The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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).
Cited by
Sorted by citations
Items 901–1,000 of 1,000
Ide, K., Satō, H., Quang, T., & Namatame, A. (2016) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2016.070245 · Cited by 2
Optimizing complex networks controllability by local structure information
Yan, H., Hou, L., Ling, Y., & Wu, G. (2016) · International Journal of Modern Physics C · doi:10.1142/s0129183116501151 · Cited by 2
Air passenger flow communities between countries
Sun, X., Wandelt, S., Dzikus, N. M., & Linke, F. (2016) · Chinese Control Conference (CCC) · doi:10.1109/chicc.2016.7554832 · Cited by 2
Perra, N. (2016) · Physics of Life Reviews · doi:10.1016/j.plrev.2016.09.009 · Cited by 2
Towards Identifying and Predicting Spatial Epidemics on Complex Meta-population Networks
Li, X., Wang, J., & Li, C. (2017) · Theoretical biology · doi:10.1007/978-981-10-5287-3_6 · Cited by 2
Modeling outbreak data: Analysis of a 2012 Ebola virus disease epidemic in DRC
Choi, B., Busch, S., Kazadi, D. M., Kebela, B., Okitolonda, E. W., Dai, Y. Q., Lumpkin, R. M., KhudaBukhsh, W. R., Saucedo, O., Yotebieng, M., et al. (2019) · BIOMATH · doi:10.11145/j.biomath.2019.10.037 · Cited by 2
Combinatorial approach to spreading processes on networks
Mazzilli, D., & Radicchi, F. (2021) · The European Physical Journal B · doi:10.1140/epjb/s10051-020-00029-z · Cited by 2
Global asymptotic stability of a general biased min‐consensus protocol
Mo, Y., Yu, L., & Yu, C. B. (2021) · IET Control Theory and Applications · doi:10.1049/cth2.12111 · Cited by 2
Radici, A., Martinetti, D., & Bevacqua, D. (2021) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa53316.2021.9591658 · Cited by 2
Gromov centrality: A multiscale measure of network centrality using triangle inequality excess
Babul, S., Devriendt, K., & Lambiotte, R. (2022) · Physical review. E · doi:10.1103/physreve.106.034312 · Cited by 2
Assessing the Risk of Spatial Spreading of Diseases in Hospitals
Lu, D. Z., Aleta, A., & Moreno, Y. (2022) · Frontiers in Physics · doi:10.3389/fphy.2022.882314 · Cited by 2
Spatial Interactions and the Spread of COVID-19: A Network Perspective
Zhang, C., & Zhang, D. (2022) · Computational Economics · doi:10.1007/s10614-022-10278-y · Cited by 2
Kuang, Z., Liu, C., Wu, J., & Wang, Y. (2022) · Heliyon · doi:10.1016/j.heliyon.2022.e11474 · Cited by 2
Staggered loans: A flexible modality of long-term financing for SMEs in global health emergencies
Rambaud, S. C., Pascual, J. L., & Santandreu, E. M. (2022) · Quantitative Finance and Economics · doi:10.3934/qfe.2022024 · Cited by 2
Structural analysis of Air Transport Network using Network indicators
Thasni, M. A., & George, S. (2022) · IEEE International Power and Renewable Energy Conference (IPRECON) · doi:10.1109/iprecon55716.2022.10059483 · Cited by 2
Influential Yield Strength of Steel Materials with Return Random Walk Gravity Centrality
Rodríguez, R., Curado, M., Rodríguez, F. D., & Vicent, J. F. (2024) · Mathematics · doi:10.3390/math12030439 · Cited by 2
VIVIAN: virtual simulation and visual analysis of epidemic spread data
Li, G., Chang, B., Zhao, J., Wang, J., He, F., Wang, Y., Xu, T., & Zhou, Z. (2024) · Journal of Visualization · doi:10.1007/s12650-024-00990-2 · Cited by 2
Differentially Private Computation of Basic Reproduction Numbers in Networked Epidemic Models
Chen, B., She, B., Hawkins, C., Benvenuti, A., Fallin, B., Paré, P. E., & Hale, M. (2024) · Proceedings of the ... American Control Conference/Proceedings of the American Control Conference · doi:10.23919/acc60939.2024.10644264 · Cited by 2
Moving beyond simulation and learning: Unveiling the potential of complexity data science
