A study on the efficiency of the estimation models of COVID-19

Today, the world is fighting against a dangerous epidemic caused by the novel coronavirus, also known as COVID-19. All have been impacted and countries are trying to recover from the social, economic, and health devastations of COVID-19. Recent epidemiology research has concentrated on using different prediction models to estimate the numbers of infected, recovered, and deceased cases around the world. This study is primarily focused on evaluating two common prediction models: Susceptible - Infected - Recovered (SIR) and Susceptible - Exposed - Infected - Recovered (SEIR). The SIR and SEIR models were compared in estimating the outbreak and identifying the better fitting model for forecasting future spread in Kuwait. Based on the results of the comparison, the SEIR model was selected for predicting COVID-19 infected, recovered, and cumulative cases. The data needed for estimation was collected from official sites of the Kuwait Government between 24 February and 1 December 2020. This study presents estimated values for peak dates and expected eradication of COVID-19 in Kuwait. The proposed estimation model is simulated using the Python Programming language on the collected data. The simulation was performed with various basic reproduction numbers (between 5.2 and 3), the initial exposed population, and the incubation rate. The results show that the SEIR model was better suited than the SIR model for predicting both infection and recovery cases with $$ values ranging from 3 to 4, $$ and $$ .