Remodelling selection to optimise disease forecasts and policies

M Gabriela M Gomes, Andrew M Blagborough, Kate E Langwig, Beate Ringwald

Research output: Contribution to journalArticlepeer-review

14 Downloads (Pure)


Mathematical models are increasingly adopted for setting disease prevention and control targets. As model-informed policies are implemented, however, the inaccuracies of some forecasts become apparent, for example overprediction of infection burdens and intervention impacts. Here, we attribute these discrepancies to methodological limitations in capturing the heterogeneities of real-world systems. The mechanisms underpinning risk factors of infection and their interactions determine individual propensities to acquire disease. These factors are potentially so numerous and complex that to attain a full mechanistic description is likely unfeasible. To contribute constructively to the development of health policies, model developers either leave factors out (reductionism) or adopt a broader but coarse description (holism). In our view, predictive capacity requires holistic descriptions of heterogeneity which are currently underutilised in infectious disease epidemiology, in comparison to other population disciplines, such as non-communicable disease epidemiology, demography, ecology and evolution.
Original languageEnglish
Article number103001
Number of pages15
JournalJournal of Physics A: Mathematical and Theoretical
Issue number10
Early online date9 Feb 2024
Publication statusPublished - 8 Mar 2024


  • reductionism
  • holism
  • infection
  • disease prevention


Dive into the research topics of 'Remodelling selection to optimise disease forecasts and policies'. Together they form a unique fingerprint.

Cite this