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Abstract

BACKGROUND

Advanced outbreak analytics were instrumental in informing governmental decision-making during the COVID-19 pandemic. However, systematic evaluations of how modelling practices, data use and science–policy interactions evolved during this and previous emergencies remain scarce.

AIM

This study assessed the evolution of modelling practices, data usage, gaps, and engagement between modellers and decision-makers to inform future global epidemic intelligence.

METHODS

We conducted a two-stage semiquantitative survey among modellers in a large European epidemic intelligence consortium. Responses were analysed descriptively across early, mid- and late-pandemic phases. We used policy citations in Overton to assess policy impact.

RESULTS

Our sample included 66 modelling contributions from 11 institutions in four European countries. COVID-19 modelling initially prioritised understanding epidemic dynamics; evaluating non-pharmaceutical interventions and vaccination impacts later became equally important. Traditional surveillance data (e.g. case line lists) were widely available in near-real time. Conversely, real-time non-traditional data (notably social contact and behavioural surveys) and serological data were frequently reported as lacking. Gaps included poor stratification and incomplete geographical coverage. Frequent bidirectional engagement with decision-makers shaped modelling scope and recommendations. However, fewer than half of the studies shared open-access code.

CONCLUSIONS

We highlight the evolving use and needs of modelling during public health crises. Persistent gaps in the availability of non-traditional data underscore the need to rethink sustainable data collection and sharing practices, including from for-profit providers. Future preparedness should focus on strengthening collaborative platforms, research consortia and modelling networks to foster data and code sharing and effective collaboration between academia, decision-makers and data providers.

© Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
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2025-10-23
2025-11-11
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References

