Modeling time to event for epicenters of the pandemic COVID-19

Abstract

The epidemic of COVID-19 has been the most mathematically informative pandemic. The unprecedented information gives rise to some unprecedented models, problems, and discussions. One of these new matters is modeling the epicenters of a pandemic. The present paper is the first attempt to model the waiting time to introduce a new epicenter during a pandemic. This modeling is conducted in terms of time-to-event, the number of epicenters, and the normalized time. We model the waiting time data by an exponential distribution, therefore, the number of epicenters can be represented through a Poisson process. Then, the parameters are estimated by the method of moments and maximum likelihood method. All the simulations are the result of 10,000 runs conducted on MATLAB R2015b. It is expected to encounter 12 and 14 (with probability 95%, 3-24 and 7-23) epicenters from 15th May to 13th June and from June 14 to July 12, 2020, respectively. We forecast that the cumulative number of confirmed cases for coming epicenters is over 10,000 when they join the existing epicenters. The paper suggests that the time to epicenter is a suitable criterion to compare the spreading speed of an epidemic in different periods or even different epidemics. Highlights The study aims to model the time to the next epicenters during the pandemic COVID-19. The study introduces the time to epicenter as a criterion to study of spreading speed of an epidemic in different periods or compare different epidemics. The study deals with the number of cumulative confirmed cases at the time that a region become epicenter. The study proposes the Poisson process as the model to describe the number of epicenters. The study suggests that exponential distribution can model the time to event for the epicenters of COVID-19.