Relevance of temporal cores for epidemic spread in temporal networks.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
27 07 2020
27 07 2020
Historique:
received:
19
03
2020
accepted:
07
07
2020
entrez:
29
7
2020
pubmed:
29
7
2020
medline:
29
7
2020
Statut:
epublish
Résumé
Temporal networks are widely used to represent a vast diversity of systems, including in particular social interactions, and the spreading processes unfolding on top of them. The identification of structures playing important roles in such processes remains largely an open question, despite recent progresses in the case of static networks. Here, we consider as candidate structures the recently introduced concept of span-cores: the span-cores decompose a temporal network into subgraphs of controlled duration and increasing connectivity, generalizing the core-decomposition of static graphs. To assess the relevance of such structures, we explore the effectiveness of strategies aimed either at containing or maximizing the impact of a spread, based respectively on removing span-cores of high cohesiveness or duration to decrease the epidemic risk, or on seeding the process from such structures. The effectiveness of such strategies is assessed in a variety of empirical data sets and compared to baselines that use only static information on the centrality of nodes and static concepts of coreness, as well as to a baseline based on a temporal centrality measure. Our results show that the most stable and cohesive temporal cores play indeed an important role in epidemic processes on temporal networks, and that their nodes are likely to include influential spreaders.
Identifiants
pubmed: 32719352
doi: 10.1038/s41598-020-69464-3
pii: 10.1038/s41598-020-69464-3
pmc: PMC7385111
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
12529Références
Barrat, A., Barthélemy, M. & Vespignani, A. Dynamical Processes on Complex Networks (Cambridge University Press, Cambridge, 2008).
doi: 10.1017/CBO9780511791383
Pastor-Satorras, R. & Vespignani, A. Immunization of complex networks. Phys. Rev. E 65, 036104 (2002).
doi: 10.1103/PhysRevE.65.036104
Cohen, R., Havlin, S. & Ben-Avraham, D. Efficient immunization strategies for computer networks and populations. Phys. Rev. Lett. 91, 247901 (2003).
doi: 10.1103/PhysRevLett.91.247901
Kitsak, M. et al. Identification of influential spreaders in complex networks. Nat. Phys. 6, 888–893 (2010).
doi: 10.1038/nphys1746
Pastor-Satorras, R., Castellano, C., Mieghem, P. V. & Vespignani, A. Epidemic processes in complex networks. Rev. Mod. Phys. 87, 925 (2015).
doi: 10.1103/RevModPhys.87.925
Herrera, J. L., Srinivasan, R., Brownstein, J. S., Galvani, A. P. & Meyers, L. A. Disease surveillance on complex social networks. PLOS Comput. Biol. 12, 1–16 (2016).
doi: 10.1371/journal.pcbi.1004928
Teng, X., Pei, S., Morone, F. & Makse, H. A. Collective influence of multiple spreaders evaluated by tracing real information flow in large-scale social networks. Sci. Rep. 6, 36043 (2016).
doi: 10.1038/srep36043
Radicchi, F. & Castellano, C. Fundamental difference between superblockers and superspreaders in networks. Phys. Rev. E 95, 012318 (2017).
doi: 10.1103/PhysRevE.95.012318
Bai, Y. et al. Optimizing sentinel surveillance in temporal network epidemiology. Sci. Rep. 7, 4804 (2017).
doi: 10.1038/s41598-017-03868-6
Holme, P. & Litvak, N. Cost-efficient vaccination protocols for network epidemiology. PLOS Comput. Biol. 13, 1–18. https://doi.org/10.1371/journal.pcbi.1005696 (2017).
doi: 10.1371/journal.pcbi.1005696
Erkol, Ş, Castellano, C. & Radicchi, F. Systematic comparison between methods for the detection of influential spreaders in complex networks. Sci. Rep. 9, 15095 (2019).
doi: 10.1038/s41598-019-51209-6
Holme, P. & Saramäki, J. Temporal networks. Phys. Rep. 519, 97–125 (2012).
doi: 10.1016/j.physrep.2012.03.001
Holme, P. Modern temporal network theory: a colloquium. Eur. Phys. J. B 88, 234 (2015).
doi: 10.1140/epjb/e2015-60657-4
Gauvin, L., Panisson, A., Cattuto, C. & Barrat, A. Activity clocks: spreading dynamics on temporal networks of human contact. Sci. Rep. 3, 3099 (2013).
doi: 10.1038/srep03099
Gauvin, L., Panisson, A. & Cattuto, C. Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach. PLoS ONE 9, e86028 (2014).
doi: 10.1371/journal.pone.0086028
Bajardi, P., Barrat, A., Savini, L. & Colizza, V. Optimizing surveillance for livestock disease spreading through animal movements. J. R. Soc. Interface 9, 2814–2825 (2012).
doi: 10.1098/rsif.2012.0289
Lee, S., Rocha, L. E. C., Liljeros, F. & Holme, P. Exploiting temporal network structures of human interaction to effectively immunize populations. PLoS ONE 7, e36439 (2012).
doi: 10.1371/journal.pone.0036439
Starnini, M., Machens, A., Cattuto, C., Barrat, A. & Pastor-Satorras, R. Immunization strategies for epidemic processes in time-varying contact networks. J. Theor. Biol. 337, 89–100 (2013).
doi: 10.1016/j.jtbi.2013.07.004
Masuda, N. & Holme, P. Predicting and controlling infectious disease epidemics using temporal networks. F1000Prime Reports 5 (2013).
