Modelling prevention and control strategies for COVID-19 propagation with patient contact networks

Abstract

Due to the COVID-19 outbreak, there is an urgent need to research the spread of disease and prevention strategies. As the spread of COVID-19 is closely related to the structure of human social networks, there are a lot of existing works that use a topological structure to analyze the characteristics of spread. Several studies have proposed certain strategies to prevent COVID-19 by analyzing the topological structure of the contact network, but most of the existing works have focused on detecting dense groups such as cliques; however, as the clique is the densest subgraph, it is easy for it to be influenced when the data has noise or lacks some edges. To reduce the influences of noise or lacks of data, there is a concept of γ-Quasi-Cliques is considered in this paper. γ-Quasi-Cliques is less restrictive and denser than cliques, and it is thus more suitable for analyzing and detecting communities in social networks to identify the close contacts of patients and achieve timely control under high levels of epidemic prevention strategies. Therefore, this paper proposed an algorithm based on the traditional formal concept analysis method for detecting γ-quasi-cliques, and also designed a model for detecting and mining close contacts and sub-close (secondary) contacts in the patient's contact network. Consequently, manual intervention occurs in response to the asymptomatic close or sub-close contacts detected by this model, and nucleic acid testing and home isolation are performed to prevent the widespread of COVID19. In our experiments, a real-life contact network is used to determine the ideal value of γ for the detection of quasi-clique, which is 0.6, and the results show the validity and feasibility of the model.