Complex Social Networks are Missing in the Dominant COVID-19 Epidemic Models


  • Gianluca Manzo Gemass, CNRS, Sorbonne University



compartmental models, ego-centered networks, scale-free networks, small-world networks, agent-based computational models


In the COVID-19 crisis, compartmental models have been largely used to predict the macroscopic dynamics of infections and deaths and to assess different non-pharmaceutical interventions aimed to contain the microscopic dynamics of person-to-person contagions. Evidence shows that the predictions of these models are affected by high levels of uncertainty. However, the link between predictions and interventions is rarely questioned and a critical scrutiny of the dependency of interventions on model assumptions is missing in public debate. In this article, I have examined the building blocks of compartmental epidemic models so influential in the current crisis. A close look suggests that these models can only lead to one type of intervention, i.e., interventions that indifferently concern large subsets of the population or even the overall population. This is because they look at virus diffusion without modelling the topology of social interactions. Therefore, they cannot assess any targeted interventions that could surgically isolate specific individuals and/or cutting particular person-to-person transmission paths. If complex social networks are seriously considered, more sophisticated interventions can be explored that apply to specific categories or set of individuals with expected collective benefits. In the last section of the article, I sketch a research agenda to promote a new generation of network-driven epidemic models.


Adam, D. (2020). The Simulations Driving the World’s Response to COVID-19. How Epidemiologists Rushed to Model the Coronavirus Pandemic. Nature, 580, 316–318.

Ajelli, M., Gonçalves, B., Balcan, D., Colizza, V., Hu, H., Ramasco, J.J., Merler, S., & Vespignani, A. (2010). Comparing Large-Scale Computational Approaches to Epidemic Modeling: Agent-Based versus Structured Metapopulation Models. BMC Infectious Diseases, 10(190).

Aleta, A., Hisi, A.N.S., Meloni, S., Poletto, C., Colizza, V., & Moreno, Y. (2017). Human Mobility Networks and Persistence of Rapidly Mutating Pathogens. Royal Society Open Science, 4, 160914.

Badham, J., & Gilbert, N. (2015). TELL ME Design: Protective Behaviour During an Epidemic. CRESS Working Paper 2015(2), University of Surrey.

Barabási, A-L. (2014). Linked. New York: Basics Books.

Barabási, A-L., & Bonabeau, E. (2003). Scale-Free Networks. Scientific American, 288(5), 60–69.

Barrat, A., Barthélemy, M., & Vespignani, A. (2008). Dynamical Processes on Complex Networks. Cambridge: Cambridge University Press.

Barrat, A., Cattuto, C., Tozzi, A.E., Vanhems, P., & Voirin, N. (2014). Measuring Contact Patterns with Wearable Sensors: Methods, Data Characteristics and Applications to Data-Driven Simulations of Infectious Diseases. Clinical Microbiology and Infection, 20(1), 10–16.

Benzell, S., Collis, A., & Nicolaides, C. (2020). Rationing Social Contact During the COVID-19 Pandemic: Transmission Risk and Social Benefits of US Locations. OSF Preprints, Center for Open Science, April 18.

Block, P., Hoffman, M., Raabe, I.J., Beam Dowd, J., Rahal, C., Kashyap, R., & Mills, M.C. (2020). Social Network-Based Distancing Strategies to Flatten the Covid 19 Curve in a Post-Lockdown World. Eprint article, April 15. Available at

Brethouwer, J-T., van de Rijt, A., Lindelauf, R., Fokkink, R. (2020). “Stay Nearby or Get Checked”: A Covid-19 Lockdown Exit Strategy. Eprint article, April 11. Available at

Camacho, A., Kucharski, A.J., Lowe, R., Eggo, R.M., & Edmunds, W.J. (2019). Assessing the Performance of Real-Time Epidemic Forecasts: A Case Study of Ebola in the Western Area Region of Sierra Leone, 2014-2015. PLoS Computational Biology, 15(2), e1006785.