  1. Mathieu E, Ritchie H, Rodes-Guirao L, Appel C, Gavrilov D, Giattino C, et al. Excess mortality during the coronavirus pandemic (COVID-19). Our World in Data; 2020. [cited 2024 Aug 4]. Available from: https://ourworldindata.org/excess-mortality-covid
  2. Laurenson-Schafer H, Sklenovská N, Hoxha A, Kerr SM, Ndumbi P, Fitzner J, et al. . Description of the first global outbreak of mpox: an analysis of global surveillance data. Lancet Glob Health. 2023;11(7):e1012-23.  https://doi.org/10.1016/S2214-109X(23)00198-5  PMID: 37349031 
  3. The hard choices covid policymakers face: Epidemiological models are among their only guides. London: The Economist Newspaper Limited; April 2020. Available from: https://www.economist.com/briefing/2020/04/04/the-hard-choices-covid-policymakers-face
  4. How epidemiology has shaped the COVID pandemic. Nature. 2021;589(7843):491-2.  https://doi.org/10.1038/d41586-021-00183-z  PMID: 33505041 
  5. van Elsland SL, O’Hare RM, McCabe R, Laydon DJ, Ferguson NM, Cori A, et al. Policy impact of the Imperial College COVID-19 Response Team: global perspective and United Kingdom case study. Health Res Policy Syst. 2024;22(1):153.  https://doi.org/10.1186/s12961-024-01236-1  PMID: 39538321 
  6. Brooks-Pollock E, Danon L, Jombart T, Pellis L. Modelling that shaped the early COVID-19 pandemic response in the UK. Philos Trans R Soc Lond B Biol Sci. 2021;376(1829):20210001.  https://doi.org/10.1098/rstb.2021.0001  PMID: 34053252 
  7. Jit M, Ainslie K, Althaus C, Caetano C, Colizza V, Paolotti D, et al. Reflections on epidemiological modeling to inform policy during the COVID-19 pandemic in western Europe, 2020-23. Health Aff (Millwood). 2023;42(12):1630-6.  https://doi.org/10.1377/hlthaff.2023.00688  PMID: 38048502 
  8. Christen P, Van Elsland S, Saulo D, Cori A, Fitzner J. Advanced analytics to inform decision making during public health emergencies. Geneva: World Health Organization, London: Imperial College London; 2024. Available from: https://doi.org/10.25561/108600
  9. European Centre for Disease Prevention and Control (ECDC). Public health and social measures for health emergencies and pandemics in the EU/EEA: recommendations for strengthening preparedness planning. Stockholm: ECDC; 2024. Available from: https://data.europa.eu/doi/10.2900/253991
  10. Haw DJ, Morgenstern C, Forchini G, Johnson R, Doohan P, Smith PC, et al. Data needs for integrated economic-epidemiological models of pandemic mitigation policies. Epidemics. 2022;41:100644.  https://doi.org/10.1016/j.epidem.2022.100644  PMID: 36375311 
  11. Meyer-Rath G, Hounsell RA, Pulliam JR, Jamieson L, Nichols BE, Moultrie H, et al. The role of modelling and analytics in South African COVID-19 planning and budgeting. PLOS Glob Public Health. 2023;3(7):e0001063.  https://doi.org/10.1371/journal.pgph.0001063  PMID: 37399174 
  12. Jahn B, Friedrich S, Behnke J, Engel J, Garczarek U, Münnich R, et al. On the role of data, statistics and decisions in a pandemic. AStA Adv Stat Anal. 2022;106(3):349-82.  https://doi.org/10.1007/s10182-022-00439-7  PMID: 35432617 
  13. Grieve R, Yang Y, Abbott S, Babu GR, Bhattacharyya M, Dean N, et al. The importance of investing in data, models, experiments, team science, and public trust to help policymakers prepare for the next pandemic. PLOS Glob Public Health. 2023;3(11):e0002601.  https://doi.org/10.1371/journal.pgph.0002601  PMID: 38032861 
  14. Meehan MT, Rojas DP, Adekunle AI, Adegboye OA, Caldwell JM, Turek E, et al. Modelling insights into the COVID-19 pandemic. Paediatr Respir Rev. 2020;35:64-9. PMID: 32680824 
  15. Steinberg DM, Balicer RD, Benjamini Y, De-Leon H, Gazit D, Rossman H, et al. The role of models in the covid-19 pandemic. Isr J Health Policy Res. 2022;11(1):36.  https://doi.org/10.1186/s13584-022-00546-5  PMID: 36266704 
  16. Eales O, Plank MJ, Cowling BJ, Howden BP, Kucharski AJ, Sullivan SG, et al. Key challenges for respiratory virus surveillance while transitioning out of acute phase of COVID-19 pandemic. Emerg Infect Dis. 2024;30(2):e230768.  https://doi.org/10.3201/eid3002.230768  PMID: 38190760 
  17. Pagel C, Yates CA. Role of mathematical modelling in future pandemic response policy. BMJ. 2022;378:e070615.  https://doi.org/10.1136/bmj-2022-070615  PMID: 36109042 
  18. Holmdahl I, Buckee C. Wrong but useful - what Covid-19 epidemiologic models can and cannot tell us. N Engl J Med. 2020;383(4):303-5.  https://doi.org/10.1056/NEJMp2016822  PMID: 32412711 
  19. Nixon K, Jindal S, Parker F, Reich NG, Ghobadi K, Lee EC, et al. An evaluation of prospective COVID-19 modelling studies in the USA: from data to science translation. Lancet Digit Health. 2022;4(10):e738-47.  https://doi.org/10.1016/S2589-7500(22)00148-0  PMID: 36150782 
