Impact of agent-based intervention strategies on the COVID-19 pandemic in large-scale dynamic contact networks

Abstract

Many intervention strategies, such as patient isolation and contact tracking, had been implemented in different countries to slow down and control the spread because of the enormous threats and losses caused by COVID-19. Since the contact relationships of millions of people within cities change over time, it is difficult to accurately predict the dynamics of large-scale outbreaks and evaluate the impact of the contact tracking and isolation strategy based on individual contact history on epidemic transmission. Here, we propose a non-markov spreading model based on individual contact dynamic network, to simulate the dynamic contact processes in a city with millions of people. In this model, the historical contact population of each infected person can be backtracked and tracked. Our model can accurately describe the COVID-19 epidemic in Wuhan and Hong Kong. We assess the impact of four agent-based epidemic intervention strategies: travel control, contact tracking and isolation, vaccination, and regular nucleic acid testing to all residents on the epidemic evolution and economic losses. We find that for the original SARS-CoV-2 virus, a strict travel control strategy is effective in both suppressing the spread of COVID-19 and minimizing economic losses. For the Omicron variant (BA.2) with stronger infectious capacity, a relatively loose travel control and an appropriate combination of the other three strategies can effectively control the epidemic outbreak while minimize economic losses. This paper provides an efficient framework for assessing the combination of different agent-based strategies by large-scale simulations in the case of unknown historical contact information of large populations, and the studies on different combinations of control strategies can provide theoretical guidance for future prevention and control.