Modeling individual-level infection dynamics using social network information

Suppawong Tuarob, Conrad S. Tucker, Marcel Salathe, Nilam Ram

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationCIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages1501-1510
Number of pages10
ISBN (Electronic)9781450337946
DOIs
StatePublished - Oct 17 2015
Event24th ACM International Conference on Information and Knowledge Management, CIKM 2015 - Melbourne, Australia
Duration: Oct 19 2015Oct 23 2015

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
Volume19-23-Oct-2015

Other

Other24th ACM International Conference on Information and Knowledge Management, CIKM 2015
CountryAustralia
CityMelbourne
Period10/19/1510/23/15

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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    Tuarob, S., Tucker, C. S., Salathe, M., & Ram, N. (2015). Modeling individual-level infection dynamics using social network information. In CIKM 2015 - Proceedings of the 24th ACM International Conference on Information and Knowledge Management (pp. 1501-1510). (International Conference on Information and Knowledge Management, Proceedings; Vol. 19-23-Oct-2015). Association for Computing Machinery. https://doi.org/10.1145/2806416.2806575