  20. Bershteyn A, Kim HY, Braithwaite RS. Real-time infectious disease modeling to inform emergency public health decision making. Annu Rev Public Health. 2022;43(1):397-418.  https://doi.org/10.1146/annurev-publhealth-052220-093319  PMID: 34995131 
  21. Zawadzki RS, Gong CL, Cho SK, Schnitzer JE, Zawadzki NK, Hay JW, et al. Where do we go from here? A framework for using susceptible-infectious-recovered models for policy making in emerging infectious diseases. Value Health. 2021;24(7):917-24.  https://doi.org/10.1016/j.jval.2021.03.005  PMID: 34243834 
  22. Becker AD, Grantz KH, Hegde ST, Bérubé S, Cummings DAT, Wesolowski A. Development and dissemination of infectious disease dynamic transmission models during the COVID-19 pandemic: what can we learn from other pathogens and how can we move forward? Lancet Digit Health. 2021;3(1):e41-50.  https://doi.org/10.1016/S2589-7500(20)30268-5  PMID: 33735068 
  23. McCabe R, Donnelly CA. Disease transmission and control modelling at the science-policy interface. Interface Focus. 2021;11(6):20210013.  https://doi.org/10.1098/rsfs.2021.0013  PMID: 34956589 
  24. Hadley L, Challenor P, Dent C, Isham V, Mollison D, Robertson DA, et al. Challenges on the interaction of models and policy for pandemic control. Epidemics. 2021;37:100499.  https://doi.org/10.1016/j.epidem.2021.100499  PMID: 34534749 
  25. Dangerfield CE, David Abrahams I, Budd C, Butchers M, Cates ME, Champneys AR, et al. , Ini Professional Services. Getting the most out of maths: How to coordinate mathematical modelling research to support a pandemic, lessons learnt from three initiatives that were part of the COVID-19 response in the UK. J Theor Biol. 2023;557:111332.  https://doi.org/10.1016/j.jtbi.2022.111332  PMID: 36323393 
  26. Atkinson P, Gobat N, Lant S, Mableson H, Pilbeam C, Solomon T, et al. Understanding the policy dynamics of COVID-19 in the UK: Early findings from interviews with policy makers and health care professionals. Soc Sci Med. 2020;266:113423.  https://doi.org/10.1016/j.socscimed.2020.113423  PMID: 33069961 
  27. James LP, Salomon JA, Buckee CO, Menzies NA. the use and misuse of mathematical modeling for infectious disease policymaking: lessons for the COVID-19 pandemic. Med Decis Making. 2021;41(4):379-85.  https://doi.org/10.1177/0272989X21990391  PMID: 33535889 
  28. Sherratt K, Carnegie AC, Kucharski A, Cori A, Pearson CAB, Jarvis CI, et al. Improving modelling for epidemic responses: reflections from members of the UK infectious disease modelling community on their experiences during the COVID-19 pandemic. Wellcome Open Res. 2024;9:12.  https://doi.org/10.12688/wellcomeopenres.19601.1  PMID: 38784437 
  29. Bouyer F, Thiongane O, Hobeika A, Arsevska E, Binot A, Corrèges D, et al. Epidemic intelligence in Europe: a user needs perspective to foster innovation in digital health surveillance. BMC Public Health. 2024;24(1):973.  https://doi.org/10.1186/s12889-024-18466-1  PMID: 38582850 
  30. Cori A, Donnelly CA, Dorigatti I, Ferguson NM, Fraser C, Garske T, et al. Key data for outbreak evaluation: building on the Ebola experience. Philos Trans R Soc Lond B Biol Sci. 2017;372(1721):20160371.  https://doi.org/10.1098/rstb.2016.0371  PMID: 28396480 
  31. Polonsky JA, Baidjoe A, Kamvar ZN, Cori A, Durski K, Edmunds WJ, et al. Outbreak analytics: a developing data science for informing the response to emerging pathogens. Philos Trans R Soc Lond B Biol Sci. 2019;374(1776):20180276.  https://doi.org/10.1098/rstb.2018.0276  PMID: 31104603 
  32. Chang S, Pierson E, Koh PW, Gerardin J, Redbird B, Grusky D, et al. Mobility network models of COVID-19 explain inequities and inform reopening. Nature. 2021;589(7840):82-7.  https://doi.org/10.1038/s41586-020-2923-3  PMID: 33171481 
  33. Jarvis CI, Gimma A, van Zandvoort K, Wong KLM, Edmunds WJ. The impact of local and national restrictions in response to COVID-19 on social contacts in England: a longitudinal natural experiment. BMC Med. 2021;19(1):52.  https://doi.org/10.1186/s12916-021-01924-7  PMID: 33602244 
  34. Chadeau-Hyam M, Eales O, Bodinier B, Wang H, Haw D, Whitaker M, et al. Breakthrough SARS-CoV-2 infections in double and triple vaccinated adults and single dose vaccine effectiveness among children in Autumn 2021 in England: REACT-1 study. EClinicalMedicine. 2022;48:101419.  https://doi.org/10.1016/j.eclinm.2022.101419  PMID: 35572721 
  35. Verelst F, Hermans L, Vercruysse S, Gimma A, Coletti P, Backer JA, et al. SOCRATES-CoMix: a platform for timely and open-source contact mixing data during and in between COVID-19 surges and interventions in over 20 European countries. BMC Med. 2021;19(1):254.  https://doi.org/10.1186/s12916-021-02133-y  PMID: 34583683 
