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The Hidden Geometry of Complex, Network-Driven Contagion Phenomena
Abstract
Predicting Disease Dissemination: In combating the global spread of an emerging infectious disease, answers must be obtained to three crucial questions: Where did the disease emerge? Where will it go next? When will it arrive? Brockmann and Helbing (p. 1337 ; see the Perspective by McLean ) analyzed disease spread via the “effective distance” rather than geographical distance, wherein two locations that are connected by a strong link are effectively close. The approach was successfully applied to predict disease arrival times or disease source using data from the the 2003 SARS viral epidemic, 2009 H1N1 influenza pandemic, and the 2011 foodborne enterohaemorrhagic Escherichia coli outbreak in Germany.
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“In this study, we show that information dynamics tailored to alter individuals' perceptions and, consequently, their behavioral response, is able to drive collective attention 12 towards false 13,14 or inflammatory 15 content, a phenomenon named infodemics [16][17][18][19] , sharing similarities with more traditional epidemics and spreading phenomena [20][21][22] .”
From Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics (2020) · cites this paper as [15]
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“By using state-of-the-art machine learning techniques to analyze the online behavior of users (see Methods for details), we have discovered an extraordinary activity of automated agents, referred to as social bots 14,15,35,39 .”
From Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics (2020) · cites this paper as [15]
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“It is based on a well established algorithm based on deep learning 37 with state-of-the-art accuracy 15,41 .”
From Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics (2020) · cites this paper as [15]
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“As our societies are increasingly instantiated in digital form, once-mathematical abstractions of social processes—networks are one prominent example—become very real parts of daily life”
From Stewardship of global collective behavior (2021) · cites this paper as [22]
Citations
Cites
Infectious Diseases of Humans: Dynamics and Control
(1992) · Annals of Internal Medicine · doi:10.7326/0003-4819-117-2-174_4 · Cited by 7,819
Infectious Diseases of Humans: Dynamics and Control
Anderson, R. M., & May, R. M. (1991) · doi:10.1093/oso/9780198545996.001.0001 · Cited by 6,179
THE WAVE OF ADVANCE OF ADVANTAGEOUS GENES
Fisher, R. A. (1937) · Annals of Eugenics · doi:10.1111/j.1469-1809.1937.tb02153.x · Cited by 4,937
The architecture of complex weighted networks.
Barrat, A., Barthelemy, M., Pastor-Satorras, R., & Vespignani, A. (2003) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.0400087101 · Cited by 4,084
(1979) · American Economic Review · Cited by 3,564
Strategies for mitigating an influenza pandemic
Ferguson, N. M., Cummings, D. A. T., Fraser, C., Cajka, J. C., Cooley, P. C., & Burke, D. S. (2006) · Nature · doi:10.1038/nature04795 · Cited by 2,203
The scaling laws of human travel
Brockmann, D., Hufnagel, L., & Geisel, T. (2006) · Nature · doi:10.1038/nature04292 · Cited by 2,182
Self-citationModelling disease outbreaks in realistic urban social networks
Eubank, S., Güçlü, H., Kumar, V. S. A., MARATHE, M. V., Srinivasan, A., Toroczkai, Z., & Wang, N. (2004) · Nature · doi:10.1038/nature02541 · Cited by 2,098
Pandemic Potential of a Strain of Influenza A (H1N1): Early Findings
Fraser, C., Donnelly, C. A., Cauchemez, S., Hanage, W. P., Kerkhove, M. D. V., Hollingsworth, T. D., Griffin, J. T., Baggaley, R. F., Jenkins, H. E., Lyons, E., et al. (2009) · Science · doi:10.1126/science.1176062 · Cited by 1,915
Multiscale mobility networks and the spatial spreading of infectious diseases
Balcan, D., Vittoria Colizza, A., Gonçalves, B., Hu, H., Ramasco, J. J., & Vespignani, A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0906910106 · Cited by 1,347
Flagged first, then by citations
Research works printed but not linked: 3
- J. D. Murray Mathematical Biology (Springer Berlin 2005).
- Abbott E. J., Firestone F. A., Mech. Eng. 55, 569–572 (1933).
- Anderson J., Am. Econ. Rev. 69, 106–116 (1979).