Emmert‐Streib, F., Cherifi, H., Kauffman, S. A., & Yli-Harja, O. P. (2024) · PLOS complex systems. · doi:10.1371/journal.pcsy.0000002 · Cited by 2
Shortest path counting in complex networks based on powers of the adjacency matrix
Tan, D., Deng, Y., Xiao, Y., & Wu, J. J. (2024) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0226144 · Cited by 2
A multiple-strain pathogen model with diffusion on the space of Radon measures
Ackleh, A. S., Saintier, N., & Zhang, A. (2024) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2024.108402 · Cited by 2
Networked advocacy and social media: conceptualizing persuasion at a network scale
Bince, R. L. (2024) · Argumentation and Advocacy · doi:10.1080/10511431.2024.2432132 · Cited by 2
Guan, B., Ren, Y., & Zhang, Y. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.116396 · Cited by 2
Analysis on early spatiotemporal transmission characteristics of COVID-19
Wang, C., Yan, J., Wang, X., & Li, M. (2020) · Acta Physica Sinica · doi:10.7498/aps.69.20200285 · Cited by 2
Convertino, M., & Hedberg, C. W. (2014) · PLoS Currents · doi:10.1371/currents.outbreaks.a73b658ef9785c259a845bff014b9fac · Cited by 2
Towards identifying epidemic processes with interplay between complex networks and human populations
Li, X., Wang, J., & Li, C. (2016) · IEEE Conference on Norbert Wiener in the 21st Century (21CW) · doi:10.1109/norbert.2016.7547462 · Cited by 2
Cities' influence on spatial epidemics
Boris Podobnik, A., Lipić, T., Bojić, I., & Antulov-Fantulin, N. (2016) · Physics of Life Reviews · doi:10.1016/j.plrev.2016.10.009 · Cited by 2
Spreading dynamics in heterogeneous graphs: Beyond the assortativity coefficient
Sugarelli, M., & Vergni, D. (2016) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2016.11.122 · Cited by 2
Yang, K.-C., Wu, Z.-X., Holme, P., & Nonaka, E. (2017) · Physical review. E · doi:10.1103/physreve.95.012306 · Cited by 2
Nishiura, H., Matsuyama, R., & Asai, Y. (2016) · Uirusu · doi:10.2222/jsv.66.79 · Cited by 2
Identifying Rumor Source of Online Social Networks in the SEIR Model
Zhou, Y., & Wu, C. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00018-9_34 · Cited by 2
Identifying Multiple Propagation Sources
Jiang, J., Wen, S., Yu, S., Liu, B., Xiang, Y., & Zhou, W. (2018) · Advances in information security · doi:10.1007/978-3-030-02179-5_11 · Cited by 2
Observer Selection for Source Identification on Complex Networks
Shi, C., Zhang, Q., & Chu, T. (2019) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2019.8865969 · Cited by 2
Which Node Properties Identify the Propagation Source in Networks?
Li, Z., Xia, C., Wang, T., & Liu, X. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-38991-8_17 · Cited by 2
Chen, S., Zhou, H. L., Lai, J., Zhou, Y., & Yu, C. (2020) · Energies · doi:10.3390/en13051257 · Cited by 2
Epidemic Spreading on Complex Networks as Front Propagation into an Unstable State
Armbruster, A., Holzer, M., Roselli, N., & Underwood, L. (2022) · Bulletin of Mathematical Biology · doi:10.1007/s11538-022-01110-7 · Cited by 2
TPSL: A propagation source localization method based on limited observation nodes
Wang, C., Man, Z., Yuan, S., & Zhang, G. (2021) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/ac3926 · Cited by 2
Source Identification of Propagating Waves Inside a Network
Plouraboué, F., Uszes, P., & Guibert, R. (2022) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2022.3144647 · Cited by 2
Chen, X., Qiu, Y., Shi, W., & Yu, P. (2022) · China Economic Review · doi:10.1016/j.chieco.2022.101800 · Cited by 2
Zhou, T. (2022) · Computational and Mathematical Methods in Medicine · doi:10.1155/2022/9554057 · Cited by 2
Exposure theory for learning complex networks with random walks
Klishin, A. A., & Bassett, D. S. (2022) · Journal of Complex Networks · doi:10.1093/comnet/cnac029 · Cited by 2
