Abstract
Social contagion is a key mechanism that shapes health behaviors, but few studies have applied this approach at the regional level to examine how vaccination beliefs and rates vary and diffuse across geographic areas. Building upon the traditional SIR model, this paper addresses this gap by applying social network theory to a new compartmental model to simulate regional contagion in COVID-19 vaccination rates in England, using panel data of new and accumulated vaccination numbers from December 2020 to June 2022. This Social Network Vaccination Rate (SNVR) model estimates each region’s initial and changing vaccination beliefs and their mutual influence on each other. The results reveal that remote regions had higher initial vaccination beliefs and stronger spillover effects on other regions such as London with more population diversity. The paper suggests that policies to increase vaccination rates should consider the heterogeneity and peer effects among regions that collectively affect vaccination beliefs. The paper also discusses the limitations of the network model and directions for future research.
Similar content being viewed by others
Data availability Statement
The datasets generated by the research and/or analyzed during the current study alongside the example codes are available in the repository, https://doi.org/10.5281/zenodo.8267518
References
Alcendor, D. J. (2021). Targeting COVID vaccine hesitancy in rural communities in Tennessee: implications for extending the COVID-19 pandemic in the South. Vaccines, 9(11), 1279.
Alessa, A., & Faezipour, M. (2018). A review of influenza detection and prediction through social networking sites. Theoretical Biology and Medical Modelling, 15(1), 1–27.
Aluttis, C., den Broucke, S. V., Chiotan, C., Costongs, C., Michelsen, K., and Brand, H. (2014). Public Health and Health Promotion Capacity at National and Regional Level: A Review of Conceptual Frameworks. Journal of Public Health Research, 3(1):jphr.2014.199. Publisher: SAGE Publications.
Andrews, N., Stowe, J., Kirsebom, F., Toffa, S., Rickeard, T., Gallagher, E., Gower, C., Kall, M., Groves, N., O’Connell, A.-M., Simons, D., Blomquist, P. B., Zaidi, A., Nash, S., Iwani Binti Abdul Aziz, N., Thelwall, S., Dabrera, G., Myers, R., Amirthalingam, G., Gharbia, S., Barrett, J. C., Elson, R., Ladhani, S. N., Ferguson, N., Zambon, M., Campbell, C. N., Brown, K., Hopkins, S., Chand, M., Ramsay, M., and Lopez Bernal, J. (2022). Covid-19 vaccine effectiveness against the Omicron (B.1.1.529) Variant. New England Journal of Medicine, 386(16):1532–1546.
Aral, S., & Nicolaides, C. (2017). Exercise contagion in a global social network. Nature Communications, 8(1), 14753.
Ardabili, S. F., Mosavi, A., Ghamisi, P., Ferdinand, F., Varkonyi-Koczy, A. R., Reuter, U., Rabczuk, T., & Atkinson, P. M. (2020). COVID-19 Outbreak Prediction with Machine Learning. Algorithms, 13(10), 249.
Babić, K., Petrović, M., Beliga, S., Martinčić-Ipšić, S., Pranjić, M., and Meštrović, A. (2021). Prediction of COVID-19 Related Information Spreading on Twitter. In 2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO), pages 395–399. ISSN: 2623-8764.
Bagcchi, S. (2021). The world’s largest COVID-19 vaccination campaign. The Lancet Infectious Diseases, 21(3), 323.
Berkman, L. F., & Kawachi, I. (2000). Social Epidemiology. Oxford: Oxford University Press.
Bicchieri, C. (2016). Norms in the wild: How to diagnose, measure, and change social norms. Oxford: Oxford University Press.
Bivins, R. E. (2015). Contagious Communities: Medicine, Migration, and the NHS in Post-war Britain. Oxford University Press. Google-Books-ID: 7jlICgAAQBAJ.
Carcione, J. M., Santos, J. E., Bagaini, C., and Ba, J. (2020). A Simulation of a COVID-19 Epidemic Based on a Deterministic SEIR Model. Frontiers in Public Health, 8.
Chandani, S., Jani, D., Sahu, P. K., Kataria, U., Suryawanshi, S., Khubchandani, J., Thorat, S., Chitlange, S., & Sharma, D. (2021). COVID-19 vaccination hesitancy in India: State of the nation and priorities for research. Brain, Behavior, & Immunity - Health, 18, 100375.
Cheng, C. (2022). Time-series associations between Public Interest in COVID-19 variants and national vaccination rate: a google trends analysis. Behavioral Sciences, 12(7), 223.