Cirillo, P., & Taleby, N.N. (2020). Tail Risk of Contagious Diseases. Nature Physics (forthcoming). Eprint article, April 18. Available at

Di Domenico, L., Pullano, G., Sabbatini, C.E., Boëlle, P-Y., & Colizza, V. (2020). Expected impact of lockdown in Île-de-France and possible exit strategies. Report 9, April 12. Available at

Duan, W., Fan, Z., Zhang, P., Guo, G., & Qiu, X. (2015). Mathematical and Computational Approaches to Epidemic Modeling: A Comprehensive Review. Frontiers in Computational Science, 9(5), 806–826.

Edmonds, B., Le Page, C., Bithell, M., Chattoe-Brown, E., Grimm, V., Meyer, R., Montañola-Sales, C., Ormerod, P., Root, H., & Squazzoni, F. (2019). Different Modelling Purposes. Journal of Artificial Societies and Social Simulation, 22(3), 1–6.

Enserink, M. & Kupferschmidt, K. (2020). Mathematics of Life and Death: How Disease Models Shape National Shutdowns and Other Pandemic Policies. Science, March 25.

Epstein, J.M. (2009). Modelling to Contain Pandemics. Nature, 460(7256): 687.

Ferguson, N.M., Laydon, D., Nedjati-Gilani, G., Imai, N., Ainslie, K., Baguelin, M., Bha- tia, S., Boonyasiri, A., Cucunubá, Z., Cuomo-Dannenburg, G., & Dighe, A. (2020). Impact of Non-Pharmaceutical Interventions (NPIs) to Reduce COVID-19 Mortality and Healthcare Demand. Report 9. Imperial College COVID-19 Response Team, London, March 16.

Flaxman, S., Mishra, S., Gandy, A., et al. (2020). Estimating the Number of Infections and the Impact of Non-Pharmaceutical Interventions on COVID-19 in 11 European Countries. Report, Imperial College London. Available at:

Génois, M., & Barrat, A. (2018). Can Co-Location Be Used as a Proxy for Face-to-Face Contacts?. EPJ Data Science, 7(11).

Génois, M., Vestergaard, C.L., Cattuto, C., & Barrat, A. (2015). Compensating for Population Sampling in Simulations of Epidemic Spread on Temporal Contact Networks. Nature Communications, 6, 8860.

Granovetter, M. (1973). The Strength of Weak Ties. American Journal of Sociology, 78(6), 1360–1380.

Grefenstette, J.J., Brown, S.T., Rosenfeld, R. et al. (2013). FRED (A Framework for Reconstructing Epidemic Dynamics): An Open-Source Software System for Modeling Infectious Diseases and Control Strategies Using Census-Based Populations. BMC Public Health, 13(940).

Heckathorn, D.D., & Cameron, C.J. (2017). Network Sampling: From Snowball and Multiplicity to Respondent-Driven Sampling. Annual Review of Sociology, 43, 101–119.

Jackson, M.O., & Lopez-Pintado, D. (2013). Diffusion and Contagion in Networks with Heterogeneous Agents and Homophily. Network Science, 1(1), 49–67.

James, A., Pitchford, J.W., & Plank, M.J. (2007). An Event-Based Model of Superspreading in Epidemics. Proc. Biol. Sci. Proceedings B, 274(1610), 741–747.

Keeling, M.J., & Eames, K.T. (2005). Networks and Epidemic Models. Journal of the Royal Society Interface, 2(4), 295–307.

Keeling, M.J., & Rohani, P. (2008). Modeling Infectious Diseases in Humans and Animals. Princeton, NJ: Princeton University Press.

Kirman, A.P. (1992). Whom or What Does the Representative Individual Represent?. Journal of Economic Perspectives, 6(2), 117–136.

Kissler, S., Tedijanto, C., Lipsitch, M., & Grad, Y.H. (2020). Social Distancing Strategies for Curbing the COVID-19 Epidemic 2020. Pre-print article available at:

Lancichinetti, A., Kivelä, M., Saramäki, J., & Fortunato, S. (2010). Characterizing the Community Structure of Complex Networks. PLOS ONE, 5(8), e11976.

Landler, M., & Castle, S. (2020). Behind the Virus Report That Jarred the U.S. and the U.K. to Action. The New York Times, March 17.

Le Monde (2020). Sur la piste du patient “zéro”. April 10, 20–21.

