran, Italy, Spain, and more recently the USA, while affecting the African continent with lower caseloads. As of May 2020, South Africa has been the most affected country with the highest caseload on the African continent with Cape Town being the the epicentre of the pandemic in South Africa. It is widely recognised that preempting the pandemic rather than attempting to cure infected patients is very crucial, especially on the African continent given its poorer healthcare system compared to the more developed countries of the Western world where the pandemic has caused much more casualties despite their more advanced state of the healthcare system. This paper proposes two predictive analytic models that can be used in the mitigation against the pandemic by i) validating of the proposed protective measures through simulation modelling and ii) pre-empting the evolution of the pandemic through data analytics. The simulation modelling builds around the classic SIR model to mimic the main protective measures suggested by the world health organisation (WHO) and implemented by affected countries. The data analytics model is built around a multi linear regression machine learning model used to predict future confirmed cases based on the data currently collected. The two models were implemented using real Covid19 data of the city of Cape Town. The results revealed the accuracy of the models and the relevance of combining simulation modelling and data analytics as relevant tools in the fight against the pandemic.