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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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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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Distorted insights from human mobility data
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Liu, Y., Wang, X., & Kurths, J. (2018) · Physical review. E · doi:10.1103/physreve.98.012313 · Cited by 22
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Wang, J., & Li, X. (2018) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2018.2873609 · Cited by 22
Fousek, J., Kaše, V., Mertel, A., Výtvarová, E., & Chalupa, A. (2018) · PLoS ONE · doi:10.1371/journal.pone.0208744 · Cited by 22
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Mocanu, D. C., Exarchakos, G., & Liotta, A. (2018) · Scientific Reports · doi:10.1038/s41598-018-19356-4 · Cited by 22
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Kattan, G. H., Aronson, J. C., & Murcia, C. (2016) · Restoration Ecology · doi:10.1111/rec.12453 · Cited by 21
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Bataille, A., Salami, H., Seck, I., Lô, M. M., Ba, A., Diop, M., Sall, B., Faye, C., Lo, M., Kaba, L., et al. (2021) · PLoS Pathogens · doi:10.1371/journal.ppat.1009397 · Cited by 21
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Wang, Y., Wang, L., Fan, H., & Wang, X. (2019) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.5097242 · Cited by 20
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Comolli, A., Wit, A. D., & Brau, F. (2019) · Physical review. E · doi:10.1103/physreve.100.052213 · Cited by 20
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Bertagnolli, G., & Domenico, M. D. (2021) · Physical review. E · doi:10.1103/physreve.103.042301 · Cited by 19
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Pei, S., Tang, S., & Zheng, Z. (2015) · PLoS ONE · doi:10.1371/journal.pone.0124848 · Cited by 20
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Fan, J., Meng, J., & Saberi, A. A. (2019) · Physical review. E · doi:10.1103/physreve.99.022304 · Cited by 19
Godói, I. P. D., Sarmento, T. T. R., Reis, E. A., Gargano, L. P., Godman, B., Acúrcio, F. D. A., Álvares-Teodoro, J., Guerra, A. A., & Ruas, C. M. (2021) · Expert Review of Pharmacoeconomics & Outcomes Research · doi:10.1080/14737167.2021.1931128 · Cited by 19
Curado, M., Rodríguez, R., Terroso-Sáenz, F., Tortosa, L., & Vicent, J. F. (2022) · Journal of Computational Science · doi:10.1016/j.jocs.2022.101819 · Cited by 19
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Cruz-Pacheco, G., Bustamante-Castaneda, J. F., Caputo, J.-G., Jiménez-Corona, M. E., & Ponce-de-León-Rosales, S. (2020) · Revista de investigaci�n Cl�nica · doi:10.24875/ric.20000113 · Cited by 18
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Wei, L., Qin, H., Wang, Y., Zhang, Z., & Yu, G. (2018) · Vehicular Communications · doi:10.1016/j.vehcom.2018.09.002 · Cited by 17
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Choi, Y., Zou, L., & Dresner, M. (2022) · Transport Policy · doi:10.1016/j.tranpol.2022.08.009 · Cited by 16
Arenas, À., Garijo, A., Gómez, S., & Villadelprat, J. (2022) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2022.112921 · Cited by 16
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Chen, Y., Liu, Y., Tang, M., & Lai, Y. (2023) · Communications Physics · doi:10.1038/s42005-023-01369-9 · Cited by 16
Towards semantic enrichment for spatial interactions
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Zhang, Y.-Q., & Li, X. (2014) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/108/28006 · Cited by 16
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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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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