Liljeros, F., Edling, C.R., Nunes Amaral, L.A., Stanley, H.E., & Aberg, Y. (2001). The Web of Human Sexual Contacts. Nature, 411(6840), 907–908.

Manzo, G. (2020). Il faut intégrer la structure des interactions sociales dans les modèles de diffusion de l’épidémie. Le Monde, April 14.

Montes, F., Jaramillo, A.M., Meisel, J.D., Diaz-Guilera, A., Valdivia, J.A., Sarmiento, O.L., & Zarama, R. (2020). Benchmarking Seeding Strategies for Spreading Processes in Social Networks: An Interplay between Influencers, Topologies and Sizes. Scientific Reports, 10(3666).

Moody, J., & Benton, R.A. (2016). Interdependent Effects of Cohesion and Concurrency for Epidemic Potential. Annals of Epidemiology, 26(4), 241-248.

Morris, M., & Kretzschmar, M. (1997). Concurrent Partnerships and the Spread of HIV. AIDS, 11(5), 641–648.

Newman, M.E.J. (2003). The Structure and Function of Complex Networks. SIAM Review, 45(2), 167–256.

Onnela, J.-P., Arbesman, S., Gonzalez, M.C., Barabási, A.L. & Christakis, N.A. (2011). Geographic Constraints on Social Network Groups. PLoS ONE, 6(4), e16939.

Onnela J.-P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., Kertesz, J., & Barabási, A.-L. (2007). Structure and Tie Strengths in Mobile Communication Networks. PNAS, 104(18), 7332–7336.

Parker, J., & Epstein, J.M. (2011). A Distributed Platform for Global-Scale Agent-Based Models of Disease Transmission. ACM Transactions on Modeling and Computer Simulation, 22(1), 2.

Rivers, C., Martin, E., Meyer, D., Inglesby, T.V., & Cicero, A.J. (2020). Modernizing and Expanding Outbreak Science to Support Better Decision Making During Public Health Crises: Lessons for COVID-19 and Beyond. Report, The Johns Hopkins Center for Health Security. Available at

Rocha, L.E.C., Liljeros, F., & Holme, P. (2011). Simulated Epidemics in an Empirical Spatiotemporal Network of 50,185 Sexual Contacts. PLoS Computational Biology, 7(3), e1001109.

Sapienza, A., Barrat, A., Cattuto, C., & Gauvin, L. (2018). Estimating the Outcome of Spreading Processes on Networks with Incomplete Information: A Dimensionality Reduction Approach. Physical Review E, 98(1), 012317.

Siegenfeld, A.F., & Bar-Yam, Y. (2020). Eliminating COVID-19: A Community-Based Analysis Eprint article, March 23. Available at

Smith, J.A. (2012). Macrostructure from Microstructure: Generating Whole Systems from Ego Networks. Sociological Methodology, 42(1), 155–205.

Smith, J.A., Moody, J., & Morgan, J.H. (2017). Network Sampling Coverage II: The Effect of Non-Random Missing Data on Network Measurement. Social Networks, 48, 78–99.

Squazzoni, F., Polhill, J.G., Edmonds, B., Ahrweiler, P., Antosz, P., Scholz, G., Chappin, É., Borit, M., Verhagen, H., Giardini, F., & Gilbert, N. (2020). Computational Models That Matter During a Global Pandemic Outbreak: A Call to Action. Journal of Artificial Societies and Social Simulation, 23(2), 10.

Stopczynski, A., Sekara, V., Sapiezynski, P., Cuttone, A., Madsen, M.M., Larsen, J.E., et al. (2014). Measuring Large-Scale Social Networks with High Resolution. PLoS ONE, 9(4), e95978.

Walker, P.G.T., Whittaker, C., Watson, O. et al. (2020). The Global Impact of COVID-19 and Strategies for Mitigation and Suppression. Report, Imperial College London.

Watts, D.J., & Strogatz, S.H. (1998). Collective Dynamics of “Small-World” Networks. Nature, 393, 440–442.




How to Cite

Manzo, G. (2020). Complex Social Networks are Missing in the Dominant COVID-19 Epidemic Models. Sociologica, 14(1), 31–49.



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