Remodelling selection to optimise disease forecasts and policies

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Gomes, M Gabriela M and Blagborough, Andrew M and Langwig, Kate E and Ringwald, Beate (2024) Remodelling selection to optimise disease forecasts and policies. Journal of Physics A: Mathematical and Theoretical, 57 (10). 103001. ISSN 0305-4470 (https://doi.org/10.1088/1751-8121/ad280d)

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Abstract

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.

ORCID iDs

Gomes, M Gabriela M ORCID logoORCID: https://orcid.org/0000-0002-1454-4979, Blagborough, Andrew M, Langwig, Kate E and Ringwald, Beate;
CORE (COnnecting REpositories)