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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Large-scale acceptance with local vaccine rejection can 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.
“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]
The Results and Discussion (pp. not specified) report that clusters of unvaccinated people can undermine herd immunity, increase outbreak probability, and mean that vaccination-coverage estimates needed to avoid outbreaks are too low. The Discussion also says infection can spread from one susceptible cluster to another. The paper does not specifically find that clustering raises the coverage percentage needed in adjacent geographical regions. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060 · see citing works
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135 · see citing works
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 · see citing works
Hall, A. J. (1992) · Transactions of the Royal Society of Tropical Medicine and Hygiene · doi:10.1016/0035-9203(92)90276-i · Cited by 2,179 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
‘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 · see citing works
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 · see citing works
Richard, J. L., Spicher, V. M., Santibanez, S. S., & Mankertz, A. (2008) · Eurosurveillance · doi:10.2807/ese.13.08.08043-en · Cited by 64 · see citing works
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 · see citing works
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 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Erdős P, Rényi A. On random graphs. I. Publicationes Math. 1959;6:290–297. [Google Scholar]
Cited by
Sorted by citations
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 · see citing works
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 · see citing works
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 · see citing works
Self-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 · see citing works
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 · see citing works
Increasing Vaccination: Putting Psychological Science Into Action
Brewer, N. T., Chapman, G. B., Rothman, A. J., Leask, J., & Kempe, A. (2017) · Psychological Science in the Public Interest · doi:10.1177/1529100618760521 · Cited by 1,233 · see citing works
Statistical physics of vaccination
Wang, Z., Bauch, C. T., Bhattacharyya, S., d’Onofrio, A., Manfredi, P., Perc, M., Perra, N., Salathé, M., & Zhao, D. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.10.006 · Cited by 954 · see citing works
Self-citationAttitudes to vaccination: A critical review
Yaqub, O., Castle-Clarke, S., Sevdalis, N., & Chataway, J. (2014) · Social Science & Medicine · doi:10.1016/j.socscimed.2014.04.018 · Cited by 1,089 · see citing works
Coupled disease–behavior dynamics on complex networks: A review
Wang, Z., Andrews, M. A., Wu, Z.-X., Wang, L., & Bauch, C. T. (2015) · Physics of Life Reviews · doi:10.1016/j.plrev.2015.07.006 · Cited by 551 · see citing works
Salathé, M., & Khandelwal, S. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002199 · Cited by 530 · see citing works
Self-citationThe Temporal Structure of Scientific Consensus Formation
Shwed, U., & Bearman, P. (2010) · American Sociological Review · doi:10.1177/0003122410388488 · Cited by 336 · see citing works
Immune life history, vaccination, and the dynamics of SARS-CoV-2 over the next 5 years
Saad-Roy, C. M., Wagner, C. E., Baker, R. E., Morris, S. E., Farrar, J., Graham, A. L., Levin, S. A., Mina, M. J., Metcalf, C. J. E., & Grenfell, B. T. (2020) · Science · doi:10.1126/science.abd7343 · Cited by 278 · see citing works
Semantic network analysis of vaccine sentiment in online social media
Kang, G. J., Ewing-Nelson, S., Mackey, L., Schlitt, J. T., Marathe, A., Abbas, K., & Swarup, S. (2017) · Vaccine · doi:10.1016/j.vaccine.2017.05.052 · Cited by 268 · see citing works
Online misinformation is linked to early COVID-19 vaccination hesitancy and refusal
Pierri, F., Perry, B. L., DeVerna, M. R., Yang, K.-C., Flammini, A., Menczer, F., & Bryden, J. (2022) · Scientific Reports · doi:10.1038/s41598-022-10070-w · Cited by 264 · see citing works
Pattern transitions in spatial epidemics: Mechanisms and emergent properties
Sun, G., Jusup, M., Jin, Z., Wang, Y., & Wang, Z. (2016) · Physics of Life Reviews · doi:10.1016/j.plrev.2016.08.002 · Cited by 261 · see citing works
Evolutionary Game Theory and Social Learning Can Determine How Vaccine Scares Unfold
Bauch, C. T., & Bhattacharyya, S. K. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002452 · Cited by 253 · see citing works
