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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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“Thus, the size of the epidemic increases with the degree of network segregation 25 and, in segregated populations, rates of resilient individuals need to be higher to reach herd immunity 26 .”
BackgroundPeer pressure is a double-edged sword in vaccination dynamics
Wu, Z.-X., & Zhang, H.-F. (2013) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/104/10002 · Cited by 38
Coupled dynamics of behaviour and disease contagion among antagonistic groups
Smaldino, P. E., & Jones, J. H. (2021) · Evolutionary Human Sciences · doi:10.1017/ehs.2021.22 · Cited by 33
de Figueiredo, A., Temfack, E., Raji, T., & Larson, H. J. (2023) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2023.2213117 · Cited by 35
“Regional losses in confidence – such as those established in this study – can translate to an increased clustering of non-vaccinated individuals, which may pose challenges for herd immunity goals.(38)”
ResultPolicy Resistance Undermines Superspreader Vaccination Strategies for Influenza
Wells, C. R., Klein, E. Y., & Bauch, C. T. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002945 · Cited by 36
Sabahelzain, M. M., Moukhyer, M. E., Dubé, È., Hardan, A., Bosma, H., & van den Borne, B. H. P. (2019) · PLoS ONE · doi:10.1371/journal.pone.0213882 · Cited by 35
“Such concerns contributed to a decrease in vaccine uptake and an increase in the number of cases of many vaccine preventable diseases over the past several years in Europe and the USA [4–6].”
BackgroundThe Timing and Nature of Behavioural Responses Affect the Course of an Epidemic
Tyson, R. C., Hamilton, S. D., Lo, A. S., Baumgaertner, B. O., & Krone, S. M. (2020) · Bulletin of Mathematical Biology · doi:10.1007/s11538-019-00684-z · Cited by 34
Kumar, N., Corpus, I., Hans, M., Harle, N., Yang, N., McDonald, C., Sakai, S. N., Janmohamed, K., Chen, K., Altice, F. L., et al. (2022) · BMC Public Health · doi:10.1186/s12889-022-12824-7 · Cited by 44
“For example, reduced vaccine uptake may increase the mortality cost of COVID-19 [18] and create clusters of non-vaccinators that disproportionately increase pandemic spread [19].”
BackgroundRapid Emergence of Free-Riding Behavior in New Pediatric Immunization Programs
Bauch, C. T., Bhattacharyya, S. K., & Ball, R. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012594 · Cited by 33
“. Recent work in this area has focused on using contact networks to describe disease transmission”
BackgroundWhat makes or breaks a campaign to stop an invading plant pathogen?
Milne, A. E., Gottwald, T. R., Parnell, S., Chávez, V. A., & van den Bosch, F. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007570 · Cited by 32
Provider dismissal policies and clustering of vaccine-hesitant families
Buttenheim, A. M., Cherng, S. T., & Asch, D. A. (2013) · Human Vaccines & Immunotherapeutics · doi:10.4161/hv.25635 · Cited by 32
“Our results build on other agent-based and game theoretic models of infectious disease dynamics that incorporate social and behavioral aspects of vaccine preferences and behaviors.(15-22) The model highlights three implications of provider tolerance policies: First, when all providers accommodate refusals and do not d…”
ResultModelling behavioural contagion
House, T. (2011) · Journal of The Royal Society Interface · doi:10.1098/rsif.2011.0014 · Cited by 32
“Modelling techniques have so far typically involved either explicit stochastic simulation (Centola and Macy, 2005, 2007; Salathé and Bonhoeffer, 2008; Funk et al., 2009), or else application of mathematical models originally developed for other applications, such as the Susceptible-Infectious-Susceptible (SIS) epidemi…”
MethodBounded rationality alters the dynamics of paediatric immunization acceptance
Oraby, T. F., & Bauch, C. T. (2015) · Scientific Reports · doi:10.1038/srep10724 · Cited by 32
Smaldino, P. E. (2022) · Current Directions in Psychological Science · doi:10.1177/09637214221075609 · Cited by 39
“Salathé and Bonhoeffer (2008) showed that if vaccinations tend to cluster in networks—that is, if people tend to assort into identity groups with others who share their views on vaccination—then the overall vaccination rate needed for herd immunity is likely to be substantially greater than what is…”
Garrett, K. A. (2012) · European Journal of Plant Pathology · doi:10.1007/s10658-012-9963-y · Cited by 31
“Epidemic and communication networks have been frequent topics for network modelling, but it is only recently that they have been considered simultaneously in systems in which information and epidemics interact as individuals use information about epidemics to guide decisions such as whether to vaccinate (Del Valle et…”
BackgroundAnderson, K.-A., & Creanza, N. (2023) · PLOS Global Public Health · doi:10.1371/journal.pgph.0001186 · Cited by 31
Wells, C. R., & Bauch, C. T. (2012) · Epidemics · doi:10.1016/j.epidem.2012.06.002 · Cited by 30
“Finally, we neglected heterogeneity relating to age or health status, as per previous models (Bauch and Earn, 2004; Vardavas et al., 2010; Wells et al., 2011; Salathe and Bonhoeffer, 2008; Poletti et al., 2009; Perisic and Bauch, 2009).”
BackgroundMethodInfluentialHomophily impacts the success of vaccine roll-outs
Burgio, G., Steinegger, B., & Arenas, À. (2022) · Communications Physics · doi:10.1038/s42005-022-00849-8 · Cited by 29
The effect of competition between health opinions on epidemic dynamics
Teslya, A., Nunner, H., Buskens, V., & Kretzschmar, M. (2022) · PNAS Nexus · doi:10.1093/pnasnexus/pgac260 · Cited by 31
Herd immunity and epidemic size in networks with vaccination homophily
Hiraoka, T., Rizi, A. K., Kivelä, M., & Saramäki, J. (2022) · Physical review. E · doi:10.1103/physreve.105.l052301 · Cited by 29
Neighborhood Determinants of 2009 Pandemic A/H1N1 Influenza Vaccination in Montreal, Quebec, Canada
Brien, S., Kwong, J. C., Charland, K., Verma, A. D., Brownstein, J. S., & Buckeridge, D. L. (2012) · American Journal of Epidemiology · doi:10.1093/aje/kws154 · Cited by 26
Covid-19, the anti-vaccine movement and immunization challenges in Brazil
Bivar, G. C. C., AGUIAR, M. E. S. C. D., Santos, R. V. C., & Cardoso, P. R. G. (2021) · Scientia Medica · doi:10.15448/1980-6108.2021.1.39425 · Cited by 25
How growers make decisions impacts plant disease control
Murray‐Watson, R. E., Hamelin, F. M., & Cunniffe, N. J. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010309 · Cited by 25
“[1], [2], [3], [4], [5]; reviewed in [6] and [7]) and animal 40 (e.”
Martin, R., Wassilak, S. G. F., Emiroğlu, N., Uzicanin, A., Deshesvoi, S., Jankovic, D., Goel, A., & Khetsuriani, N. (2011) · The Journal of Infectious Diseases · doi:10.1093/infdis/jir137 · Cited by 25
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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