Unfolding selection to infer individual risk heterogeneity for optimising disease forecasts and policy development

TL;DR

This paper emphasizes the importance of detailed heterogeneity modeling in infectious disease forecasts to improve policy accuracy, highlighting the limitations of current reductionist and holistic approaches.

Contribution

It advocates for unfolding selection mechanisms to better capture individual risk heterogeneity, enhancing disease prediction and policy development.

Findings

Current models often overpredict infection burdens.

Holistic heterogeneity descriptions are underutilized in epidemiology.

Unfolding selection improves model predictive capacity.

Abstract

Mathematical models are increasing adopted for setting targets for disease prevention and control. As model-informed policies are implemented, however, the inaccuracies of some forecasts become apparent, for example overprediction of infection burdens and overestimation of intervention impacts. Here, we attribute these discrepancies to methodological limitations in capturing the heterogeneities of real-world systems. The mechanisms underpinning single factors for infection and their interactions determine individual propensities to acquire disease. These are potentially so numerous that to attain a full mechanistic description may be 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 but common in other disciplines.