Modeling COVID-19 in Iran using Particle Swarm Optimization algorithm

Sahafizadeh, E.; Khajeian, M.

Background: The first confirmed cases of COVID-19 in Iran were reported on February 19, 2020. The coronavirus expanded rapidly in all Iranian provinces and three waves of COVID-19 cases have been observed since the pandemic took effect and the fourth wave of Covid-19 cases will likely be observed soon. This study aimed to model the spread of COVID-19 in Iran and to estimate the epidemic parameters and to predict the short-term future trend of COVID-19 in Iran. Methods: We proposed a modified SEIR epidemic spreading model and we used data from February 20, 2020, to April 9, 2021, on the number of cases reported by Iranian governments to fit the proposed model on the reported data. Particle Swarm Optimization (PSO) algorithm was employed to estimate the parameters of the proposed model and the numerical simulation results were obtained by Runge-Kutta method. The estimated parameters were employed to calculate the effective reproduction number and to predict the short-term future trends of COVID-19 cases. Results: The results indicated that the effective reproduction number has increased during Nowruz (Persian New Year) and it was estimated to be 1.28. Considering only two exposed cases as the initial cases in the model, the cumulative number of exposed cases was estimated to be 15,252,372 individuals since the beginning of the outbreak. The prediction of the short-term future trends of COVID-19 cases with different scenarios showed that another peak of the pandemic cases occurs in the next weeks. By immediate lockdown implementation the number of active infected cases was estimated to be 397,585. Conclusion: Different scenarios of short-term prediction of the future trends of COVID-19 cases indicated that immediate strict social distancing policies need to be implemented to prevent a tremendous burden of the fourth major wave of COVID-19 infections on the health care system of Iran.