Chia, S. C., Lu, F., and Sun, Y. (2023). Tracking the Influence of Misinformation on Elderly People’s Perceptions and Intention to Accept COVID-19 Vaccines. Health Communication, 38(5):855–865. Publisher: Routledge _eprint: https://doi.org/10.1080/10410236.2021.1980251.
Cihan, P. (2021). Forecasting fully vaccinated people against COVID-19 and examining future vaccination rate for herd immunity in the US, Asia, Europe, Africa, South America, and the World. Applied Soft Computing, 111, 107708.
Donato, K. M., & Duncan, E. M. (2011). Migration, Social Networks, and Child Health in Mexican Families. Journal of Marriage and Family, 73(4), 713–728. https://doi.org/10.1111/j.1741-3737.2011.00841.x
Fishbein, M., & Ajzen, I. (2009). The Reasoned Action Approach. New York: Psychology Press.
Garett, R., & Young, S. D. (2021). Online misinformation and vaccine hesitancy. Translational Behavioral Medicine, 11(12), 2194–2199.
Gilkey, M. B., Magnus, B. E., Reiter, P. L., McRee, A.-L., Dempsey, A. F., & Brewer, N. T. (2014). The Vaccination Confidence Scale: A Brief Measure of Parents’ Vaccination Beliefs. Vaccine, 32(47), 6259–6265.
Gurwitz, D. (2021). COVID-19 vaccine hesitancy: Lessons from Israel. Vaccine, 39(29), 3785–3786.
Huang, R., Moudon, A. V., Cook, A. J., & Drewnowski, A. (2015). The spatial clustering of obesity: does the built environment matter? Journal of Human Nutrition and Dietetics, 28(6), 604–612. https://doi.org/10.1111/jhn.12279
Jahanbin, K., & Rahmanian, V. (2020). Using twitter and web news mining to predict COVID-19 outbreak. Asian Pacific Journal of Tropical Medicine, 13(8), 378.
Konstantinou, P., Georgiou, K., Kumar, N., Kyprianidou, M., Nicolaides, C., Karekla, M., and Kassianos, A. P. (2021). Transmission of Vaccination Attitudes and Uptake Based on Social Contagion Theory: A Scoping Review. Vaccines, 9(6):607. Number: 6 Publisher: Multidisciplinary Digital Publishing Institute.
Kuhlman, C., Kumar, V. S. A., Marathe, M., Swarup, S., Tuli, G., Ravi, S. S., and Rosenkrantz, D. J. (2011). A Bi-Threshold Model of Complex Contagion and its Application to the Spread of Smoking Behavior. In Proceedings of the workshop on social network mining and analysis, SNA-KDD 2011.
Lam, C. N., Kaplan, C., & Saluja, S. (2022). Relationship between mask wearing, testing, and vaccine willingness among Los Angeles County adults during the peak of the COVID-19 pandemic. Translational Behavioral Medicine, 12(3), 480–485.
Lamsal, R., Harwood, A., & Read, M. R. (2022). Twitter conversations predict the daily confirmed COVID-19 cases. Applied Soft Computing, 129, 109603.
Li, T., & Zhang, Y. (2015). Social network types and the health of older adults: Exploring reciprocal associations. Social Science & Medicine, 130, 59–68.
Loomba, S., de Figueiredo, A., Piatek, S. J., de Graaf, K., and Larson, H. J. (2021). Measuring the impact of COVID-19 vaccine misinformation on vaccination intent in the UK and USA. Nature Human Behaviour, 5(3):337–348. Number: 3 Publisher: Nature Publishing Group.
MacDonald, N. E. (2015). Vaccine hesitancy: Definition, scope and determinants. Vaccine, 33(34), 4161–4164.
Malik, A. A., McFadden, S. M., Elharake, J., & Omer, S. B. (2020). Determinants of COVID-19 vaccine acceptance in the US. EClinicalMedicine, 26, 100495.
Mehta, B., Jannat-Khah, D., Mancuso, C. A., Bass, A. R., Moezinia, C. J., Gibofsky, A., Goodman, S. M., & Ibrahim, S. (2020). Geographical variations in COVID-19 perceptions and patient management: a national survey of rheumatologists. Seminars in Arthritis and Rheumatism, 50(5), 1049–1054.
Narayan, D. and Pritchett, L. (2000). Social capital: Evidence and implications. Social capital: A multifaceted perspective, pages 269–295. Publisher: The World Bank Washington, DC.
