Disconnecting for the good: A network-oriented model for social contagion of opinions and social network interventions to increase adherence to social distancing


The pandemic of the new COVID-19 has raised many questions to a very connected society as to how to best respond to such a challenge at this current time. The best response so far is to call people for following the instructions from the World Health Organisation (WHO) as a way of reducing the spread of the virus and thus relieving the health system, striving to avoid a collapse. This work studies the spread of positive opinion on adhering to social distancing based on network topology and metrics using a network-oriented model for social contagion. It is shown that interventions based on social network measurements can be used to boost the spread of positive opinion about adhering to these measures. It is also shown that our model accounts for the relevance the health authorities have on encouraging people to partake in social distancing voluntarily.

Palavras-chave: contágio social, modelagem baseada em agentes, COVID-19, redes complexas


Araújo, E. (2018). Contagious: Modeling the Spread of Behaviours, Perceptions and Emotions in Social Networks. PhD thesis, Vrije Universiteit Amsterdam.

Araújo, E. and Treur, J. (2016). Analysis and refinement of a temporal-causal network model for absorption of emotions. In International Conference on Computational Collective Intelligence, pages 27–39. Springer.

Barabási, A.-L. and Albert, R. (1999). Emergence of scaling in random networks. science, 286(5439):509–512.

Bosse, T., Duell, R., Memon, Z. A., Treur, J., and Van Der Wal, C. N. (2009). A multiagent model for emotion contagion spirals integrated within a supporting ambient agent model. In International Conference on Principles and Practice of Multi-Agent Systems, pages 48–67. Springer.

Braunack-Mayer, A., Tooher, R., Collins, J. E., Street, J. M., and Marshall, H. (2013). Understanding the school community’s response to school closures during the h1n1 2009 influenza pandemic. BMC public health, 13(1):344.

Centola, D. (2018). How behavior spreads: The science of complex contagions, volume 3. Princeton University Press.

Chhatwal, J. and He, T. (2015). Economic evaluations with agent-based modelling: an introduction. Pharmacoeconomics, 33(5):423–433.

Dandekar, R. and Barbastathis, G. (2020). Quantifying the effect of quarantine control in covid-19 infectious spread using machine learning. medRxiv.

Erdös, P. and Rényi, A. (1960). On the evolution of random graphs. Publ. Math. Inst. Hung. Acad. Sci, 5(1):17–60.

Fang, Y., Nie, Y., and Penny, M. (2020). Transmission dynamics of the covid-19 outbreak and effectiveness of government interventions: A data-driven analysis. Journal of Medical Virology, 92(6):645–659.

Freeman, L. C. (1989). Social networks and the structure experiment. Research methods in social network analysis, pages 11–40.

Hennig, M., Brandes, U., Pfeffer, J., and Mergel, I. (2012). Studying social networks: A guide to empirical research. Campus Verlag.

Klôh, V., Silva, G., Ferro, M., Araújo, E., de Melo, C. B., de Andrade Lima, J. R. P., and Martins, E. R. (2020). The virus and socioeconomic inequality: An agent-based model to simulate and assess the impact of interventions to reduce the spread of covid-19 in Rio de Janeiro, Brazil. Brazilian Journal of Health Review (BJRH), pages 3647 – 3673. http://www.brjd.com.br/index.php/BJHR/article/view/9209.

Koo, J. R., Cook, A. R., Park, M., Sun, Y., Sun, H., Lim, J. T., Tam, C., and Dickens, B. L. (2020). Interventions to mitigate early spread of sars-cov-2 in singapore: a modelling study. The Lancet Infectious Diseases.

Kucharski, A., Russell, T., Diamond, C., Liu, Y., Edmunds, J., Funk, S., Eggo, R., Sun, F., Jit, M., Munday, J., Davies, N., Gimma, A., Zandvoort, K., Gibbs, H., Hellewell, J., Jarvis, C., Clifford, S., Quilty, B., Bosse, N., and Flasche, S. (2020). Early dynamics of transmission and control of covid-19: a mathematical modelling study. The Lancet Infectious Diseases.

Li, L., Yang, Z., Dang, Z., Meng, C., Huang, J., Meng, H., Wang, D., Chen, G., Zhang, J., Peng, H., and Shao, Y. (2020). Propagation analysis and prediction of the covid-19. Infectious Disease Modelling, 5:282 – 292.

Lin, Q., Zhao, S., Gao, D., Lou, Y., Yang, S., Musa, S. S., Wang, M. H., Cai, Y., Wang, W., Yang, L., and He, D. (2020). A conceptual model for the coronavirus disease 2019 (covid-19) outbreak in wuhan, china with individual reaction and governmental action. International Journal of Infectious Diseases, 93:211 – 216.

McPherson, M., Smith-Lovin, L., and Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual review of sociology, 27(1):415–444.

Saunders-Hastings, P., Hayes, B. Q., et al. (2017). Modelling community-control strategies to protect hospital resources during an influenza pandemic in ottawa, canada. PloS one, 12(6).

Sokolowski, J. A. and Banks, C. M. (2011). Principles of modeling and simulation: a multidisciplinary approach. John Wiley & Sons.

Treur, J. (2020). NETWORK-ORIENTED MODELING FOR ADAPTIVE NETWORKS: Designing Higher-order Adaptive Biological,... Mental and Social Network Models. Springer.

van Woudenberg, T., Simoski, B., Araújo, E., Bevelander, K., Burk, W., Smit, C., Buijs, L., Klein, M., and Buijzen, M. (2019). Simulated social network interventions to promote physical activity: Who should be the influence agents? Journal of Medical Internet Research, 21.

Walker, P. G., Whittaker, C., Watson, O., Baguelin, M., Ainslie, K., Bhatia, S., Bhatt, S., Boonyasiri, A., Boyd, O., Cattarino, L., et al. (2020). The global impact of covid-19 and strategies for mitigation and suppression. Technical Report 12, Imperial College London.

Watts, D. J. and Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. nature, 393(6684):440–442.

Webster, R. K., Brooks, S. K., Smith, L. E., Woodland, L., Wessely, S., and Rubin, G. J. (2020). How to improve adherence with quarantine: Rapid review of the evidence. Public Health.

Wolfram, C. (2020). An agent-based model of covid-19. Complex Systems, 29:87 105.

Zhou, X., Ma, X., Hong, N., Su, L., Ma, Y., He, J., Jiang, H., Liu, C., Shan, G., Zhu, W., Zhang, S., and Long, Y. (2020). Forecasting the worldwide spread of covid-19 based on logistic model and seir model. medRxiv.
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ARAÚJO, Eric; FERRO, Mariza; SILVA, Gabrieli. Disconnecting for the good: A network-oriented model for social contagion of opinions and social network interventions to increase adherence to social distancing. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 9. , 2020, Cuiabá. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2020 . p. 142-153. ISSN 2595-6094. DOI: https://doi.org/10.5753/brasnam.2020.11170.