From A Timeline Contact Graph to Close Contact Tracing and Infection Diffusion Intervention

DOI number:

10.1109/TKDE.2024.3423476

Affiliation of Author(s):

Singapore Management University

Journal:

IEEE Transactions on Knowledge and Data Engineering (TKDE)

Place of Publication:

America

Key Words:

Graph Structure;Infection Diffusion

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.

First Author:

Yipeng Zhang

Correspondence Author:

Xiaoli Wang

Co-author:

Zhifen Bao,Yuchen Li,Baihua ZHENG

Indexed by:

Original Research

Discipline:

计算机科学 / 数据工程 / 图网络 / 传播模型 /公共卫生干预

Document Type:

Journal Article

Volume:

36

Issue:

12

ISSN No.:

1041-4347

Translation or Not:

no

Date of Publication:

2024-07-14