|Cases and deaths predictions based in data driven modeling - click to open|
Additional detail regarding modelling approach
Phenomenological models have previously been applied to model disease outbreaks such as Ebola (Pell et al., 2018), Zika virus (Sebrango-Rodríguez et al., 2017) and more recently the COVID-19 outbreak (Roosa et al., 2020; Shen, 2020). We adopt a similar approach to model the COVID-19 trajectory in South Africa. Specifically, the logistic growth model, Richards model, Weibull model and Gompertz model are calibrated to the reported number of COVID-19 cases and deaths from 5 March 2020 to present, and predictions are presented for the models which provide the best fit to the data. The aforementioned models are fitted using least squares estimation. We rely on the general bootstrap method (Efron & Tibshirani, 1994) and apply a parametric bootstrapping approach which has used previously to quantify parameter uncertainty and construct confidence intervals in modeling studies(Chowell, 2017; Chowell et al., 2019) . In this method, multiple observations are repeatedly sampled from the best-fit model to quantify parameter uncertainty by assuming that the time series follows a Poisson distribution.