Why We Need More Data before the Next Pandemic


  • Nigel Gilbert Centre for Research in Social Simulation (CRESS), University of Surrey https://orcid.org/0000-0002-5937-2410
  • Edmund Chattoe-Brown School of Media, Communication and Sociology, University of Leicester https://orcid.org/0000-0001-8232-6896
  • Christopher Watts Independent researcher, Cambridgeshire https://orcid.org/0000-0002-0861-9815
  • Duncan Robertson School of Business and Economics, Loughborough University https://orcid.org/0000-0002-7801-5451




Covid-19, time use data, epidemiological model, social network, social contacts


Attempts to control the current pandemic through public health interventions have been driven by predictions based on modelling, thus bringing epidemiological models to the forefront of policy and public interest. It is almost inevitable that there will be further pandemics and controlling, suppressing and ameliorating their effects will undoubtedly involve the use of models. However, the accuracy and usefulness of models are highly dependent on the data that are used to calibrate and validate them. In this article, we consider the data needed by the two main types of epidemiological modelling (compartmental and agent-based) and the adequacy of the currently available data sources. We conclude that at present the data for epidemiological modelling of pandemics is seriously deficient and we make suggestions about how it would need to be improved. Finally, we argue that it is important to initiate efforts to collect appropriate data for modelling now, rather than waiting for the next pandemic.


Abbott, A. (2016). Processual Sociology. Chicago: University of Chicago Press.

Alteri, L., Parks, L., Raffini, L., & Vitale, T. (2021). Covid-19 and the Structural Crisis of Liberal Democracies. Determinants and Consequences of the Governance of Pandemic. Partecipazione e Conflitto, 14(1), 1–37. https://doi.org/10.1285/i20356609v14i1p01

Anderson, R.M. & May, R.M. (1992). Infectious Diseases of Humans: Dynamics and Control. Oxford: Oxford University Press.

Azad, S. & Devi, S. (2020). Tracking the Spread of Covid-19 in India via Social Networks in the Early Phase of the Pandemic. Journal of Travel Medicine, 27(8), taaa130. https://doi.org/10.1093/jtm/taaa130

Biggs, A.T. & Littlejohn, L.F. (2021). Revisiting the Initial Covid-19 Pandemic Projections. The Lancet Microbe, 2(3), e91–e92. https://doi.org/10.1016/S2666-5247(21)00029-X

Birrell, P., Blake, J., van Leeuwen, E., Gent, N., & De Angelis, D. (2021). Real-Time Nowcasting and Forecasting of Covid-19 Dynamics in England: The First Wave. Philosophical Transactions of the Royal Society B, 376(1829), 20200279. https://doi.org/10.1098/rstb.2020.0279

Bontempi, E., Vergalli, S., & Squazzoni, F. (2020). Understanding Covid-19 Diffusion Requires an Interdisciplinary, Multi-dimensional Approach. Environmental Research, 188, 109814. https://doi.org/10.1016/j.envres.2020.109814

Brooks-Pollock, E., Christensen, H., Trickey, A., Hemani, G., Nixon, E., Thomas, A.C., & Danon, L. (2021). High Covid-19 Transmission Potential Associated with Re-opening Universities Can Be Mitigated with Layered Interventions. Nature Communications, 12, 1507. https://doi.org/10.1038/s41467-021-25169-3

Bruno, I., Didier, E., & Vitale, T. (2014). Statactivism: Forms of Action between Disclosure and Affirmation. Partecipazione e Conflitto, 7(2), 198–220. https://doi.org/10.1285/i20356609v7i2p198

Burgess, S., Ponsford, M.J., & Gill, D. (2020). Editorial: Are We Underestimating Seroprevalence of SARS-CoV-2? British Medical Journal, 370, m3364. https://doi.org/10.1136/bmj.m3364

Cadman, P. & Freeman, S. (2020). A New Proximity Risk Calculation for the NHS Test & Trace Covid App. Zuehlke. https://www.zuehlke.com/en/insights/a-new-proximity-risk-calculation-for-the-nhs-test-trace-covid-app

Carrella, E. (2021). No Free Lunch When Estimating Simulation Parameters. Journal of Artificial Societies and Social Simulation, 24(2), 7. https://doi.org/10.18564/jasss.4572

Carrella, E., Bailey, R., & Madsen, J.K. (2020). Calibrating Agent-Based Models with Linear Regressions. Journal of Artificial Societies and Social Simulation, 23(1), 7. https://doi.org/10.18564/jasss.4150

