论文标题
展开选择以推断个人风险异质性以优化疾病预测和政策制定
Unfolding selection to infer individual risk heterogeneity for optimising disease forecasts and policy development
论文作者
论文摘要
用于设定预防疾病和控制目标的数学模型正在增加。但是，随着模型信息的政策的实施，某些预测的不准确性变得显而易见，例如，预测感染负担的过度预测和对干预影响的高估。在这里，我们将这些差异归因于方法学局限性，以捕获现实世界系统的异质性。为感染及其相互作用的单一因素的机制决定了获得疾病的个人倾向。这些可能是如此之多，以至于获得完整的机械描述可能是不可行的。为了建立卫生政策的制定，模型开发人员要么将因素遗漏（还原主义），要么采用更广泛但粗糙的描述（整体）。我们认为，预测能力需要对异质性的整体描述，这些描述目前在传染病流行病学中不足，但在其他学科中很常见。
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.