Jentsch, P. C., Anand, M., & Bauch, C. T. (2021) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(21)00057-8 · Cited by 226 · see citing works
Analysis of SIR epidemic model with information spreading of awareness
Kabir, K. M. A., Kuga, K., & Tanimoto, J. (2018) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2018.12.017 · Cited by 224 · see citing works
Complex Contagions: A Decade in Review
Guilbeault, D., Becker, J., & Centola, D. (2017) · arXiv.org · doi:10.1007/978-3-319-77332-2_1 · Cited by 179 · see citing works
Social Networks and Health: New Developments in Diffusion, Online and Offline
Zhang, J., & Centola, D. M. (2019) · Annual Review of Sociology · doi:10.1146/annurev-soc-073117-041421 · Cited by 208 · see citing works
Early warning signals of regime shifts in coupled human–environment systems
Bauch, C. T., Sigdel, R. P., Pharaon, J., & Anand, M. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1604978113 · Cited by 185 · see citing works
Risk perception and effectiveness of uncoordinated behavioral responses in an emerging epidemic
Poletti, P., Ajelli, M., & Merler, S. (2012) · Mathematical Biosciences · doi:10.1016/j.mbs.2012.04.003 · Cited by 160 · see citing works
Cerda, A. A., & García, L. Y. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.626852 · Cited by 160 · see citing works
On the existence of a threshold for preventive behavioral responses to suppress epidemic spreading
Sahneh, F. D., Chowdhury, F. N., & Scoglio, C. (2012) · Scientific Reports · doi:10.1038/srep00632 · Cited by 155 · see citing works
The Impact of Imitation on Vaccination Behavior in Social Contact Networks
Ndeffo-Mbah, M. L., Liu, J., Bauch, C. T., Tekel, Y. I., Medlock, J., Ancel, L. W., & Galvani, A. P. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002469 · Cited by 154 · see citing works
Acheampong, T., Akorsikumah, E. A., Osae-Kwapong, J., Khalid, M., Appiah, A., & Amuasi, J. (2021) · Vaccines · doi:10.3390/vaccines9080814 · Cited by 141 · see citing works
The dynamics of health behavior sentiments on a large online social network
Salathé, M., Vu, D. Q., Khandelwal, S., & Hunter, D. R. (2013) · EPJ Data Science · doi:10.1140/epjds16 · Cited by 116 · see citing works
Self-citationSocial encounter networks: collective properties and disease transmission
Danon, L., House, T., Read, J. M., & Keeling, M. J. (2012) · Journal of The Royal Society Interface · doi:10.1098/rsif.2012.0357 · Cited by 110 · see citing works
Dynamics of COVID-19 under social distancing measures are driven by transmission network structure
Nande, A., Adlam, B., Sheen, J. K., Levy, M. Z., & Hill, A. L. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008684 · Cited by 111 · see citing works
de Figueiredo, A., Larson, H. J., & Reicher, S. D. (2021) · EClinicalMedicine · doi:10.1016/j.eclinm.2021.101109 · Cited by 106 · see citing works
Kabir, K. M. A., Kuga, K., & Tanimoto, J. (2019) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2019.109548 · Cited by 107 · see citing works
Dyda, A., King, C. L., Dey, A., Leask, J., & Dunn, A. G. (2020) · BMC Public Health · doi:10.1186/s12889-020-09327-8 · Cited by 103 · see citing works
Kabir, K. M. A., & Tanimoto, J. (2019) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2019.01.020 · Cited by 102 · see citing works
Effect of information spreading to suppress the disease contagion on the epidemic vaccination game
Kabir, K. M. A., Kuga, K., & Tanimoto, J. (2019) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2018.12.023 · Cited by 97 · see citing works
Stability analysis and optimal control of an epidemic model with awareness programs by media
MISRA, A. K., Sharma, A., & Shukla, J. B. (2015) · Biosystems · doi:10.1016/j.biosystems.2015.11.002 · Cited by 92 · see citing works
Arthur, R., Gurley, E., Salje, H., Bloomfield, L. S. P., & Jones, J. H. (2017) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2016.0454 · Cited by 90 · see citing works
Risk of disease and willingness to vaccinate in the United States: A population-based survey
Baumgaertner, B. O., Ridenhour, B. J., Justwan, F., Carlisle, J. E., & Miller, C. R. (2020) · PLoS Medicine · doi:10.1371/journal.pmed.1003354 · Cited by 91 · see citing works
Complex social contagion makes networks more vulnerable to disease outbreaks
Campbell, E. M., & Salathé, M. (2013) · Scientific Reports · doi:10.1038/srep01905 · Cited by 89 · see citing works
Self-citationLiu, F., Enanoria, W. T. A., Zipprich, J., Blumberg, S., Harriman, K. H., Ackley, S. F., Wheaton, W. D., Allpress, J. L. E., & Porco, T. C. (2015) · BMC Public Health · doi:10.1186/s12889-015-1766-6 · Cited by 88 · see citing works
