Modeling COVID-19 spread using multi-agent simulation with small-world network approach

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 spatiotemporal 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 1 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 7 days earlier than in the normal scenario, with a reduction of 40.35% in the total number of infections. Discussion Our methodology emphasizes the crucial role of timely epidemic interventions in curbing the spread of infectious diseases, notably in the predictive assessment and evaluation of lockdown strategies. Furthermore, this approach adeptly forecasts the influence of varying intervention timings on peak infection rates and total case numbers, accurately reflecting real-world virus transmission patterns. This highlights the importance of proactive measures in diminishing epidemic impacts. It furnishes a robust framework, empowering policymakers to refine epidemic response strategies based on a synthesis of predictive modeling and empirical data.

Agent-based modeling to estimate the impact of lockdown scenarios and events on a pandemic exemplified on SARS-CoV-2

“…One disadvantage of this approach is that it is unable to simulate the correct number of specific places and to define agent roles in the same way as in ABM models for specific places like Florida 31 or specific dependencies like resources and factories 34 . Furthermore, it is not capable of simulating small-world networks 33 or of analyzing the spread of the virus to other cities through human mobility 32 . This model does not focus on pharmaceutical interventions or vaccinations.…”

“…The current ABM models from Florida utilise synthetic maps to simulate the location of Ocala 31 or base their ABM assumptions on resource-dependent communities like Alaska, focusing on factories 34 . Other ABMs focus on the spread of COVID-19, influenced by human mobility 32 or expand their model to small-world networks 33 . The objective of our model is to be as abstract as possible at the scale of small cities, focusing on the opening or closing of specific places such as events.…”

Phylogenetic inference of inter-population transmission rates for infectious diseases

“…This model can be readily expanded into more intricate epidemiological models. For instance, it can adopt the SEIR model framework, which, compared to the SIR model, provides a more accurate representation of infectious diseases with discernible incubation periods [ 25 ]. However, since infection and recovery rates are two pivotal parameters in the formula of estimating inter-population transmission rates, the multi-population SIR model is deemed sufficient for accurately estimating these rates.…”