Tanrıverdi, G., & Durak, M. Ş. (2022) · Journal of Aviation · doi:10.30518/jav.1172121 · Cited by 2
Nonlinear model of infection wavy oscillation of COVID-19 in Japan based on diffusion kinetics
Tsuruyama, T. (2022) · Scientific Reports · doi:10.1038/s41598-022-23633-8 · Cited by 2
Designing for Human Behaviour in a Systemic World
Maier, A. M., & Cash, P. J. (2023) · Springer eBooks · doi:10.1007/978-3-030-46054-9_16-2 · Cited by 3
Travel restrictions cannot prevent the introduction of new COVID variants
Zhang, Q., Ye, Y., & Zeng, D. D. (2023) · Journal of Travel Medicine · doi:10.1093/jtm/taad066 · Cited by 2
Wang, Q., An, K., Liu, Z., & Guan, W. (2023) · Journal of Traffic and Transportation Engineering (English Edition) · doi:10.1016/j.jtte.2023.11.001 · Cited by 2
Weber, T., Kaibel, V., & Säger, S. (2023) · Optimization and Engineering · doi:10.1007/s11081-023-09869-x · Cited by 2
Effectiveness of vaccination and quarantine policies to curb the spread of COVID-19
Jang, G. H., Kim, S. J., Lee, M. J., & Son, S. (2024) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2024.129580 · Cited by 2
Forecasting confirmed cases of the COVID-19 pandemic with a migration-based epidemiological model
Wang, X., Yang, L., Zhang, H., Yang, Z., & Liu, C. (2020) · Statistics and Its Interface · doi:10.4310/20-sii641 · Cited by 2
Path-integral formulation of spreading processes in complex networks
Iannelli, F., & Sokolov, I. M. (2021) · The European Physical Journal Special Topics · doi:10.1140/epjs/s11734-021-00161-6 · Cited by 2
Wen, H., Shi, F., Liu, Y., Xie, C. H., Qin, G., Wang, F., Liu, X., Bai, J., Hong, Q., Ma, R., et al. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.914732 · Cited by 2
Multiplex mobility network and metapopulation epidemic simulations of Italy based on open data
Desiderio, A., Salina, G., & Cimini, G. (2022) · Journal of Physics Complexity · doi:10.1088/2632-072x/ac9a29 · Cited by 2
Dynamic Responses of Networks under Perturbations: Solutions, Patterns and Predictions
Zhang, X. (2018) · doi:10.53846/goediss-6898 · Cited by 2
Digital therapeutics for distributed response to global pandemicsPreprint
Hacohen, A., Reuven Cohen, C., Efroni, S., Barzel, B., & Bachelet, I. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/444851 · Cited by 2
A combined network and machine learning approaches for product market forecastingPreprint
Fan, J.-F., Cohen, K., Shekhtman, L., Liu, S., Meng, J., Louzoun, Y., & Havlin, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1811.10273 · Cited by 2
Ewers, B., Donges, J. F., Heitzig, J., & Peterson, S. (2019) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.1902.07481 · Cited by 4
Genetic drift in range expansions is very sensitive to density feedback in dispersal and growthPreprint
Bîrzu, G., Matin, S., Hallatschek, O., & Korolev, K. S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.11627 · Cited by 2
Benchmarking Measures of Network InfluencePreprint
Bramson, A., & Vandermarliere, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1602.05744 · Cited by 2
Impact of airline travel network on the global importation risk of monkeypox, 2022Preprint
Kinoshita, R., Sassa, M., Otake, S., Yoshimatsu, F., Shi, S., Ueno, R., Suzuki, M., & Yoneoka, D. (2022) · medRxiv · doi:10.1101/2022.09.17.22280060 · Cited by 2
Jung, S.-M., Miura, F., Murayama, H., Funk, S., Wallinga, J., Lessler, J., & Endo, A. (2023) · medRxiv · doi:10.1101/2023.10.06.23296610 · Cited by 2
Global solidarity in genomic surveillance improves early detection of respiratory virus threatsPreprint
de Jong, S. P. J., Nichols, B. E., RUIJTER, A. D., Parker, E., Mitesser, V., Happi, C. T., de Jong, M. D., Han, A. X., & Russell, C. A. (2023) · medRxiv · doi:10.1101/2023.11.01.23297901 · Cited by 2
A Lyapunov Analysis of a Most Probable Path Finding Algorithm
