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The effect of opinion clustering on disease outbreaks
Open accessAbstract
Many high-income countries currently experience large outbreaks of vaccine-preventable diseases such as measles despite the availability of highly effective vaccines. This phenomenon lacks an explanation in countries where vaccination rates are rising on an already high level. Here, we build on the growing evidence that belief systems, rather than access to vaccines, are the primary barrier to vaccination in high-income countries, and show how a simple opinion formation process can lead to clusters of unvaccinated individuals, leading to a dramatic increase in disease outbreak probability. In particular, the effect of clustering on outbreak probabilities is strongest when the vaccination coverage is close to the level required to provide herd immunity under the assumption of random mixing. Our results based on computer simulations suggest that the current estimates of vaccination coverage necessary to avoid outbreaks of vaccine-preventable diseases might be too low.
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“While large-scale vaccine rejection threatens herd immunity goals, large-scale acceptance with local vaccine rejection can also have negative consequences for community (herd) immunity, as clustering of non-vaccinators can disproportionately increase the needed percentage of vaccination coverage to achieve herd immunity in adjacent geographical regions and encourage epidemic spread 24 .”
From Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA (2021) · cites this paper as [24]
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Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
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
Hall, A. J. (1992) · Transactions of the Royal Society of Tropical Medicine and Hygiene · doi:10.1016/0035-9203(92)90276-i · Cited by 2,146
Measles Outbreaks in a Population with Declining Vaccine Uptake
Jansen, V. A. A., Stollenwerk, N., Jensen, H. J., Ramsay, M. E., Edmunds, W. J., & Rhodes, C. J. (2003) · Science · doi:10.1126/science.1086726 · Cited by 370
Parker, A. A., Staggs, W., Dayan, G. H., Ortega‐Sanchez, I. R., Rota, P. A., Lowe, L., Boardman, P., Teclaw, R. F., Graves, C., & LeBaron, C. W. (2006) · New England Journal of Medicine · doi:10.1056/nejmoa060775 · Cited by 290
Summary and Conclusions: Measles Elimination Meeting, 16–17 March 2000
Orenstein, W. A., Samuel, K. L., & Hinman, A. R. (2004) · The Journal of Infectious Diseases · doi:10.1086/377696 · Cited by 224
‘Clustering of exemptions’ as a collective action threat to herd immunity
May, T., & Silverman, R. D. (2003) · Vaccine · doi:10.1016/s0264-410x(02)00627-8 · Cited by 120
Schmid, D., Holzmann, H., Abele, S., Kasper, S. S., König, S., Meusburger, S., Hrabcik, H., Luckner-Hornischer, A., Bechter, E., DeMartin, A., et al. (2008) · Eurosurveillance · doi:10.2807/ese.13.16.18838-en · Cited by 69
Richard, J. L., Spicher, V. M., Santibanez, S. S., & Mankertz, A. (2008) · Eurosurveillance · doi:10.2807/ese.13.08.08043-en · Cited by 64
Measles resurges in Italy: preliminary data from September 2007 to May 2008
Filia, A., Crescenzo, M. D., Seyler, T., Bella, A., Atti, M. L. C. D., Nicoletti, L., Magurano, F., & Salmaso, S. (2008) · Eurosurveillance · doi:10.2807/ese.13.29.18928-en · Cited by 42
Measles outbreaks in London, United Kingdom - a preliminary report
Heathcock, R., & Watts, C. (2008) · Eurosurveillance · doi:10.2807/ese.13.15.18829-en · Cited by 22
Research works printed but not linked: 1
- Erdős P, 1959, On random graphs. I, Publicationes Math, 6, 290
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Epidemic processes in complex networks
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
Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA
Loomba, S., de Figueiredo, A., Piatek, S. J., de Graaf, K., & Larson, H. J. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01056-1 · Cited by 1,800
Modelling the influence of human behaviour on the spread of infectious diseases: a review
Funk, S., Salathé, M., & Jansen, V. A. A. (2010) · Journal of The Royal Society Interface · doi:10.1098/rsif.2010.0142 · Cited by 1,322
“Models have recently been proposed to base vaccination behaviour on the spread of opinions in a social neighbourhood (Salathé and Bonhoeffer, 2008; Eames, 2009) rather than individual rational behaviour.”
InfluentialSelf-citationThe State of Vaccine Confidence 2016: Global Insights Through a 67-Country Survey
Larson, H. J., de Figueiredo, A., Xiahong, Z., Schulz, W. S., Verger, P., Johnston, I. G., Cook, A. R., & Jones, N. S. (2016) · EBioMedicine · doi:10.1016/j.ebiom.2016.08.042 · Cited by 1,264
The spread of awareness and its impact on epidemic outbreaks
Funk, S., Gilad, E., Watkins, C., & Jansen, V. A. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0810762106 · Cited by 1,134
Metadata
- Authors
- Salathé, M., & Bonhoeffer, S.
- Affiliations
- Stanford University (US)
- ETH Zurich (CH)
- Corresponding author
- Salathé, Marcel
- Venue
- Journal of The Royal Society Interface
- Publisher
- The Royal Society
- Published
- 2008-08-19
- Volume
- 5
- Issue
- 29
- Pages
- 1505-1508
- ISSN
- 1742-5662 (ISSN-L)
- 1742-5689
- PMC
- PMC2607358
- OpenAlex
- W2109953716
- Cited by
- 238
- References
- 12
- Citations by year
- 2026: 11 · 2025: 14 · 2024: 14 · 2023: 18 · 2022: 31 · 2021: 28 · 2020: 21 · 2019: 16 · 2018: 7 · 2017: 10 · 2016: 11 · 2015: 14 · 2014: 3 · 2013: 12 · 2012: 14
- FWCI
- 4.93
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of The Royal Society Interface (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Funders
- National Institute of General Medical Sciences (GM28016, R01GM028016)
- Topics
- Social Sciences › Health › Vaccine Coverage and Hesitancy
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Opinion Dynamics and Social Influence
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical and Theoretical Epidemiology and Ecology Models
- Keywords
- opinion clustering, outbreak, measles, vaccine-preventable diseases, vaccination coverage, herd immunity, belief systems, opinion formation, high-income countries, outbreak probability
- MeSH terms
- Computer Simulation, Culture, Disease Outbreaks, Humans, Models, Theoretical, Patient Acceptance of Health Care, Public Opinion, Vaccination, Cluster Analysis
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