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Impact of misinformation in temporal network epidemiology

(2019) NETWORK SCIENCE. 7(1). p.52-69
Author
Organization
Abstract
We investigate the impact of misinformation about the contact structure on the ability to predict disease outbreaks. We base our study on 31 empirical temporal networks and tune the frequencies in errors in the node identities or time stamps of contacts. We find that for both these spreading scenarios, the maximal misprediction of both the outbreak size and time to extinction follows an stretched exponential convergence as a function of the error frequency. We furthermore determine the temporal-network structural factors influencing the parameters of this convergence.
Keywords
Sociology and Political Science, Communication, Social Psychology, DYNAMICS, COMMUNICATION, PATTERNS, BEHAVIOR, computational epidemiology, network science, complex networks

Citation

Please use this url to cite or link to this publication:

MLA
Holme, Petter, and Luis E. C. Rocha. “Impact of Misinformation in Temporal Network Epidemiology.” NETWORK SCIENCE, vol. 7, no. 1, 2019, pp. 52–69, doi:10.1017/nws.2018.28.
APA
Holme, P., & Rocha, L. E. C. (2019). Impact of misinformation in temporal network epidemiology. NETWORK SCIENCE, 7(1), 52–69. https://doi.org/10.1017/nws.2018.28
Chicago author-date
Holme, Petter, and Luis E C Rocha. 2019. “Impact of Misinformation in Temporal Network Epidemiology.” NETWORK SCIENCE 7 (1): 52–69. https://doi.org/10.1017/nws.2018.28.
Chicago author-date (all authors)
Holme, Petter, and Luis E C Rocha. 2019. “Impact of Misinformation in Temporal Network Epidemiology.” NETWORK SCIENCE 7 (1): 52–69. doi:10.1017/nws.2018.28.
Vancouver
1.
Holme P, Rocha LEC. Impact of misinformation in temporal network epidemiology. NETWORK SCIENCE. 2019;7(1):52–69.
IEEE
[1]
P. Holme and L. E. C. Rocha, “Impact of misinformation in temporal network epidemiology,” NETWORK SCIENCE, vol. 7, no. 1, pp. 52–69, 2019.
@article{8632444,
  abstract     = {{We investigate the impact of misinformation about the contact structure on the ability to predict disease outbreaks. We base our study on 31 empirical temporal networks and tune the frequencies in errors in the node identities or time stamps of contacts. We find that for both these spreading scenarios, the maximal misprediction of both the outbreak size and time to extinction follows an stretched exponential convergence as a function of the error frequency. We furthermore determine the temporal-network structural factors influencing the parameters of this convergence.}},
  author       = {{Holme, Petter and Rocha, Luis E C}},
  issn         = {{2050-1242}},
  journal      = {{NETWORK SCIENCE}},
  keywords     = {{Sociology and Political Science,Communication,Social Psychology,DYNAMICS,COMMUNICATION,PATTERNS,BEHAVIOR,computational epidemiology,network science,complex networks}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{52--69}},
  title        = {{Impact of misinformation in temporal network epidemiology}},
  url          = {{http://doi.org/10.1017/nws.2018.28}},
  volume       = {{7}},
  year         = {{2019}},
}

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