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Modelling the pandemic: attuning models to their contexts
  1. Tim Rhodes1,2,
  2. Kari Lancaster2,
  3. Shelley Lees3,
  4. Melissa Parker3
  1. 1 Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, UK
  2. 2 Faculty of Arts and Social Sciences, University of New South Wales, Sydney, New South Wales, Australia
  3. 3 Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, UK
  1. Correspondence to Professor Tim Rhodes; tim.rhodes{at}


The evidence produced in mathematical models plays a key role in shaping policy decisions in pandemics. A key question is therefore how well pandemic models relate to their implementation contexts. Drawing on the cases of Ebola and influenza, we map how sociological and anthropological research contributes in the modelling of pandemics to consider lessons for COVID-19. We show how models detach from their implementation contexts through their connections with global narratives of pandemic response, and how sociological and anthropological research can help to locate models differently. This potentiates multiple models of pandemic response attuned to their emerging situations in an iterative and adaptive science. We propose a more open approach to the modelling of pandemics which envisages the model as an intervention of deliberation in situations of evolving uncertainty. This challenges the ‘business-as-usual’ of evidence-based approaches in global health by accentuating all science, within and beyond pandemics, as ‘emergent’ and ‘adaptive’.

  • public health

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  • Handling editor Seye Abimbola

  • Twitter @tim__rhodes

  • Contributors TR led the writing for this analysis with all coauthors contributing throughout.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement There are no data available for sharing linked to this analysis which is based on literature in the public domain.