Once an epidemic erupts, growth models can be used to predict the course of the outbreak and quantify its consequences. The advantages and limitations of these methods have been extensively discussed \cite{chowell2016mathematical}. Machine learning algorithms have also been utilized with the most recent application being in the current COVID-19 pandemic \cite{wang2020prediction}. Correlating the number of COVID-19 cases with parameters obtained using big data approaches can predict future rise in the number of cases. For example, monitoring of digital data streams can provide an early indication of a rise in the COVID-19 cases and deaths in the next 2 to 3 weeks \cite{Kogan2021}. All models have limitations arising from the imperfect nature of the data. The need for open, better, detailed data is imperative for the deployment of models with improved accuracy, models that will have better predictive ability and will be more useful for the timely application of appropriate control measures for the COVID-19 pandemic \cite{Vespignani_2020}.