Notarte, K. I., Catahay, J. A., Velasco, J. V., Pastrana, A., Ver, A. T., Pangilinan, F. C., Peligro, P. J., Casimiro, M., Guerrero, J. J., Gellaco, M. M. L., Lippi, G., Henry, B. M., and Fernández-de-las Peñas, C. (2022). Impact of COVID-19 vaccination on the risk of developing long-COVID and on existing long-COVID symptoms: A systematic review. eClinicalMedicine, 53:101624.
Paluck, E. L., Shepherd, H., & Aronow, P. M. (2016). Changing climates of conflict: A social network experiment in 56 schools. Proceedings of the National Academy of Sciences, 113(3), 566–571.
Perry, M., Akbari, A., Cottrell, S., Gravenor, M. B., Roberts, R., Lyons, R. A., Bedston, S., Torabi, F., & Griffiths, L. (2021). Inequalities in coverage of COVID-19 vaccination: A population register based cross-sectional study in Wales, UK. Vaccine, 39(42), 6256–6261.
Piotrowski, M. (2006). The effect of social networks at origin communities on migrant remittances: evidence from Nang Rong District. European Journal of Population/Revue Europenne de Dmographie, 22(1):67–94. Publisher: Springer.
Poros, M. (2011). Migrant social networks: Vehicles for migration, integration, and development. Migration Policy Institute, 30.
Rabb, N., Bowers, J., Glick, D., Wilson, K. H., and Yokum, D. (2022). The influence of social norms varies with “others” groups: Evidence from COVID-19 vaccination intentions. Proceedings of the National Academy of Sciences, 119(29):e2118770119. Publisher: Proceedings of the National Academy of Sciences.
Rashed, E. A., & Hirata, A. (2021). Infectivity Upsurge by COVID-19 Viral Variants in Japan: Evidence from Deep Learning Modeling. International Journal of Environmental Research and Public Health, 18(15), 7799.
Raveendran, A. V., Jayadevan, R., & Sashidharan, S. (2021). Long COVID: An overview. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 15(3), 869–875.
Rickert, V. I., Auslander, B. A., Cox, D. S., Rosenthal, S. L., Rickert, J. A., Rupp, R., & Zimet, G. D. (2014). School-based vaccination of young US males: Impact of health beliefs on intent and first dose acceptance. Vaccine, 32(17), 1982–1987.
Rosenberg, E. S., Dorabawila, V., Easton, D., Bauer, U. E., Kumar, J., Hoen, R., Hoefer, D., Wu, M., Lutterloh, E., Conroy, M. B., Greene, D., & Zucker, H. A. (2022). Covid-19 Vaccine Effectiveness in New York State. New England Journal of Medicine, 386(2), 116–127.
Sadasivuni, S. T. and Zhang, Y. (2020). Using Gradient Methods to Predict Twitter Users’ Mental Health with Both COVID-19 Growth Patterns and Tweets. In 2020 IEEE International Conference on Humanized Computing and Communication with Artificial Intelligence (HCCAI), pages 65–66.
Sallam, M. (2021). COVID-19 Vaccine Hesitancy Worldwide: A Concise Systematic Review of Vaccine Acceptance Rates. Vaccines, 9(2), 160.
Santonja, F. J., Morales, A., Villanueva, R. J., & Corts, J. C. (2012). Analysing the effect of public health campaigns on reducing excess weight: A modelling approach for the Spanish Autonomous Region of the Community of Valencia. Evaluation and Program Planning, 35(1), 34–39.
Sattar, N. S., & Arifuzzaman, S. (2021). COVID-19 Vaccination Awareness and Aftermath: Public Sentiment Analysis on Twitter Data and Vaccinated Population Prediction in the USA. Applied Sciences, 11(13), 6128.
Shilo, S., Rossman, H., & Segal, E. (2021). Signals of hope: gauging the impact of a rapid national vaccination campaign. Nature Reviews Immunology, 21(4), 198–199.
Southwell, B. G. (2013). Social Networks and Popular Understanding of Science and Health: Sharing Disparities. JHU Press.
Stokols, D. (1992). Establishing and maintaining healthy environments: Toward a social ecology of health promotion. American Psychologist, 47:6–22. Place: US Publisher: American Psychological Association.
Tran, T. Q. and Sakuma, J. (2019). Seasonal-adjustment Based Feature Selection Method for Predicting Epidemic with Large-scale Search Engine Logs. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD ’19, pages 2857–2866, New York, NY, USA. Association for Computing Machinery.
Tu, B., Wei, L., Jia, Y., & Qian, J. (2021). Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: - evidence from Baidu index. BMC Infectious Diseases, 21(1), 98.