Braess's Paradox in Epidemic Game: Better Condition Results in Less Payoff
Zhang, H.-F., Yang, Z., Wu, Z.-X., Wang, B.-H., & Zhou, T. (2013) · Scientific Reports · doi:10.1038/srep03292 · Cited by 86 · see citing works
Barclay, V. C., Smieszek, T., He, J., Cao, G., Rainey, J. J., Gao, H., Uzicanin, A., & Salathé, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0087042 · Cited by 85 · see citing works
Self-citationA social contagious model of the obesity epidemic
Huang, H. Q., Yan, Z., Chen, Y., & Liu, F. (2016) · Scientific Reports · doi:10.1038/srep37961 · Cited by 79 · see citing works
Epidemic spreading with awareness and different timescales in multiplex networks
Ventura, P. C., Velásquez-Rojas, F., Connaughton, C., Vázquez, F., Moreno, Y., & Rodrigues, F. A. (2019) · Physical review. E · doi:10.1103/physreve.100.032313 · Cited by 78 · see citing works
Xia, S., & Liu, J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0060373 · Cited by 78 · see citing works
Characterizing the impact of spatial clustering of susceptibility for measles elimination
Truelove, S., Graham, M., Moss, W. J., Metcalf, C. J. E., Ferrari, M. J., & Lessler, J. (2018) · Vaccine · doi:10.1016/j.vaccine.2018.12.012 · Cited by 77 · see citing works
Glasser, J. W., Feng, Z., Omer, S. B., Smith, P. J., & Rodewald, L. E. (2016) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(16)00004-9 · Cited by 75 · see citing works
Untangling the Interplay between Epidemic Spread and Transmission Network Dynamics
Kamp, C. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000984 · Cited by 74 · see citing works
Loomba, S., de Figueiredo, A., Piatek, S. J., de Graaf, K., & Larson, H. J. (2020) · medRxiv · doi:10.1101/2020.10.22.20217513 · Cited by 74 · see citing works
Impact of committed individuals on vaccination behavior
Liu, X.-T., Wu, Z.-X., & Zhang, L. (2012) · Physical Review E · doi:10.1103/physreve.86.051132 · Cited by 73 · see citing works
Mesa, D. O., Hogan, A. B., Watson, O. J., Charles, G. D., Hauck, K., Ghani, A. C., & Winskill, P. (2022) · Communications Medicine · doi:10.1038/s43856-022-00075-x · Cited by 68 · see citing works
Dueling biological and social contagions
Fu, F., Christakis, N. A., & Fowler, J. H. (2017) · Scientific Reports · doi:10.1038/srep43634 · Cited by 71 · see citing works
Studies on a multidimensional public opinion network model and its topic detection algorithm
Wang, G., Chi, Y., Liu, Y., & Wang, Y.-F. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2018.11.010 · Cited by 68 · see citing works
Oscillatory dynamics in the dilemma of social distancing.Preprint
Glaubitz, A., & Fu, F. (2020) · PubMed · Cited by 65 · see citing works
Behavioral Epidemiology of Infectious Diseases: An Overview
Bauch, C. T., d’Onofrio, A., & Manfredi, P. (2012) · Springer eBooks · doi:10.1007/978-1-4614-5474-8_1 · Cited by 69 · see citing works
Masters, N. B., Eisenberg, M. C., Delamater, P. L., Kay, M., Boulton, M. L., & Zelner, J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2011529117 · Cited by 65 · see citing works
Threat-Related Information Suggests Competence: A Possible Factor in the Spread of Rumors
Boyer, P., & Parren, N. (2015) · PLoS ONE · doi:10.1371/journal.pone.0128421 · Cited by 65 · see citing works
Kabir, K. M. A., & Tanimoto, J. (2019) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2019.04.007 · Cited by 64 · see citing works
Oscillatory dynamics in the dilemma of social distancing
Glaubitz, A., & Fu, F. (2020) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2020.0686 · Cited by 61 · see citing works
MacFarlane, D., Hurlstone, M. J., & Ecker, U. K. H. (2020) · Social Science & Medicine (1967) · doi:10.1016/j.socscimed.2020.112790 · Cited by 53 · see citing works
Substance, use in relation to COVID-19: A scoping review
Kumar, N., Janmohamed, K., Nyhan, K., Martins, S. S., Cerdá, M., Hasin, D. S., Scott, J., Frimpong, A. S., Pates, R. M., Ghandour, L. A., et al. (2021) · Addictive Behaviors · doi:10.1016/j.addbeh.2021.107213 · Cited by 55 · see citing works
The effect of COVID-19 vaccination in Italy and perspectives for living with the virus
Marziano, V., Guzzetta, G., Mammone, A., Riccardo, F., Poletti, P., Trentini, F., Manica, M., Siddu, A., Bella, A., Stefanelli, P., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27532-w · Cited by 52 · see citing works
Dissecting the indirect effects caused by vaccines into the basic elements
Lefebvre, C. D. S., Terlinden, A., & Standaert, B. A. (2015) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2015.1052196 · Cited by 52 · see citing works
How will mass-vaccination change COVID-19 lockdown requirements in Australia?
