Spatio-temporal agent based simulation of COVID-19 disease and investigating the effect of vaccination (case study: Urmia)

Proper management of epidemic diseases such as Covid-19 is very important because of its effects on the economy, culture and society of nations. By applying various control strategies such as closing schools, restricting night traffic and mass vaccination program, the spread of this disease has been somewhat controlled but not completely stopped. The main goal of this research is to provide a flexible spatio-temporal model for simulating the spread of the Covid-19 disease in order to investigate and evaluate the effectiveness of vaccination. For this purpose, the combination of Agent Based Modelling (ABM) with changeable input parameters and Geospatial Information System (GIS) has been used. The disease spreads through the interaction of the designed agents with each other and with the environment, with the help of the SEIRD epidemic model, and the characteristics of the agents are monitored during the simulation period. To evaluate the model, the real data of patients with the disease in Urmia city from the time of the outbreak to 140 days later were used. The results show that the implemented model simulates the spread of the disease with MAPE= 32.86% and NRMSE= 8.62%. By simulating the vaccination implementation plan, the total number of infected people will decrease by 36.12% and the total number of deaths will decrease by 44.48%. Comparison of simulation outputs and real data shows a similarity of 82% between model results and reality. The result of this research shows that agent based modelling has been able to simulate the spread of the corona virus to an acceptable extent and evaluate the control strategies effectively; Therefore, agent based models can be used to simulate the spread of different variants of the Corona virus and other epidemic diseases, as well as to simulate the environment's response and control strategies.