Dynamic tracking with model-based forecasting for the spread of the COVID-19 pandemic

In this paper, a susceptible-infected-removed (SIR) model has been used to track the evolution of the spread of COVID-19 in four countries of interest. In particular, the epidemic model, that depends on some basic characteristics, has been applied to model the evolution of the disease in Italy, India, South Korea and Iran. The economic, social and health consequences of the spread of the virus have been cataclysmic. Hence, it is imperative that mathematical models can be developed and used to compare published datasets with model predictions. The predictions estimated from the presented methodology can be used in both the qualitative and quantitative analysis of the spread. They give an insight into the spread of the virus that the published data alone cannot, by updating them and the model on a daily basis. We show that by doing so, it is possible to detect the early onset of secondary spikes in infections or the development of secondary waves. We considered data from March to August, 2020, when different communities were affected severely and demonstrate predictions depending on the model's parameters related to the spread of COVID-19 until the end of December, 2020. By comparing the published data with model results, we conclude that in this way, it may be possible to reflect better the success or failure of the adequate measures implemented by governments and authorities to mitigate and control the current pandemic.