Risk identification of major infectious disease epidemics based on complex network theory

Major infectious disease epidemic (MIDE) poses a great threat to human survival and development. It is critical to the MIDE prevention and control to figure out the risk influencing factors that may lead to MIDE outbreaks. MIDE risk identification is the starting point and the basis of risk management. This study conducts the risk identification of MIDE based on complex network theory. To this end, we create MIDE risk network and improve the classical Leaderrank algorithm with the idea of biased random wandering adopted. SLR1 algorithm and SLR2 algorithm are proposed. And SLR1 and SLR2 algorithms are compared with Leaderrank and Pagerank algorithms, based on which we select the best performing algorithm from SLR1 and SLR2 algorithms as the novel algorithm proposed in this study. And we use the best performing algorithm to complete the risk identification of MIDE. Results show that MIDE risk network has such properties as small-world and scale-free. Under targeted attacks the risk network exhibits high vulnerability. Both SLR1 and SLR2 outperform the other two algorithms, and SLR2 demonstrates the best performance. Therefore, SLR2 is used to rank the importance of risk factors. Fifteen key risk factors are identified which are related to the vulnerability of personnel, equipment, resources, environment and management, and the risk receptor exposure. The validity of SLR2 implementation in MIDE risk identification is verified from theoretical and practical perspective. This study facilitates MIDE risk reduction and thus improves MIDE risk management. What's more, the proposed SLR2 algorithm can be used for the risk identification of other disasters.