A data-driven tool for tracking and predicting the course of COVID-19 epidemic as it evolves

For an emergent disease, such as Covid-19, with no past epidemiological data to guide models, modelers struggle to make predictions of the course of the epidemic (1). Policy decisions depend on such predictions but they vary widely. On the other hand much empirical information is already contained in the data of evolving epidemiological profiles. We show, both with evidence from data, and theoretically, how the ratio of daily infected and recovered cases can be used to track and predict the course of the epidemic. Ability to predict the turning points and the end of the epidemic is of crucial importance for fighting the epidemic and planning for a return to normalcy. The accuracy of the prediction of the peaks of the epidemic is validated using data in different regions in China showing the effects of different levels of quarantine. The validated tool can be applied to other countries where Covid-19 has spread, and generally to future epidemics. A preliminary prediction for South Korea is made with limited data, with end of the epidemic as early as the second week of April, surprisingly.