From a timeline contact graph to close contact tracing and infection diffusion intervention

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 viru...

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Main Authors: ZHANG, Yipeng, BAO, Zhifeng, LI, Yuchen, ZHENG, Baihua, WANG, Xiaoli
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Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/sis_research/9035
https://ink.library.smu.edu.sg/context/sis_research/article/10038/viewcontent/10586786_av.pdf
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spelling sg-smu-ink.sis_research-100382024-07-25T07:57:59Z From a timeline contact graph to close contact tracing and infection diffusion intervention ZHANG, Yipeng BAO, Zhifeng LI, Yuchen ZHENG, Baihua WANG, Xiaoli 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. 2024-01-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9035 info:doi/10.1109/TKDE.2024.3423476 https://ink.library.smu.edu.sg/context/sis_research/article/10038/viewcontent/10586786_av.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Accuracy Graph Structure Immune system Infection Diffusion Pandemics Social networking (online) Task analysis Trajectory Viruses (medical) COVID-19 Databases and Information Systems Health Information Technology
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Accuracy
Graph Structure
Immune system
Infection Diffusion
Pandemics
Social networking (online)
Task analysis
Trajectory
Viruses (medical)
COVID-19
Databases and Information Systems
Health Information Technology
spellingShingle Accuracy
Graph Structure
Immune system
Infection Diffusion
Pandemics
Social networking (online)
Task analysis
Trajectory
Viruses (medical)
COVID-19
Databases and Information Systems
Health Information Technology
ZHANG, Yipeng
BAO, Zhifeng
LI, Yuchen
ZHENG, Baihua
WANG, Xiaoli
From a timeline contact graph to close contact tracing and infection diffusion intervention
description 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.
format text
author ZHANG, Yipeng
BAO, Zhifeng
LI, Yuchen
ZHENG, Baihua
WANG, Xiaoli
author_facet ZHANG, Yipeng
BAO, Zhifeng
LI, Yuchen
ZHENG, Baihua
WANG, Xiaoli
author_sort ZHANG, Yipeng
title From a timeline contact graph to close contact tracing and infection diffusion intervention
title_short From a timeline contact graph to close contact tracing and infection diffusion intervention
title_full From a timeline contact graph to close contact tracing and infection diffusion intervention
title_fullStr From a timeline contact graph to close contact tracing and infection diffusion intervention
title_full_unstemmed From a timeline contact graph to close contact tracing and infection diffusion intervention
title_sort from a timeline contact graph to close contact tracing and infection diffusion intervention
publisher Institutional Knowledge at Singapore Management University
publishDate 2024
url https://ink.library.smu.edu.sg/sis_research/9035
https://ink.library.smu.edu.sg/context/sis_research/article/10038/viewcontent/10586786_av.pdf
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