  36. Kraemer MUG, Tsui JLH, Chang SY, Lytras S, Khurana MP, Vanderslott S, et al. Artificial intelligence for modelling infectious disease epidemics. Nature. 2025;638(8051):623-35.  https://doi.org/10.1038/s41586-024-08564-w  PMID: 39972226 
  37. Hennessey K, Pezzoli L, Mantel C. A framework for seroepidemiologic investigations in future pandemics: insights from an evaluation of WHO’s Unity Studies initiative. Health Res Policy Syst. 2023;21(1):34.  https://doi.org/10.1186/s12961-023-00973-z  PMID: 37194007 
  38. Oliver N, Lepri B, Sterly H, Lambiotte R, Deletaille S, De Nadai M, et al. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. Sci Adv. 2020;6(23):eabc0764.  https://doi.org/10.1126/sciadv.abc0764  PMID: 32548274 
  39. Ingelbeen B, van Kleef E, Mbala P, Danis K, Macicame I, Hens N, et al. Embedding risk monitoring in infectious disease surveillance for timely and effective outbreak prevention and control. BMJ Glob Health. 2025;10(2):e016870.  https://doi.org/10.1136/bmjgh-2024-016870  PMID: 39961690 
  40. Kucharski AJ, Funk S, Eggo RM. The COVID-19 response illustrates that traditional academic reward structures and metrics do not reflect crucial contributions to modern science. PLoS Biol. 2020;18(10):e3000913.  https://doi.org/10.1371/journal.pbio.3000913  PMID: 33064730 
  41. Hadley L, Rich C, Tasker A, Restif O, Funk S. How does policy modelling work in practice? A global analysis on the use of epidemiological modelling in health crises. PLOS Glob Public Health. 2025;5(6):e0004675.  https://doi.org/10.1371/journal.pgph.0004675  PMID: 40478854 
Modelling practices, data provisioning, sharing and dissemination needs for pandemic decision-making: a European survey-based modellers’ perspective, 2020 to 2022
<p class="citation"> <span>Citation style for this article:</span> <span class="meta-value authors"> <a href="/search?value1=Esther+van+Kleef&option1=author&noRedirect=true" class="nonDisambigAuthorLink">van Kleef Esther</a>, <a href="/search?value1=Wim+Van+Bortel&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Van Bortel Wim</a>, <a href="/search?value1=Elena+Arsevska&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Arsevska Elena</a>, <a href="/search?value1=Luca+Busani&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Busani Luca</a>, <a href="/search?value1=Simon+Dellicour&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Dellicour Simon</a>, <a href="/search?value1=Laura+Di+Domenico&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Di Domenico Laura</a>, <a href="/search?value1=Marius+Gilbert&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Gilbert Marius</a>, <a href="/search?value1=Sabine+L+van+Elsland&option1=author&noRedirect=true" class="nonDisambigAuthorLink">van Elsland Sabine L</a>, <a href="/search?value1=Moritz+UG+Kraemer&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Kraemer Moritz UG</a>, <a href="/search?value1=Shengjie+Lai&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Lai Shengjie</a>, <a href="/search?value1=Philippe+Lemey&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Lemey Philippe</a>, <a href="/search?value1=Stefano+Merler&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Merler Stefano</a>, <a href="/search?value1=Zoran+Milosavljevic&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Milosavljevic Zoran</a>, <a href="/search?value1=Annapaola+Rizzoli&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Rizzoli Annapaola</a>, <a href="/search?value1=Danijela+Simic&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Simic Danijela</a>, <a href="/search?value1=Andrew+J+Tatem&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Tatem Andrew J</a>, <a href="/search?value1=Maguelonne+Teisseire&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Teisseire Maguelonne</a>, <a href="/search?value1=William+Wint&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Wint William</a>, <a href="/search?value1=Vittoria+Colizza&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Colizza Vittoria</a>, <a href="/search?value1=Chiara+Poletto&option1=author&noRedirect=true" class="nonDisambigAuthorLink">Poletto Chiara</a></span>. Modelling practices, data provisioning, sharing and dissemination needs for pandemic decision-making: a European survey-based modellers’ perspective, 2020 to 2022 . <a href="/content/ecdc">Euro Surveill.</a> 2025;30(42):pii=2500216. <a href="https://doi.org/10.2807/1560-7917.ES.2025.30.42.2500216" title="doi link" target="_blank">https://doi.org/10.2807/1560-7917.ES.2025.30.42.2500216</a> <span class="ES_Article_citation"> <span class="generated">Received</span>: 24 Mar 2025;&nbsp;&nbsp; <span class="generated">Accepted</span>: 28 Jul 2025 </span> </p>
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