Building a sensible SIR estimation model for COVID-19 outspread in Kuwait

Abstract

The Susceptible - Infected - Recovered (SIR) model is used in this research to analyze and predict the outbreak of coronavirus (COVID-19) in Kuwait. The time dependent SIR model is used to model the growth of COVID-19 and to predict future values of infection and recovery rates. This research presents an analysis on the impact of the preventive measures taken by Kuwait’s local authorities to control the spread. It also empirically examines the validity of various values of R0 ranging from 2 to 5.2. The proposed model is built using Python language modules and simulated using official data of Kuwait in the period from February 24th to May 28th of 2020. Our results show the SIR model is almost fitted with the actual confirmed cases of both infection and recovery for the values of R0 ranging from 3 to 4. The results shown indicate COVID-19 peak infection rates and their anticipated dates for Kuwait. It has been observed from the obtained prediction that if preventive measures are not strictly followed, the infection numbers will grow exponentially.

Keywords

• COVID-19
• SIR model
• Regression
• Coronavirus
• Forecasting
• Logistic regression