Valente, T. W. (2010). Social Networks and Health: Models, Methods, and Applications. Oxford: Oxford University Press.
Valente, T. W., Watkins, S. C., Jato, M. N., Van Der Straten, A., & Tsitsol, L.-P.M. (1997). Social network associations with contraceptive use among Cameroonian women in voluntary associations. Social Science & Medicine, 45(5), 677–687.
Vulpe, S. N., & Rughiniş, C. (2021). Social amplification of risk and probable vaccine damage: A typology of vaccination beliefs in 28 European countries. Vaccine, 39(10), 1508–1515.
Weissman, G. E., Crane-Droesch, A., Chivers, C., Luong, T., Hanish, A., Levy, M. Z., Lubken, J., Becker, M., Draugelis, M. E., Anesi, G. L., Brennan, P. J., Christie, J. D., Hanson, C. W., Mikkelsen, M. E., & Halpern, S. D. (2020). Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic. Annals of Internal Medicine, 173(1), 21–28.
Wende, M. E., Stowe, E. W., Eberth, J. M., McLain, A. C., Liese, A. D., Breneman, C. B., Josey, M. J., Hughey, S. M., and Kaczynski, A. T. (2021). Spatial clustering patterns and regional variations for food and physical activity environments across the United States. International Journal of Environmental Health Research, 31(8):976–990. Publisher: Taylor & Francis _eprint: https://doi.org/10.1080/09603123.2020.1713304.
Woo, H., Cho, Y., Shim, E., Lee, J.-K., Lee, C.-G., & Kim, S. H. (2016). Estimating Influenza Outbreaks Using Both Search Engine Query Data and Social Media Data in South Korea. Journal of Medical Internet Research, 18(7), e4955.
World Health Organization. (2022). WHO Coronavirus (COVID-19) Dashboard. Technical report: World Health Organization.
Wouters, O. J., Shadlen, K. C., Salcher-Konrad, M., Pollard, A. J., Larson, H. J., Teerawattananon, Y., & Jit, M. (2021). Challenges in ensuring global access to COVID-19 vaccines: production, affordability, allocation, and deployment. The Lancet, 397(10278), 1023–1034.
Youm, Y., & Laumann, E. O. (2002). Social network effects on the transmission of sexually transmitted diseases. Sexually Transmitted Diseases, 29(11), 689–697.
Youm, Y., Laumann, E. O., Ferraro, K. F., Waite, L. J., Kim, H. C., Park, Y.-R., Chu, S. H., Joo, W.-T., & Lee, J. A. (2014). Social network properties and self-rated health in later life: comparisons from the Korean social life, health, and aging project and the national social life, health and aging project. BMC Geriatrics, 14(1), 102.
Zeroual, A., Harrou, F., Dairi, A., & Sun, Y. (2020). Deep learning methods for forecasting COVID-19 time-Series data: a comparative study. Chaos, Solitons & Fractals, 140, 110121.
Zhang, J., & Centola, D. (2019). Social networks and health: new developments in diffusion, online and offline. Annual Review of Sociology, 45(1), 91–109.
Zhou, X., & Li, Y. (2022). Forecasting the COVID-19 vaccine uptake rate: an infodemiological study in the US. Human Vaccines & Immunotherapeutics, 18(1), 2017216.
Zhou, X., Li, Y., Correa, A., Salustri, F., & Skordis, J. (2023). The need for voices from the grassroots in China’s public health system. The Lancet Regional Health - Western Pacific, 32, 100743.
Acknowledgements
Y. Li and X. Zhou planned the study and developed the framework. Y. Li wrote the initial paper, and X. Zhou and Z. Lyv participated in the writing and revising of the paper. All authors participated in the data analysis and reviewed the final draft of the paper. Additionally, we appreciate the valuable comments from Jonathan Clindaniel and Sanja Miklin on earlier versions of the manuscript.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
We declare that there is no conflict of interest.
Authors’ Google Scholar Profiles:
Yiang Li: https://scholar.google.com/citations?user=Qljet7wAAAAJ &hl=en Xingzuo Zhou: https://scholar.google.com/citations?user=f3E2ExYAAAAJ &hl=en Zejian Lyv: not available
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Li, Y., Zhou, X. & Lyu, Z. Regional contagion in health behaviors: evidence from COVID-19 vaccination modeling in England with social network theorem. J Comput Soc Sc 7, 197–216 (2024). https://doi.org/10.1007/s42001-023-00232-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s42001-023-00232-9