A multi-scale agent-based model of infectious disease transmission to assess the impact of vaccination and non-pharmaceutical interventions: The COVID-19 case

Mathematical and computational models are useful tools for virtual policy experiments on infectious disease control. Most models fail to provide flexible and rapid simulation of various epidemic scenarios for policy assessment. This paper establishes a multi-scale agent-based model to investigate the infectious disease propagation between cities and within a city using the knowledge from person-to-person transmission. In the model, the contact and infection of individuals at the micro scale where an agent represents a person provide insights for the interactions of agents at the meso scale where an agent refers to hundreds of individuals. Four cities with frequent population movements in China are taken as an example and actual data on traffic patterns and demographic parameters are adopted. The scenarios for dynamic propagation of infectious disease with no external measures are compared versus the scenarios with vaccination and non-pharmaceutical interventions. The model predicts that the peak of infections will decline by 67.37% with 80% vaccination rate, compared to a drop of 89.56% when isolation and quarantine measures are also in place. The results highlight the importance of controlling the source of infection by isolation and quarantine throughout the epidemic. We also study the effect when cities implement inconsistent public health interventions, which is common in practical situations. Based on our results, the model can be applied to COVID-19 and other infectious diseases according to the various needs of government agencies.