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Impact of misinformation in temporal network epidemiology

Published online by Cambridge University Press:  25 April 2019

Petter Holme Open the ORCID record for Petter Holme [Opens in a new window]  and
Luis E. C. Rocha
Petter Holme*
Affiliation:
Institute of Innovative Research, Tokyo Institute of Technology, Tokyo, Japan
Luis E. C. Rocha
Affiliation:
Centre for Business Network Analysis, Department of International Business and Economics, Business School University of Greenwich, London, United Kingdom (e-mail: [email protected])
*
*Corresponding author. Email: [email protected]
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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

computational epidemiologynetwork sciencecomplex networks
Type
Original Article
Information
Network Science , Volume 7 , Issue 1 , March 2019 , pp. 52 - 69
Copyright
© Cambridge University Press 2019 

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References

Adams, J., & Moody, J. (2007). To tell the truth: Measuring concordance in multiply reported network data. Social Networks, 29(1), 44–58.CrossRefGoogle Scholar
Anderson, R. M., & May, R. M. (1992). Infectious diseases in humans. Oxford: Oxford University Press.Google Scholar
Bajardi, P., Barrat, A., Natale, F., Savini, L., & Colizza, V. (2011). Dynamical patterns of cattle trade movements. PLOS One, 6, e19869.CrossRefGoogle ScholarPubMed
Bansal, S., Read, J., Pourbohloul, B., & Meyers, L. A. (2010). The dynamic nature of contact networks in infectious disease epidemiology. Journal of Biological Dynamics, 4, 478–489.CrossRefGoogle ScholarPubMed
Barabási, A.-L. (2015). Network science. Cambridge, UK: Cambridge University Press.Google Scholar
Bigwood, G., Henderson, T., Rehunathan, D., Bateman, M., & Bhatti, S. (2011). CRAWDAD dataset st_andrews/sassy (v. 2011-06-03). Retrieved from http://crawdad.org/st_andrews/sassy/20110603/mobile.Google Scholar
Colman, E., Spies, K., & Bansal, S. (2018). The reachability of contagion in temporal contact networks: How disease latency can exploit the rhythm of human behavior. BMC Infectious Diseases, 18(1), 219.CrossRefGoogle ScholarPubMed
Eagle, N., & Pentland, A. S. (2006). Reality mining: Sensing complex social systems. Personal and Ubiquitous Computing, 10(4), 255–268.Google Scholar
Ebel, H., Mielsch, L.-I., & Bornholdt, S. (2002). Scale-free topology of e-mail networks. Physical Review E, 66, 035103.Google ScholarPubMed
Eckmann, J.-P., Moses, E., & Sergi, D. (2004). Entropy of dialogues creates coherent structures in e-mail traffic. Proceedings of the National Academy of Sciences of the United States of America, 101, 14333–14337.CrossRefGoogle ScholarPubMed
Génois, M., Vestergaard, C. L., Fournet, J., Panisson, A., Bonmarin, I., & Barrat, A. (2015). Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers. Network Science, 3(9), 326–347.CrossRefGoogle Scholar
Gernat, T., Rao, V. D., Middendorf, M., Dankowicz, H., Goldenfeld, N., & Robinson, G. E. (2018). Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks. Proceedings of the National Academy of Sciences of the United States of America, 115(7), 1433–1438.CrossRefGoogle ScholarPubMed
Giesecke, J. (2002). Modern infectious disease epidemiology (2nd ed.). London: Arnold.Google Scholar
Goh, K.-I., & Barabási, A.-L. (2008). Burstiness and memory in complex systems. EPL (Europhysics Letters), 81(4), 48002.CrossRefGoogle Scholar
Hethcote, H. W. (2000). The mathematics of infectious diseases. SIAM Review, 32(4), 599–653.CrossRefGoogle Scholar
Holme, P. (2005). Network reachability of real-world contact sequences. Physical Review E, 71(4), 046119.CrossRefGoogle ScholarPubMed
Holme, P. (2013a). Epidemiologically optimal static networks from temporal network data. PLOS Computational Biology, 9, e1003142.Google Scholar
van den Broeck, W., Quaggiotto, M., Isella, L., Barrat, A., & Cattuto, C. (2012). The making of sixty-nine days of close encounters at The Science Gallery. Leonardo, 45, 201–202.CrossRefGoogle Scholar
Vanhems, P., Barrat, A., Cattuto, C., Pinton, J.-F., Khanafer, N., Régis, C., … Voirin, N. (2013). Estimating potential infection transmission routes in hospital wards using wearable proximity sensors. PLOS One, 8, e73970.CrossRefGoogle Scholar
Viswanath, B., Mislove, A., Cha, M., & Gummadi, K. P. (2009). On the evolution of user interaction in Facebook. Proceedings of the 2nd ACM workshop on online social networks. WOSN ‘09 (pp. 37–42). New York, NY: ACM.Google Scholar
Volz, E. M., Miller, J. C., Galvani, A., & Ancel Meyers, L. (2011). Effects of heterogeneous and clustered contact patterns on infectious disease dynamics. PLOS Computational Biology, 7(6), 1–13.CrossRefGoogle ScholarPubMed
Zhang, Y.-Q., Li, X., Xu, J., & Vasilakos, A. (2015). Human interactive patterns in temporal networks. IEEE Transactions on Systems, Man, and Cybernetics, 45(2), 214–222.CrossRefGoogle Scholar