Mo, Y., Dasgupta, S., & Beal, J. (2021) · IEEE Control Systems Letters · doi:10.1109/lcsys.2021.3088260 · Cited by 1
A Simple Approach to the Multiple Source Identification of Information Diffusion
Li, X., Yu, X. W., Guo, C., Wang, Y., & Wu, J. (2022) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-14903-0_12 · Cited by 1
Perspective on the spatio-temporal spread of epidemics in metapopulation networks
Wang, J., Du, Z., Li, H., Wang, L., Xu, X.-K., Wang, Z., Wang, L., & Li, X. (2023) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/acfab8 · Cited by 1
Branching stable processes and motion by mean curvature flow
Becker, K., Etheridge, A. M., & Letter, I. (2024) · Electronic Journal of Probability · doi:10.1214/24-ejp1087 · Cited by 1
Comparing micro-level and macro-level models for epidemic diffusion in the metro system
Kuo, P.-F., Wen, T., Chuang, T., Chiu, C.-S., Ye, Y., & Putra, I. G. B. (2024) · Journal of Simulation · doi:10.1080/17477778.2024.2321879 · Cited by 1
Timely Personalized Exposure Risk Estimation for Air Travelers
Chan, C., & Wen, T. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2024.3523336 · Cited by 1
Goel, N., Liebhold, A. M., Bertelsmeier, C., Hooten, M. B., Korolev, K. S., & Keitt, T. H. (2025) · Ecological Monographs · doi:10.1002/ecm.70003 · Cited by 1
Spatiotemporal Epidemiological Similarity Based on Patient Trajectories
Pujante-Otalora, L., Campos, M., & Juárez, J. M. (2025) · IEEE Access · doi:10.1109/access.2025.3562095 · Cited by 1
Mounesan, L., Farhadi, E., Eybpoosh, S., Hosseini, ., Parsaeian, M., Gharibzadeh, S., Ahmadinezhad, M., Bahari, F., Gouya, M. M., Haghdoost, A., et al. (2025) · International Journal of Preventive Medicine · doi:10.4103/ijpvm.ijpvm_146_24 · Cited by 1
Critical behavior of epidemics depending on the interplay between temporal scales and human behavior
Maniscalco, D., Bertola, T., d’Andrea, V., & Domenico, M. D. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.116501 · Cited by 1
In itinere infections covertly undermine localized epidemic control in metapopulations
Dilisante, F., Valgañón, P., Soriano‐Paños, D., & Gómez‐Gardeñes, J. (2025) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0275094 · Cited by 1
Identifying influential nodes in social networks considering heterogeneous cost
Hu, W., Deng, Y., Xiao, Y., & Wu, J. J. (2025) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2025.109110 · Cited by 1
DIMPLES: Distributed Influence Maximization for Pandemic pLanning on Exascale Systems
Minutoli, M., Neff, R., Sattar, N. S., Lu, H., Feo, J., Mortveit, H., Vullikanti, A. K. S., Xie, D., Wilson, M., von Laszewski, G., et al. (2025) · International Conference on Supercomputing · doi:10.1145/3721145.3730414 · Cited by 1
Combining Gravity Box-Coverage With Effective Distance to Identify Key Nodes in Complex Networks
Liu, X., He, S., Fiumara, G., Meo, P. D., & Zhou, T. (2025) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2025.3606264 · Cited by 1
Li, J., He, Y., Dong, Z., & Li, T. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3058-8_11 · Cited by 1
Wang, H., Huang, H., & Liu, H. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.117585 · Cited by 1
de Jong, S. P. J., Nichols, B. E., RUIJTER, A. D., Parker, E., Mitesser, V., Happi, C. T., de Jong, M. D., Han, A. X., & Russell, C. A. (2026) · Nature Communications · doi:10.1038/s41467-025-67442-9 · Cited by 1
Li, J., Liu, G., Wang, H., Zhang, J., Huang, J., & Zang, Y. (2026) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2026.112420 · Cited by 1
Asymmetrical social copying drives behavioural cascades following a shock event
Moussaïd, M., Toyokawa, W., & Kurvers, R. H. J. M. (2026) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2024.0451 · Cited by 1
Effective distance-based multi-modality feature selection for Alzheimer’s disease identification