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Sorted by citations
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Pastor-Satorras, R., Castellano, C., Van Mieghem, P., & Vespignani, A. (2015) · Reviews of Modern Physics · doi:10.1103/revmodphys.87.925 · Cited by 3,697
Tian, H., Liu, Y., Li, Y., Wu, C.-H., Chen, B., Kraemer, M. U. G., Li, B., Cai, J., Xu, B., Yang, Q., et al. (2020) · Science · doi:10.1126/science.abb6105 · Cited by 2,010
Ranking the effectiveness of worldwide COVID-19 government interventions
Haug, N., Geyrhofer, L., Londei, A., Dervic, E., Desvars-Larrive, A., Loreto, V., Pinior, B., Thurner, S., & Klimek, P. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-01009-0 · Cited by 1,412
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Kraemer, M. U. G., Reiner, R. C., Brady, O. J., Messina, J. P., Gilbert, M., Pigott, D. M., Yi, D., Johnson, K. B., Earl, L., Marczak, L. B., et al. (2019) · Nature Microbiology · doi:10.1038/s41564-019-0376-y · Cited by 1,393
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Barbosa, H., Barthélemy, M., Ghoshal, G., James, C. R., Lenormand, M., Louail, T., de Menezes, R. P., Ramasco, J. J., Simini, F., & Tomasini, M. (2018) · Physics Reports · doi:10.1016/j.physrep.2018.01.001 · Cited by 1,104
Statistical physics of vaccination
Wang, Z., Bauch, C. T., Bhattacharyya, S., d’Onofrio, A., Manfredi, P., Perc, M., Perra, N., Salathé, M., & Zhao, D. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.10.006 · Cited by 967
Constructing, conducting and interpreting animal social network analysis
Farine, D. R., & Whitehead, H. (2015) · Journal of Animal Ecology · doi:10.1111/1365-2656.12418 · Cited by 966
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Jia, J. S., Lu, X., Yuan, Y., Xu, G., Jia, J., & Christakis, N. A. (2020) · Nature · doi:10.1038/s41586-020-2284-y · Cited by 835
Zaburdaev, V., Denisov, S. I., & Klafter, J. (2015) · Reviews of Modern Physics · doi:10.1103/revmodphys.87.483 · Cited by 779
Jusup, M., Holme, P., Kanazawa, K., Takayasu, M., Romić, I., Wang, Z., Geček, S., Lipić, T., Boris Podobnik, A., Wang, L., et al. (2022) · Physics Reports · doi:10.1016/j.physrep.2021.10.005 · Cited by 688
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De Domenico, M., Granell, C., Porter, M. A., & Arenas, A. (2016) · Nature Physics · doi:10.1038/nphys3865 · Cited by 649
Saving Human Lives: What Complexity Science and Information Systems can Contribute
Helbing, D., Brockmann, D., Chadefaux, T., Donnay, K., Blanke, U., Woolley-Meza, O., Moussaïd, M., Johansson, A., Krause, J., Schutte, S., et al. (2014) · Journal of Statistical Physics · doi:10.1007/s10955-014-1024-9 · Cited by 634
Self-citationAssessing the risks of ‘infodemics’ in response to COVID-19 epidemics
Gallotti, R., Valle, F., Castaldo, N., Sacco, P., & De Domenico, M. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-00994-6 · Cited by 591
Mapping the zoonotic niche of Ebola virus disease in Africa
Pigott, D. M., Golding, N., Mylne, A., Huang, Z., Henry, A. J., Weiss, D. J., Brady, O. J., Kraemer, M. U. G., Smith, D. L., Moyes, C. L., et al. (2014) · eLife · doi:10.7554/elife.04395 · Cited by 475
The science of science: From the perspective of complex systems
Zeng, A., Shen, Z., Zhou, J., Wu, J., Fan, Y., Wang, Y., & Stanley, E. (2017) · Physics Reports · doi:10.1016/j.physrep.2017.10.001 · Cited by 420
Mathematical models to characterize early epidemic growth: A review
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COVID-19 lockdown induces disease-mitigating structural changes in mobility networks
Schlosser, F., Maier, B. F., Jack, O., Hinrichs, D., Zachariae, A., & Brockmann, D. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2012326117 · Cited by 369
Self-citationPreliminary prediction of the basic reproduction number of the Wuhan novel coronavirus 2019‐nCoV
Zhou, T., Liu, Q.-H., Yang, Z., Liao, J., Yang, K., Bai, W., Lu, X. B., & Zhang, W. (2020) · Journal of Evidence-Based Medicine · doi:10.1111/jebm.12376 · Cited by 361
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Ji, P., Ye, J., Mu, Y., Lin, W. J., Tian, Y., Hens, C., Perc, M., Tang, Y. S., Sun, J., & Kurths, J. (2023) · Physics Reports · doi:10.1016/j.physrep.2023.03.005 · Cited by 375
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Mena, I., Nelson, M. I., Quezada-Monroy, F., Dutta, J., Cortes-Fernández, R., Puente, J. H. L., Castro-Peralta, F., Cunha, L. F., Trovão, N. S., Lozano-Dubernard, B., et al. (2016) · eLife · doi:10.7554/elife.16777 · Cited by 365
Data based identification and prediction of nonlinear and complex dynamical systems
