Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2024

Abstract

This paper proposes a novel graph structure to address the problems of information spreading in a real-world, frequently updating graph, with two main contributions at hand: accurately tracing infection diffusion according to fine-grained user movements and finding vulnerable vertices under the virus immunization scenario to mitigate infection diffusion. Unlike previous work that primarily predicts the long-term epidemic trend at the census level, this study aims to intervene in the short-term at the individual level. Therefore, two downstream tasks are formulated to illustrate practicalities: Epidemic Mitigating in Public Area problem (EMA) and Epidemic Maximized Spread in Public Area problem (ESA), where EMA aims to find intervention strategies, and ESA is an adversarial solution against the intervention strategy to test the robustness. Comprehensive experiments are conducted using two real-world datasets with millions of public transport trips, which demonstrate the effectiveness of our approach and highlight the importance of considering the dynamic nature of close contacts in epidemic modelling.

Keywords

Accuracy, Graph Structure, Immune system, Infection Diffusion, Pandemics, Social networking (online), Task analysis, Trajectory, Viruses (medical), COVID-19

Discipline

Databases and Information Systems | Health Information Technology

Research Areas

Data Science and Engineering

Areas of Excellence

Digital transformation

Publication

IEEE Transactions on Knowledge and Data Engineering

First Page

1

Last Page

12

ISSN

1041-4347

Identifier

10.1109/TKDE.2024.3423476

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TKDE.2024.3423476

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