Cevik, M., Marcus, J.L., Buckee, C., & Smith, T.C. (2020). Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Transmission Dynamics Should Inform Policy. Clinical Infectious Diseases, 73(Supplement 2), S170–S176. https://doi.org/10.1093/cid/ciaa1442

Chang, S.L., Harding, N., Zachreson, C., Cliff, O.M., & Prokopenko, M. (2020). Modelling Transmission and Control of the Covid-19 Pandemic in Australia. Nature Communications, 11, 5710. https://doi.org/10.1038/s41467-020-19393-6

Chattoe-Brown, E. (2021). Why Questions Like "Do Networks Matter?" Matter to Methodology: How Agent-Based Modelling Makes It Possible to Answer Them. International Journal of Social Research Methodology, 24(4), 429–442. https://doi.org/10.1080/13645579.2020.1801602

Chua, A.Q., Al Knawy, B., Grant, B., Legido-Quigley, H., Lee, W.-C., Leung, G.M., & Maurer-Stroh, S. (2021). How the Lessons of Previous Epidemics Helped Successful Countries Fight Covid-19. British Medical Journal, 372, n486. https://doi.org/10.1136/bmj.n486

Conlan, A.J.K., Klepac, P., Kucharski, A.J., Kissler, S.M., Tang, M.L., Fry, H., & Gog, J.R. (2021). Human Mobility Data from the BBC Pandemic Project. medRxiv. https://doi.org/10.1101/2021.02.19.21252079

CORDIS (2009). Improving Public Health Policy in Europe through Modelling and Economic Evaluation of Interventions for the Control of Infectious Diseases. CORDIS EU Research Results. https://cordis.europa.eu/project/id/502084

Daly, M. (2020). Covid-19 and Care Homes in England: What Happened and Why? Social Policy and Administration, 54(7), 985–998. https://doi.org/10.1111/spol.12645

Davies, N.G., Klepac, P., Liu, Y., Prem, K., Jit, M., CMMID Covid-19 Working Group, & Eggo, R.M. (2020a). Age-dependent Effects in the Transmission and Control of Covid-19 Epidemics. Nature Medicine, 26, 1205–1211. https://doi.org/10.1038/s41591-020-0962-9

Davies, N.G., Kucharski, A.J., Eggo, R.M., Gimma, A., & Edmunds, W.J. on behalf of the Centre for the Mathematical Modelling of Infectious Diseases Covid-19 Working Group (2020b). Effects of Non-pharmaceutical Interventions on Covid-19 Cases, Deaths, and Demand for Hospital Services in the UK: A Modelling Study. Lancet Public Health, 5(7), E375–E385. https://doi.org/10.1016/S2468-2667(20)30133-X

Denford, S., Morton, K.S., Lambert, H., Zhang, J., Smith, L.E., Rubin, J.G., & Yardley, L. (2020). Understanding Patterns of Adherence to Covid-19 Mitigation Measures: A Qualitative Interview Study. medRxiv. https://doi.org/10.1101/2020.12.11.20247528

Dignum, F. (Ed.). (2021). Social Simulation for a Crisis: Results and Lessons from Simulating the Covid-19 Crisis. Cham: Springer.

Dimeglio, C., Miedougé, M., Loubes, J.M., Mansuy, J.M., & Izopet, J. (2021). Side Effect of a 6 pm Curfew for Preventing the Spread of SARS-CoV-2: A Modeling Study from Toulouse, France. Journal of Infection, 82(5), 186–230. https://doi.org/10.1016/j.jinf.2021.01.021

Elgethun, K., Yost, M.G., Fitzpatrick, C.T., Nyerges, T.L., & Fenske, R.A. (2007). Comparison of Global Positioning System (GPS) Tracking and Parent-Report Diaries to Characterize Children's Time-Location Patterns. Journal of Exposure Science and Environmental Epidemiology, 17(2), 196–206. https://doi.org/10.1038/sj.jes.7500496

Endo, A., Abbott, S., Kucharski, A., & Funk, S. (2020). Estimating the Overdispersion in Covid-19 Transmission Using Outbreak Sizes outside China [version 3; peer review: 2 approved]. Wellcome Open Research, 5, 67. https://doi.org/10.12688/wellcomeopenres.15842.3

Endo, A., Leclerc, Q., Knight, G., Medley, G., Atkins, K., Funk, S., & Kucharski, A. (2021). Implication of Backward Contact Tracing in the Presence of Overdispersed Transmission in Covid-19 Outbreaks [version 2; peer review: 2 approved]. Wellcome Open Research, 5, 239. https://doi.org/10.12688/wellcomeopenres.16344.2

