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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]
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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]
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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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