Modeling COVID-19 Spread using Multi-Agent Simulation with Small-World Network Approach

Abstract

Abstract Background The rapid global spread of COVID-19 has seriously impacted people's daily lives and the social economy while also posing a threat to their lives. The analysis of infectious disease transmission is of significant importance for the rational allocation of epidemic prevention and control resources, the management of public health emergencies, and the improvement of future public health systems. Methods We propose a spatio-temporal COVID-19 transmission model with a neighborhood as an agent unit and an urban spatial network with long and short edge connections. The spreading model includes a network of defined agent attributes, transformation rules, and social relations and a small world network representing agents' social relations. Parameters for each stage are fitted by the Runge-Kutta method combined with the SEIR model. Using the NetLogo development platform, accurate dynamic simulations of the spatial and temporal evolution of the early epidemic were achieved. Results Experimental results demonstrate that the fitted curves from the four stages agree with actual data, with only a 12.27% difference between the average number of infected agents and the actual number of infected agents after simulating one hundred times. Additionally, the model simulates and compares different "city closure" scenarios. The results showed that implementing a 'lockdown' 10 days earlier would lead to the peak number of infections occurring seven days earlier than in the normal scenario, with a reduction of 40.35% in the total number of infections. Discussion The intervention of epidemic prevention measures will significantly impact the transmission of the disease, and the earlier the intervention occurs, the more pronounced the effect in suppressing the spread of the epidemic. This approach can accurately replicate actual virus transmission data and predict the epidemic's future trend based on available data so that health decision-makers may better comprehend its spread.