Farjam, M., Bianchi, F., Squazzoni, F., & Bravo, G. (2021). Dangerous Liaisons: An Online Experiment on the Role of Scientific Experts and Politicians in Ensuring Public Support for Anti-Covid Measures. Royal Society Open Science, 8(3), 201310. https://doi.org/10.1098/rsos.201310

Ferguson, N.M., Laydon, D., Nedjati-Gilani, G., Imai, N., Ainslie, K., Baguelin, M., & Ghani, A.C. (2020). Report 9 -- Impact of Non-Pharmaceutical Interventions (NPIs) to Reduce Covid-19 Mortality and Healthcare Demand. https://www.imperial.ac.uk/mrc-global-infectious-disease-analysis/covid-19/report-9-impact-of-npis-on-covid-19/

Funk, S. (2018). Socialmixr: Social Mixing Matrices for Infectious Disease Modelling [data collection]. The Comprehensive R Archive Network, R package version 0.0.1. https://CRAN.R-project.org/package=socialmixr

Gershuny, J., Sullivan, O., Sevilla, A., Vega-Rapun, M., Foliano, F., Lamote de Grignon, J., & Walthery, P. (2021). A New Perspective from Time Use Research on the Effects of Social Restrictions on Covid-19 Behavioral Infection Risk. PLOS ONE, 16(2), e0245551. https://doi.org/10.1371/journal.pone.0245551

Gilbert, N. (2019). Agent-Based Models (2nd ed.). London: Sage.

Gmel, G. & Daeppen, J.B. (2007). Recall Bias for Seven-Day Recall Measurement of Alcohol Consumption among Emergency Department Patients: Implications for Case-Crossover Designs. Journal of Studies on Alcohol and Drugs, 68(2), 303–310. https://doi.org/10.15288/jsad.2007.68.303

Hendry, D.F. & Richard, J.F. (1983). The Econometric Analysis of Economic Time Series. International Statistical Review, 51(2), 111–148. https://doi.org/10.2307/1402738

Hills, S. & Eraso, Y. (2021). Factors Associated with Non-Adherence to Social Distancing Rules during the Covid-19 Pandemic: A Logistic Regression Analysis. BMC Public Health, 21, 352. https://doi.org/10.1186/s12889-021-10379-7

Hoeben, E.M., Bernasco, W., Liebst, L.S., van Baak, C., & Lindegaard R.M. (2021). Social Distancing Compliance: A Video Observational Analysis. PLOS ONE, 16(3), e0248221. https://doi.org/10.1371/journal.Pone.0248221

Improbable (2020). Synthetic Environment Technology Accelerates Pandemic Modelling. https://www.improbable.io/blog/improbable-synthetic-environment-technology-accelerates-uk-pandemic-modelling

Jorge, A., D'Silva, K., Cohen, A., Wallace, Z.S., McCormick, N., Zhang, Y., & Choi, H.K. (2021). Temporal Trends in Severe Covid-19 Outcomes in Patients with Rheumatic Disease: A Cohort Study. Lancet Rheumatology, 3(2), e131–e137. https://doi.org/10.1016/S2665-9913(20)30422-7

Khunti, K., Singh, A.K., Pareek, M., & Hanif, W. (2020). Is Ethnicity Linked to Incidence or Outcomes of Covid-19? British Medical Journal, 369, m1548. https://doi.org/10.1136/bmj.m1548

Kissler, S.M., Klepac, P., Tang, M., Conlan, A.J.K., & Gog, J.R. (2020). Sparking "The BBC Four Pandemic": Leveraging Citizen Science and Mobile Phones to Model the Spread of Disease. BioRxiv, 479154. https://doi.org/10.1101/479154

Klepac, P., Kissler, S., & Gog, J. (2018). Contagion! The BBC Four Pandemic -- the Model behind the Documentary. Epidemics, 24, 49–59. https://doi.org/10.1016/j.epidem.2018.03.003

Lee, D., Heo, K., & Seo, Y. (2020). Covid-19 in South Korea: Lessons for Developing Countries. World Development, 135, 105057. https://doi.org/10.1016/j.worlddev.2020.105057

Lewis, D. (2021). Superspreading Drives the Covid Pandemic -- and Could Help to Tame It. Nature, 590(7847), 544–546. https://doi.org/10.1038/d41586-021-00460-x

Lorig, F., Johansson, E., & Davidsson, P. (2021). Agent-Based Social Simulation of the Covid-19 Pandemic: A Systematic Review. Journal of Artificial Societies and Social Simulation, 24(3), 5. https://doi.org/10.18564/jasss.4601

