A Comparative Study for COVID-19 Forecasting Models

Abstract

The COVID-19 was declared as an international health emergency concern by World Health Organization (WHO) in 2020. It caused about 7 million deaths and has taken interest in various disciplines. On the other hand, modeling infectious diseases can provide critical planning to control the outbreak and public health research. In this work, we consider three classical epidemic models, namely, the SI (Susceptible, Infectious) model, SIS (Susceptible, Infectious, Susceptible) model and SIR (Susceptible, Infectious, Recovered) model to simulate the spread of COVID-19 in Türkiye. We compare their performances by applying recent data of COVID-19 outbreak. We present numerical experiments to indicate which models can reproduce the epidemic dynamics qualitatively and quantitatively for forecasting.