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Remodelling selection to optimise disease forecasts and policies
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Resumo(s)
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.
Descrição
Funding Information: This paper benefited from supportive discussions with numerous colleagues, especially Mauricio Barreto, Maxine Caws, Andrea Doeschl-Wilson, Nicholas Feasey, Marcelo Ferreira, Philippe Glaziou, Stephen Gordon, Jessica King, James LaCourse, Christian Lienhardt, Paul McKeigue, Penelope Phillips-Howard, Lisa Reimer, Meta Roestenberg, Jamie Rylance, Bertel Squire, Russell Stothard, Miriam Taegtmeyer, Dianne Terlouw, Rachel Tolhurst, Tom Wingfield. This work is funded by national funds through the FCT – Fundação para a Ciência e a Tecnologia, I.P., under the scope of the projects UIDB/00297/2020 ( https://doi.org/10.54499/UIDB/00297/2020 ) and UIDP/00297/2020 ( https://doi.org/10.54499/UIDP/00297/2020 ) (Center for Mathematics and Applications) MGMG has received additional funding from the Innovative Medicines Initiative 2 Joint Undertaking under Grant Agreement No 101007799 (Inno4Vac). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. This communication reflects the author’s view and that neither IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained therein. Publisher Copyright: © 2024 The Author(s). Published by IOP Publishing Ltd.
Palavras-chave
epidemiology heterogeneity individual variation infectious disease dynamics remodelling selection Statistical and Nonlinear Physics Statistics and Probability Modelling and Simulation Mathematical Physics General Physics and Astronomy SDG 3 - Good Health and Well-being