Prediction of COVID-19 Spreading Profiles in South Korea, Italy and Iran by Data-Driven Coding

Choujun Zhan; Chi K. Tse; Zhikang Lai; Tianyong Hao; Jingjing Su

The spreading of the 2019 New Coronavirus Disease (COVID-19 or SARS-CoV-2) has evolved from a contagion originally confined within Wuhan, China, in December 2019, rapidly to a global contagion, which has spread to 87 countries within two months. The numbers of confirmed infection cases in South Korea, Italy and Iran have surged in the last two weeks, reaching 7,313, 5,883 and 5,823, respectively, on March 8, 2020. The global fatality rate, however, remains below 3%. In this study, we build on the result of our previous work [4] that establishes a library of parameters of an augmented SEIR model, corresponding to the historic spreading profiles of 367 cities in China. This library forms a set of profile codes that cover a variety of possible epidemic progression profiles. By comparing the early incomplete data of epidemic progression collected for a specific population with the historic profiles, we select a few candidate profiles from the historic archive using a nonlinear optimization procedure. The corresponding profile codes of the selected historic progression profiles can then be used to produce estimates of the future progression for that specific population. We apply this method to predict the spreading of COVID-19 in South Korea, Italy and Iran. Results show that the three countries will soon see infection peaks in most cities in the coming 2 to 3 weeks, with South Korea's cases reaching their peaks slightly earlier than the others. The percentage of population eventually infected will be less than 0.3%, 0.05% and 0.02% for South Korea, Italy and Iran, respectively. The epidemic is expected to end before June 2020, and depending on the effectiveness of treatment, particular cities may see full recovery or zero infection sooner or later than others. It is worth noting that the epidemic progression in South Korean cities are found to be more rapid than typical, implying that the authorities might have taken effective measures to control the spread. The predicted progressions for Italy and Iran, on the other hand, are found to display profiles that are typical of those in the historical archive, and unless more stringent measures are taken, the peaks and subsequent decline of the infection numbers will unlikely come sooner or more rapidly than the predicted trajectories. Finally, we should stress that the proposed data-driven coding method is applicable to predicting epidemic progression in any given population and the accuracy of prediction will depend on the adequacy of the available data in allowing a reliable match to be identified from the historical archive.