Wang, W.-X., Lai, Y.-C., & Grebogi, C. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.06.004 · Cited by 328
Dynamically induced cascading failures in power grids
Schäfer, B., Witthaut, D., Timme, M., & Latora, V. (2018) · Nature Communications · doi:10.1038/s41467-018-04287-5 · Cited by 311
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Wang, W., Liu, Q.-H., Liang, J., Hu, Y., & Zhou, T. (2019) · Physics Reports · doi:10.1016/j.physrep.2019.07.001 · Cited by 309
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Bak-Coleman, J. B., Alfano, M., Barfuss, W., Bergstrom, C. T., Centeno, M. A., Couzin, I. D., Donges, J. F., Galesic, M., Gersick, A. S., Jacquet, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2025764118 · Cited by 289
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How did COVID-19 impact air transportation? A first peek through the lens of complex networks
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Vayena, E., Salathé, M., Madoff, L. C., & Brownstein, J. S. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1003904 · Cited by 268
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Centeno, M. A., Nag, M., Patterson, T. S., Shaver, A., & Windawi, A. J. (2015) · Annual Review of Sociology · doi:10.1146/annurev-soc-073014-112317 · Cited by 240
Kraemer, M. U. G., Faria, N. R., Reiner, R. C., Golding, N., Nikolay, B., Stasse, S., Johansson, M. A., Salje, H., Faye, O., Wint, W., et al. (2016) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(16)30513-8 · Cited by 232
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Jiang, J., Wen, S., Yu, S., Xiang, Y., & Zhou, W. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2615098 · Cited by 235
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Liao, H., Mariani, M. S., Medo, M., Zhang, Y., & Zhou, M.-Y. (2017) · Physics Reports · doi:10.1016/j.physrep.2017.05.001 · Cited by 239
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Yu, S., Liu, M., Dou, W., Liu, X., & Zhou, S. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2610963 · Cited by 223
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Winkelmann, R., Donges, J. F., Smith, E. K., Milkoreit, M., Eder, C., Heitzig, J., Κατσανίδου, Α., Wiedermann, M., Wunderling, N., & Lenton, T. M. (2021) · Ecological Economics · doi:10.1016/j.ecolecon.2021.107242 · Cited by 270
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Wille, M., & Holmes, E. C. (2019) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a038489 · Cited by 205
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Zhou, X., & Zafarani, R. (2019) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3373464.3373473 · Cited by 225
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Hens, C., Harush, U., Haber, S., Reuven Cohen, C., & Barzel, B. (2019) · Nature Physics · doi:10.1038/s41567-018-0409-0 · Cited by 196
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Wang, L., & Li, X. (2014) · Chinese Science Bulletin · doi:10.1007/s11434-014-0499-8 · Cited by 198
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Pei, S., Kandula, S., Yang, W., & Shaman, J. L. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1708856115 · Cited by 182
Iturria‐Medina, Y., Sotero, R. C., Toussaint, P. J., Evans, A. C., & Initiative, A. T. A. D. N. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003956 · Cited by 185
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Harush, U., & Barzel, B. (2017) · Nature Communications · doi:10.1038/s41467-017-01916-3 · Cited by 172
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Vogel, J. W., Corriveau‐Lecavalier, N., Franzmeier, N., Pereira, J. B., Brown, J. A., Maaß, A., Botha, H., Seeley, W. W., Bassett, D. S., Jones, D. T., et al. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00731-8 · Cited by 180
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Saberi, M., Ghamami, M., Gu, Y., Shojaei, M., & Fishman, E. K. (2017) · Journal of Transport Geography · doi:10.1016/j.jtrangeo.2017.11.018 · Cited by 153
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Taylor, D., Klimm, F., Harrington, H. A., Kramár, M., Mischaikow, K., Porter, M. A., & Mucha, P. J. (2015) · Nature Communications · doi:10.1038/ncomms8723 · Cited by 148
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Shelke, S., & Attar, V. (2019) · Online Social Networks and Media · doi:10.1016/j.osnem.2018.12.001 · Cited by 148
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Wang, L., & Wu, J. T. K. (2018) · Nature Communications · doi:10.1038/s41467-017-02344-z · Cited by 139
