APPLICATION OF STOCHASTIC MODEL FOR LENGTHY EPIDEMIC FORECASTING

Abstract

In this paper we consider a stochastic discrete-time epidemic model, with the infectivity depending on the age of infection and existing formula for the maximum likelihood estimation of the parameter responsible for the rate of the infection spread. In order to utilize the real number of infection cases statistics, a detection rate parameter is introduced. A program for automatic parameter estimation using past data with future epidemic simulation is developed. We present the comparison between the simulation of COVID-19 cases in Kyiv and real data using manual and automatic parameter estimation. We consider the possibility of the epidemic partition into several intervals with different parameters in order to simulate lengthy epidemics with significant changes in dynamics. We present the comparison between different numbers of partitions for long-term COVID-19 simulation in Kyiv (Ukraine) and Czech Republic, which have different dynamics of the epidemic development.