Liu, S., Perra, N., Karsai, M. & Vespignani, A. Controlling contagion processes in activity driven networks. Phys. Rev. Lett. 112, 118702 (2014).
doi: 10.1103/PhysRevLett.112.118702
Valdano, E. et al. Predicting epidemic risk from past temporal contact data. PLOS Comput. Biol. 11, 1–19 (2015).
doi: 10.1371/journal.pcbi.1004152
Gemmetto, V., Barrat, A. & Cattuto, C. Mitigation of infectious disease at school: targeted class closure vs school closure. BMC Infect. Dis. 14, 695 (2014).
doi: 10.1186/s12879-014-0695-9
Gauvin, L., Panisson, A., Barrat, A. & Cattuto, C. Revealing latent factors of temporal networks for mesoscale intervention in epidemic spread (2015). arXiv:1501.02758 .
Génois, M. & Barrat, A. Can co-location be used as a proxy for face-to-face contacts?. EPJ Data Sci. 7, 11 (2018).
doi: 10.1140/epjds/s13688-018-0140-1
Litvinova, M., Liu, Q.-H., Kulikov, E. S. & Ajelli, M. Reactive school closure weakens the network of social interactions and reduces the spread of influenza. In Proceedings of the National Academy of Sciences (2019).
Galimberti, E., Barrat, A., Bonchi, F., Cattuto, C. & Gullo, F. Mining (maximal) span-cores from temporal networks. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM ’18, 107–116 (ACM, New York, NY, USA, 2018).
Batagelj, V. & Zaveršnik, M. Fast algorithms for determining (generalized) core groups in social networks. ADAC 5, 129–145 (2011).
doi: 10.1007/s11634-010-0079-y
Castellano, C. & Pastor-Satorras, R. Competing activation mechanisms in epidemics on networks. Sci. Rep. 2, 371 (2012).
doi: 10.1038/srep00371
Rozenshtein, P. & Gionis, A. Temporal pagerank. In Frasconi, P., Landwehr, N., Manco, G. & Vreeken, J. (eds.) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2016, vol. 9852 of ecture Notes in Computer Science (Springer, 2016).
Sociopatterns collaboration. www.sociopatterns.org . Accessed 18 June 2019.
Toth, D. J. A. et al. The role of heterogeneity in contact timing and duration in network models of influenza spread in schools. J. R. Soc. Interface 12, 20150279 (2015).
doi: 10.1098/rsif.2015.0279
Valdano, E., Ferreri, L., Poletto, C. & Colizza, V. Analytical computation of the epidemic threshold on temporal networks. Phys. Rev. X 5, 021005 (2015).
Valdano, E., Poletto, C. & Colizza, V. Infection propagator approach to compute epidemic thresholds on temporal networks: impact of immunity and of limited temporal resolution. Eur. Phys. J. B 88, 341 (2015).
doi: 10.1140/epjb/e2015-60620-5
Eidsaa, M. & Almaas, E. [Formula: see text]-core network decomposition: A generalization of [Formula: see text]-core analysis to weighted networks. Phys. Rev. E 88, 062819 (2013).
doi: 10.1103/PhysRevE.88.062819
Mastrandrea, R., Fournet, J. & Barrat, A. Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys. PLoS ONE 10, e0136497 (2015).
doi: 10.1371/journal.pone.0136497
Génois, M. et al. Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers. Netw. Sci. 3, 326–347 (2015).
doi: 10.1017/nws.2015.10
Gauvin, L. et al. Randomized reference models for temporal networks (2018). arXiv:1806.04032
Smieszek, T. & Salathé, M. A low-cost method to assess the epidemiological importance of individuals in controlling infectious disease outbreaks. BMC Med. 11, 35 (2013).
doi: 10.1186/1741-7015-11-35
Stehlé, J. et al. High-resolution measurements of face-to-face contact patterns in a primary school. PLoS ONE 6, e23176 (2011).
doi: 10.1371/journal.pone.0023176
Vanhems, P. et al. Estimating potential infection transmission routes in hospital wards using wearable proximity sensors. PLoS ONE 8, e73970 (2013).
doi: 10.1371/journal.pone.0073970
Isella, L. et al. What’s in a crowd? analysis of face-to-face behavioral networks. J. Theor. Biol. 271, 166–180 (2011).
doi: 10.1016/j.jtbi.2010.11.033