The effectiveness of intervention measures on Mers-CoV transmission by using the contact networks reconstructed from link prediction data
Bibliographic Data
| ID | 22073125 |
|---|---|
| Authors | Eunmi Kim (0000-0003-2287-2902, Ewha Womans University), Yunhwan Kim (0000-0003-1138-6802, Kookmin University), Hyeonseong Jin (Jeju National University), Yeonju Lee (0000-0002-0314-6238), Yeon-Ju Lee (0000-0002-1550-571X, Korea University), Hyosun Lee (0000-0002-0107-3100, Kyung Hee University), Sunmi Lee (0000-0003-1126-0660, Kyung Hee University, corresponding author) |
| Year | 2024 |
| Volume | 12 |
| Pages | 1386495-1386495 |
| Publication date | 2024-05-17 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2024.1386495 |
| PMID | 38827618 |
| OpenAlex | W4397016580 |
| Language | EN |
| References cited | 48 |
Introduction: Mitigating the spread of infectious diseases is of paramount concern for societal safety, necessitating the development of effective intervention measures. Epidemic simulation is widely used to evaluate the efficacy of such measures, but realistic simulation environments are crucial for meaningful insights. Despite the common use of contact-tracing data to construct realistic networks, they have inherent limitations. This study explores reconstructing simulation networks using link prediction methods as an alternative approach. Methods: The primary objective of this study is to assess the effectiveness of intervention measures on the reconstructed network, focusing on the 2015 MERS-CoV outbreak in South Korea. Contact-tracing data were acquired, and simulation networks were reconstructed using the graph autoencoder (GAE)-based link prediction method. A scale-free (SF) network was employed for comparison purposes. Epidemic simulations were conducted to evaluate three intervention strategies: Mass Quarantine (MQ), Isolation, and Isolation combined with Acquaintance Quarantine (AQ + Isolation). Results: Simulation results showed that AQ + Isolation was the most effective intervention on the GAE network, resulting in consistent epidemic curves due to high clustering coefficients. Conversely, MQ and AQ + Isolation were highly effective on the SF network, attributed to its low clustering coefficient and intervention sensitivity. Isolation alone exhibited reduced effectiveness. These findings emphasize the significant impact of network structure on intervention outcomes and suggest a potential overestimation of effectiveness in SF networks. Additionally, they highlight the complementary use of link prediction methods. Discussion: This innovative methodology provides inspiration for enhancing simulation environments in future endeavors. It also offers valuable insights for informing public health decision-making processes, emphasizing the importance of realistic simulation environments and the potential of link prediction methods
Computer network · Psychiatry · Telecommunications · Advanced MIMO Systems Optimization · Bioinformatics and Genomic Networks · Complex Network Analysis Techniques · Computer Science · Medicine
Targeted Social Distancing Designs for Pandemic Influenza
Line
DeepWalk
Link prediction in complex networks
Epidemic Spreading in Scale-Free Networks
Scale-Free Networks
Networks and epidemic models
Node2vec
Reconstructing the social network of HIV key populations from locally observed information
Effectiveness of Intervention Strategies on Mers-CoV Transmission Dynamics in South Korea, 2015
Exploration of Superspreading Events in 2015 Mers-CoV Outbreak in Korea by Branching Process Models
Economic analysis of pandemic influenza mitigation strategies for five pandemic severity categories
Influences on influenza transmission within terminal based on hierarchical structure of personal contact network
| Citation velocity | historical |
|---|---|
| Highly cited | No |