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Gomes, Gabriel, Blagborough, Andrew, Langwig, Kate and Ringwald, Beate (2024) 'Remodelling selection to optimise disease forecasts and policies'. Journal of Physics A: Mathematical and Theoretical, Vol 57, Issue 10, p. 103001.
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RemodellingSelection.pdf - Accepted Version Available under License Creative Commons Attribution. Download (1MB) | Preview |
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.
| Item Type: | Article |
|---|---|
| Subjects: | W General Medicine. Health Professions > W 26.5 Informatics. Health informatics |
| Faculty: Department: | Clinical Sciences & International Health > Clinical Sciences Department |
| Digital Object Identifer (DOI): | https://doi.org/10.1088/1751-8121/ad280d |
| Depositing User: | Christy Littlejohn |
| Date Deposited: | 15 Feb 2024 11:05 |
| Last Modified: | 04 Mar 2024 14:57 |
| URI: | https://archive.lstmed.ac.uk/id/eprint/24025 |
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