research-article

Modeling Individual-Level Infection Dynamics Using Social Network Information

Authors:

Suppawong Tuarob,

Conrad S. Tucker,

Marcel Salathe,

Nilam RamAuthors Info & Claims

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

Pages 1501 - 1510

https://doi.org/10.1145/2806416.2806575

Published: 17 October 2015 Publication History

Abstract

Epidemic monitoring systems engaged in accurate discovery of infected individuals enable better understanding of the dynamics of epidemics and thus may promote effective disease mitigation or prevention. Currently, infection discovery systems require either physical participation of potential patients or provision of information from hospitals and health-care services. While social media has emerged as an increasingly important knowledge source that reflects multiple real world events, there is only a small literature examining how social media information can be incorporated into computational epidemic models. In this paper, we demonstrate how social media information can be incorporated into and improve upon traditional techniques used to model the dynamics of infectious diseases. Using flu infection histories and social network data collected from 264 students in a college community, we identify social network signals that can aid identification of infected individuals. Extending the traditional SIRS model, we introduce and illustrate the efficacy of an Online-Interaction-Aware Susceptible-Infected-Recovered-Susceptible (OIA-SIRS) model based on four social network signals for modeling infection dynamics. Empirical evaluations of our case study, flu infection within a college community, reveal that the OIA-SIRS model is more accurate than the traditional model, and also closely tracks the real-world infection rates as reported by CDC ILINet and Google Flu Trend.

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Ye YPandey ABawden CSumsuzzman DRajput RShoukat ASinger BMoghadas SGalvani A(2025)Integrating artificial intelligence with mechanistic epidemiological modeling: a scoping review of opportunities and challengesNature Communications10.1038/s41467-024-55461-x16:1Online publication date: 10-Jan-2025
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Pujante-Otalora LCanovas-Segura BCampos MJuarez J(2023)The use of networks in spatial and temporal computational models for outbreak spread in epidemiology: A systematic reviewJournal of Biomedical Informatics10.1016/j.jbi.2023.104422143(104422)Online publication date: Jul-2023
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Index Terms

Modeling Individual-Level Infection Dynamics Using Social Network Information
1. Applied computing
  1. Life and medical sciences

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cover image ACM Conferences

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

October 2015

1998 pages

ISBN:9781450337946

DOI:10.1145/2806416

General Chairs:
James Bailey
The University of Melbourne
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IBM
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University of Amsterdam
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Ravi Kumar
Google
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Vanessa Murdock
Microsoft
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Timos Sellis
RMIT University
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Chinese University of Hong Kong

Copyright © 2015 ACM.

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Published: 17 October 2015

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Cited By

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https://doi.org/10.1038/s41467-024-55461-x
Rashid MWang D(2023)CovidTrak: A Vision on Social Intelligence-Empowered COVID-19 Contact TracingIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.319510310:6(3385-3405)Online publication date: Dec-2023
https://doi.org/10.1109/TCSS.2022.3195103
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https://doi.org/10.1016/j.jbi.2023.104422
Noraset TChatrinan KTawichsri TThaipisutikul TTuarob S(2022)Language-agnostic deep learning framework for automatic monitoring of population-level mental health from social networksJournal of Biomedical Informatics10.1016/j.jbi.2022.104145133(104145)Online publication date: Sep-2022
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Chatrinan KKangpanich AWichit TNoraset TTuarob STawichsri T(2021)Towards Approximating Population-Level Mental Health in Thailand Using Large-Scale Social Media DataTowards Open and Trustworthy Digital Societies10.1007/978-3-030-91669-5_26(334-343)Online publication date: 30-Nov-2021
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