Zachreson, C., Chang, S. L., Cliff, O. M., & Prokopenko, M. (2021) · The Lancet Regional Health - Western Pacific · doi:10.1016/j.lanwpc.2021.100224 · Cited by 49 · see citing works
Freezing period strongly impacts the emergence of a global consensus in the voter model
Wang, Z., Liu, Y., Wang, L., Zhang, Y., & Wang, Z. (2014) · Scientific Reports · doi:10.1038/srep03597 · Cited by 50 · see citing works
To close the childhood immunization gap, we need a richer understanding of parents' decision-making
Corben, P., & Leask, J. (2016) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2016.1221553 · Cited by 48 · see citing works
Networks of influence and infection: parental choices and childhood disease
Eames, K. T. D. (2009) · Journal of The Royal Society Interface · doi:10.1098/rsif.2009.0085 · Cited by 45 · see citing works
Word usage mirrors community structure in the online social network Twitter
Bryden, J., Funk, S., & Jansen, V. A. A. (2013) · EPJ Data Science · doi:10.1140/epjds15 · Cited by 45 · see citing works
Toward Precision Healthcare: Context and Mathematical Challenges
Colijn, C., Jones, N. S., Johnston, I. G., Yaliraki, S., & Barahona, M. (2017) · Frontiers in Physiology · doi:10.3389/fphys.2017.00136 · Cited by 44 · see citing works
Daughton, A. R., & Paul, M. J. (2019) · Journal of Medical Internet Research · doi:10.2196/13090 · Cited by 44 · see citing works
Voinson, M., Billiard, S., & Alvergne, A. (2015) · PLoS ONE · doi:10.1371/journal.pone.0142990 · Cited by 41 · see citing works
Sudden transitions in coupled opinion and epidemic dynamics with vaccination
Pires, M. A., Oestereich, A. L., & Crokidakis, N. (2018) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/aabfc6 · Cited by 41 · see citing works
Dutta, T., Agley, J. D., Meyerson, B. E., Barnes, P. A., Sherwood‐Laughlin, C., & Nicholson‐Crotty, J. (2021) · PLoS ONE · doi:10.1371/journal.pone.0253318 · Cited by 41 · see citing works
Enanoria, W. T. A., Liu, F., Zipprich, J., Harriman, K. H., Ackley, S. F., Blumberg, S., Worden, L., & Porco, T. C. (2016) · PLoS ONE · doi:10.1371/journal.pone.0167160 · Cited by 40 · see citing works
Spatial Clustering of Vaccine Exemptions on the Risk of a Measles Outbreak
Gromis, A. R., & Liu, K. (2021) · PEDIATRICS · doi:10.1542/peds.2021-050971 · Cited by 39 · see citing works
Cambra, U. C., Martínez, L. M., & González, J. I. N. (2019) · El Profesional de la Informacion · doi:10.3145/epi.2019.mar.17 · Cited by 39 · see citing works
Desinformación, vacunas y Covid-19. Análisis de la infodemia y la conversación digital en Twitter
Ureta, A. L., Peña-Fernández, S., & Morales-i-Gras, J. (2021) · Revista Latina de Comunicación Social · doi:10.4185/rlcs-2021-1504 · Cited by 39 · see citing works
Network segregation and the propagation of misinformation
Stein, J., Keuschnigg, M., & van de Rijt, A. (2023) · Scientific Reports · doi:10.1038/s41598-022-26913-5 · Cited by 38 · see citing works
Peer 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 · see citing works
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 36 · see citing works
de Figueiredo, A., Temfack, E., Raji, T., & Larson, H. J. (2023) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2023.2213117 · Cited by 35 · see citing works
Policy 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 35 · see citing works
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 · see citing works
The 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 · see citing works
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 42 · see citing works
Rapid 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 · see citing works
What 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 · see citing works
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 · see citing works
Modelling behavioural contagion
House, T. (2011) · Journal of The Royal Society Interface · doi:10.1098/rsif.2011.0014 · Cited by 32 · see citing works
Bounded rationality alters the dynamics of paediatric immunization acceptance
Oraby, T. F., & Bauch, C. T. (2015) · Scientific Reports · doi:10.1038/srep10724 · Cited by 32 · see citing works
Smaldino, P. E. (2022) · Current Directions in Psychological Science · doi:10.1177/09637214221075609 · Cited by 31 · see citing works
Garrett, K. A. (2012) · European Journal of Plant Pathology · doi:10.1007/s10658-012-9963-y · Cited by 31 · see citing works
Anderson, K.-A., & Creanza, N. (2023) · PLOS Global Public Health · doi:10.1371/journal.pgph.0001186 · Cited by 31 · see citing works
Wells, C. R., & Bauch, C. T. (2012) · Epidemics · doi:10.1016/j.epidem.2012.06.002 · Cited by 30 · see citing works
Homophily 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 · see citing works
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 · see citing works
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 26 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
- 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
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › 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
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