Mahmood, I., Arabnejad, H., Suleimenova, D., Sassoon, I., Marshan, A. Serrano-Rico, A. & Groen, D. (2020). FACS: A Geospatial Agent-Based Simulator for Analysing Covid-19 Spread and Public Health Measures on Local Regions. Journal of Simulation. https://doi.org/10.1080/17477778.2020.1800422

Manzo, G. (2020). Complex Social Networks Are Missing in the Dominant Covid-19 Epidemic Models. Sociologica, 14(1), 31–49. https://doi.org/10.6092/issn.1971-8853/10839

Manzo, G. & Van de Rijt, A. (2020). Halting SARS-CoV-2 by Targeting High-Contact Individuals. Journal of Artificial Societies and Social Simulation, 23(4), 10. https://doi.org/10.18564/jasss.4435

Mossong, J., Hens, N., Jit, M., Beutels, P., Auranen, K., Mikolajczyk, R., & Edmunds, W.J. (2008). Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases. PLOS Medicine, 5, e74. https://doi.org/10.1371/journal.pmed.0050074

Mossong, J., Hens, N., Jit, M., Beutels, P., Auranen, K., Mikolajczyk, R., & Edmunds, W.J. (2020). POLYMOD Social Contact Data (Version 2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.1043437

Mullan, K. & Chatzitheochari, S. (2019). Changing Times Together? A Time-Diary Analysis of Family Time in the Digital Age in the United Kingdom. Journal of Marriage and Family, 81(4), 795–811. https://doi.org/10.1111/jomf.12564

Newman, M.E.J. (2002). Spread of Epidemic Disease on Networks. Physical Review E, 66(1), 016128. https://doi.org/10.1103/PhysRevE.66.016128

Paolisso, M. & Hames, R. (2010). Time Diary versus Instantaneous Sampling: A Comparison of Two Behavioral Research Methods. Field Methods, 22(4), 357–377. https://doi.org/10.1177/1525822X10379200

Park, J. (2021). Governing a Pandemic with Data on the Contactless Path to AI: Personal Data, Public Health, and the Digital Divide in South Korea, Europe and the United States in Tracking of Covid-19. Partecipazione e Conflitto, 14(1), 79–112. https://doi.org/10.1285/i20356609v14i1p79

Prem, K., Cook, A.R., & Jit, M. (2017). Projecting Social Contact Matrices in 152 Countries Using Contact Surveys and Demographic Data. PLOS Computational Biology, 13(9), e1005697. https://doi.org/10.1371/journal.pcbi.1005697

Santamaria, C., Sermi, F., Spyratos, S., Iacus, S.M., Annunziato, A., Tarchi, D., & Vespe, M. (2020). Measuring the Impact of Covid-19 Confinement Measures on Human Mobility Using Mobile Positioning Data. A European Regional Analysis. Safety Science, 132, 104925. https://doi.org/10.1016/j.ssci.2020.104925

Santelli, A., Bammer, G., Bruno, I., Charters, E., Di Fiore, M., Didier, E., & Vineis, P. (2020). Five Ways to Ensure that Models Serve Society: A Manifesto. Nature Human Behaviour, 582, 482–484. https://doi.org/10.1038/d41586-020-01812-9

SoleimanvandiAzar, N., Irandoost, S.F., Ahmadi, S., Xosravi, T., Ranjbar, H., Mansourian, M., & Lebni, J.Y. (2021). Explaining the Reasons for Not Maintaining the Health Guidelines to Prevent Covid-19 in High-Risk Jobs: A Qualitative Study in Iran. BMC Public Health, 21(1), 1–15. https://doi.org/10.1186/s12889-021-10889-4

Squazzoni, F., Polhill, J.G., Edmonds, B., Ahrweiler, P., Antosz, P., Scholz, G. & 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. https://doi.org/10.18564/jasss.4298

Sullivan, O., Gershuny, J., Sevilla, A., Walthery, P., & Vega-Rapun, M. (2020). Time Use Diary Design for Our Times – an Overview, Presenting a Click-and-Drag Diary Instrument (CaDDI) for Online Application. Journal of Time Use Research, 15(1), 1–17. https://jtur.iatur.org/home/article/c73705a3-2c6f-46d4-9616-0f197e40455c




How to Cite

Gilbert, N., Chattoe-Brown, E., Watts, C., & Robertson, D. (2021). Why We Need More Data before the Next Pandemic. Sociologica, 15(3), 125–143. https://doi.org/10.6092/issn.1971-8853/13221



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