Wang, M., Ye, T., Ma, C., Li, X., Liu, M., & Zhang, D. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110582 · Cited by 1
Understanding and predicting the global spread of emergent infectious diseases
Brockmann, D. (2014) · Public Health Forum · doi:10.1016/j.phf.2014.07.001 · Cited by 1
Self-citationTendency of International Air Travels
Sato, A. (2014) · Springer eBooks · doi:10.1007/978-4-431-54974-1_8 · Cited by 1
Provenance Identification in Diffusion Networks with Incomplete Cascades
Shi, C., Zhang, Q., & Chu, T. (2018) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2018.8483866 · Cited by 1
Identifying Propagation Source in Large-Scale Networks
Jiang, J., Wen, S., Yu, S., Liu, B., Xiang, Y., & Zhou, W. (2018) · Advances in information security · doi:10.1007/978-3-030-02179-5_12 · Cited by 1
Rees, E. E., Ng, V., Gachon, P., Mawudeku, A., McKenney, D. W., Pedlar, J., Yemshanov, D., Parmely, J., & Knox, J. (2019) · Relevé des maladies transmissibles au Canada · doi:10.14745/ccdr.v45i05a02f · Cited by 1
Impacts of hub nodes on the spread of Name-Your-Own-Price auction
Huang, H. Q., Chen, Y., Zhou, L., & Hu, Y. (2021) · International Journal of Modern Physics C · doi:10.1142/s0129183122500085 · Cited by 1
STANDARD DIFFUSION IN GROWING NETWORKS: MODELING INTERACTION PATTERNS
Fürstenau, D., & Kliewer, N. (2014) · European Conference on Information Systems · Cited by 1
THE IMPACT OF THE METRO SYSTEM ON THE SPREAD OF COVID-19
Chiu, C.-W., & Kuo, P.-F. (2022) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xliii-b4-2022-79-2022 · Cited by 1
Directed Network Disassembly Method Based on Non-Backtracking Matrix
Ma, J., Wang, P., & Li, H. (2022) · Applied Sciences · doi:10.3390/app122312047 · Cited by 1
Detecting Global Community Structure in a COVID-19 Activity Correlation Network
Sayama, H. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-21127-0_46 · Cited by 1
The effect of novel coronavirus disease (COVID-19) on air transport
Sugiyanto, G., Santoso, P. B., & Santi, M. Y. (2023) · AIP conference proceedings · doi:10.1063/5.0110645 · Cited by 1
Meini, M., Felice, G. D., Petrella, M., & Spinelli, G. (2023) · RIVISTA GEOGRAFICA ITALIANA · doi:10.3280/rgioa1-2023oa15435 · Cited by 1
CLP: A Community based Label Propagation Framework for Multiple Source Detection
Zhang, C., Fu, L., Long, F., Wang, X., & Zhou, C. (2023) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom53939.2023.10228907 · Cited by 1
Critical Nodes Evaluation in Multiplex Heterogeneous Network Based on Gravity Model
Zhan, X., Shu, J., & Liu, L. (2023) · IEEE International Conference on Communication Software and Networks · doi:10.1109/iccsn57992.2023.10297335 · Cited by 1
Multiplex Network Approach for Modeling the Spread of African Swine Fever in Poland
Jarynowski, A. K., Czekaj, Ł., Semenov, A., & Belik, V. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-0669-3_32 · Cited by 1
Emergence of Loops in Spatial Networks With Self-Organized Growth
Chen, W., Zeng, A., & Cui, X. (2024) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2024.3382812 · Cited by 1
The Impact of Covid-19 on the Aviation Industry in Air Transportation
-, P. K. (2024) · International Journal For Multidisciplinary Research · doi:10.36948/ijfmr.2024.v06i02.19137 · Cited by 1
García-García, D., Fernández-Martínez, B., Bartumeus, F., & Gómez‐Barroso, D. (2024) · JMIR Public Health and Surveillance · doi:10.2196/51191 · Cited by 1
Predicting Higher-order Dynamics without Network Topology by Ridge Regression
Zhou, Z., Li, C., Qu, B., & Li, X. (2024) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas58744.2024.10558445 · Cited by 1
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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review