Sentinel nodes identification for infectious disease surveillance on temporal social networks

Active surveillance, which aims at detecting and controlling infectious diseases at an early stage, is essential to prevent the spread of infections, protect people’s health, and promote social good. One difficult problem in active surveillance is how to intelligently sample a small group of nodes as sentinels from a large number of individuals for detecting the outbreaks of infectious diseases as early as possible. To sample sentinels, the existing methods depending on the global information about a social network are infeasible for mapping out social connections is time-consuming and inaccurate. Instead, some existing studies utilize local information about individuals’ connected neighbors to heuristically select sentinels. However, few of them take into account the temporal structure of social connections, which is believed to have a direct effect on the spread of infectious diseases. In this paper, we propose two temporal-network surveillance strategies for selecting sentinels based on the friendship paradox theory, a sociological theory describing a phenomenon in social networks that most people have fewer friends than their friends have. By simulating our strategies with three existing strategies based on the susceptible-infected (SI) model, the results show that our proposed 1^stAN and 2^ndRN strategies can detect the outbreak of infectious diseases earlier than the other strategies on the synthetic temporal network and two real-world temporal social networks, respectively.