The effectiveness of intervention measures on MERS-CoV transmission by using the contact networks reconstructed from link prediction data - Kim, Eunmi; Kim, Yunhwan; Jin, Hyeonseong; Lee, Yeonju; Lee, Hyosun; Lee, Sunmi (Frontiers in Public Health, 2024)
Mitigating the spread of infectious diseases stands as a paramount concern for societal safety, with the development of effective intervention measures being pivotal in addressing this issue. Epidemic simulation serves as an efficient method for evaluating the efficacy of intervention measures, but the utilization of realistic simulation environments is essential for deriving meaningful insights. While contact-tracing data have been commonly employed in literature to construct realistic networks for simulation, they come with inherent theoretical and empirical limitations. As an alternative approach, this study explores the reconstruction of simulation networks based on contact-tracing data using link prediction methods. The primary objective of this study is to assess the effectiveness of intervention measures on the reconstructed network, using the 2015 MERS-CoV outbreak in South Korea as a case study. Contact-tracing data were obtained, and simulation networks were reconstructed utilizing the graph autoencoder (GAE)-based link prediction method, with a scale-free (SF) network used for comparison purposes.Epidemic simulations were then conducted on these networks to evaluate the effectiveness of three intervention strategies: Mass Quarantine (MQ), Isolation, and Isolation combined with Acquaintance Quarantine (AQ + Isolation). The simulation results indicated that AQ + Isolation was the most effective intervention on the GAE network, with consistent epidemic curves observed across interventions due to their high clustering coefficients. Conversely, MQ and AQ + Isolation demonstrated high effectiveness on the SF network, attributed to the network's low clustering coefficient and intervention sensitivity, while Isolation alone exhibited reduced effectiveness. These findings underscore the significant impact of network structure on intervention outcomes and suggest a potential overestimation of intervention effectiveness in SF networks. Furthermore, 1 Eunmi Kim et al.they highlight the complementary use of link prediction methods. This innovative methodology serves as inspiration for future endeavors aimed at enhancing simulation environments and provides valuable insights for informing public health decision-making processes.