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Kraemer, M. U. G., Sadilek, A., Zhang, Q., Marchal, N., Tuli, G., Cohn, E. L., Hswen, Y., Perkins, T. A., Smith, D. L., Reiner, R. C., et al. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0875-0 · Cited by 137
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Shang, Q., Deng, Y., & Cheong, K. H. (2021) · Information Sciences · doi:10.1016/j.ins.2021.01.053 · Cited by 137
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Ding, F., Fu, J., Jiang, D., Hao, M., & Lin, G. (2018) · Acta Tropica · doi:10.1016/j.actatropica.2017.11.020 · Cited by 130
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Guo, C., Yang, L., Chen, X., Chen, D., Gao, H., & Ma, J. (2020) · Entropy · doi:10.3390/e22020242 · Cited by 137
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Resilience of networks with community structure behaves as if under an external field
Dong, G., Fan, J., Shekhtman, L., Shai, S., Du, R., Tian, L., Chen, X., Stanley, E., & Havlin, S. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1801588115 · Cited by 126
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Local vulnerability and global connectivity jointly shape neurodegenerative disease propagation
Zheng, Y., Zhang, Y., Yau, Y. H. C., Zeighami, Y., Larcher, K. M.-H., Mišić, B., & Dagher, A. (2019) · PLoS Biology · doi:10.1371/journal.pbio.3000495 · Cited by 119
Vaccination and epidemics in networked populations—An introduction
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Multi-frequency complex network from time series for uncovering oil-water flow structure
Gao, Z., Yang, Y., Fang, P., Jin, N., Xia, C., & Hu, L.-D. (2015) · Scientific Reports · doi:10.1038/srep08222 · Cited by 124
Virus evolution and transmission in an ever more connected world
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Tian, H., Sun, Z., Faria, N. R., Yang, J., Cazelles, B., Huang, S., Xu, B., Yang, Q., Pybus, O. G., & Xu, B. (2017) · PLoS neglected tropical diseases · doi:10.1371/journal.pntd.0005694 · Cited by 118
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Kraemer, M. U. G., Golding, N., Bisanzio, D., Bhatt, S., Pigott, D. M., Ray, S. E., Brady, O. J., Brownstein, J. S., Faria, N. R., Cummings, D. A. T., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-41192-3 · Cited by 116
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Hallatschek, O., & Fisher, D. S. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1404663111 · Cited by 116
Gao, J. G., Zhang, Y., & Zhou, T. (2019) · Physics Reports · doi:10.1016/j.physrep.2019.05.002 · Cited by 122
Peixoto, P. S., Marcondes, D., Peixoto, C., de Queiroz, L., Gouveia, R., Delgado, A., & Oliva, S. M. (2020) · PLoS ONE · doi:10.1371/journal.pone.0235732 · Cited by 115
Effective distances for epidemics spreading on complex networks
Iannelli, F., Koher, A., Brockmann, D., Hövel, P., & Sokolov, I. M. (2017) · Physical review. E · doi:10.1103/physreve.95.012313 · Cited by 113
Self-citationRumor Source Identification in Social Networks with Time-Varying Topology
Jiang, J., Wen, S., Yu, S., Xiang, Y., & Zhou, W. (2016) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2016.2522436 · Cited by 113
Why Italy First? Health, Geographical and Planning Aspects of the COVID-19 Outbreak
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Metadata
- Authors
- Brockmann, D., & Helbing, D.
- Affiliations
- Northwestern University (US)
- ETH Zurich (CH)
- Corresponding author
- Brockmann, Dirk
- Venue
- Science
- Published
- 2013-12-13
- Volume
- 342
- Issue
- 6164
- Pages
- 1337-1342
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W1991288592
- Cited by
- 1,253
- References
- 43
- Citations by year
- 2026: 63 · 2025: 92 · 2024: 92 · 2023: 97 · 2022: 155 · 2021: 152 · 2020: 150 · 2019: 94 · 2018: 93 · 2017: 72 · 2016: 87 · 2015: 65 · 2014: 40 · 2013: 1
- FWCI
- 45.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical and Theoretical Epidemiology and Ecology Models
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Keywords
- effective distance, epidemic spread, rumor propagation, opinion dynamics, diffusion of innovations, wave propagation, spatiotemporal patterns, air traffic network, 2009 H1N1 pandemic, network heterogeneity
- MeSH terms
- Computer Simulation, Disease Outbreaks, Humans, Influenza, Human, Models, Biological, Population Density, Prognosis, Communicable Diseases, Emerging, Severe Acute Respiratory Syndrome, Severe acute respiratory syndrome-related coronavirus, Influenza A Virus, H1N1 Subtype, Pandemics, Spatio-Temporal Analysis, Human Migration
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