Spatio-temporal simulation of the novel coronavirus (COVID-19) outbreak using the agent-based modeling approach (case study: Urmia, Iran)

The ongoing outbreak of the COVID-19 as the current global concern threatens lives of many people around the world. COVID-19 is highly contagious so that it has infected more than 1,848,439 people until April 14, 2020 and killed more than 117,217 people. The main aim of this study is to develop an agent-based model (ABM) that simulates the spatio-temporal outbreak of COVID-19. The main innovation of this research is investigating the impacts of various strategies of school and educational center closures, heeding social distancing, and office closures on controlling the COVID-19 outbreak in Urmia city, Iran. In this research, the outbreak of COVID-19 disease was simulated with the help of ABM so that all agents considered in the ABM along with their attributes and behaviors as well as the environment of the ABM were described. Besides, the transmission of COVID-19 between human agents was simulated based on the SEIRD model, and finally, all control strategies applied in Urmia city along with corresponding actions of each control strategy were implemented in the ABM. The results of the ABM indicated that school and educational center closures in Urmia city, reduced the number of infected people by 4.96% each week on average and 49.61% in total from February 21 until May 10. Heeding social distancing by 30% and 70% of people of Urmia city from March 27, led to decrease the number of infected people by 5.24% and 10.07% each week, on average and 31.46% and 60.44% in total, respectively, and if 30% and 70% of civil servants of Urmia city did not go to work, the number of infected people would be decreased by 3.30% and 5.25% each week, on average and 32.98% and 52.48% in total from February 21 until May 10, respectively. As a result of this research, heeding social distancing by the majority of people is recommended for Urmia city in the current situation. The main advantages of disease modeling are to investigate how the disease is likely to evolve amongst the population of society and also assess the impacts of control strategies on controlling the outbreak of disease.