Yitzhak Moda’i Chair in Technology and Economics, Technion--Israel Institute of Technology, Haifa 32000, Israel.
BMC Public Health. 2012 Dec 19;12:1091. doi: 10.1186/1471-2458-12-1091.
Formulation and evaluation of public health policy commonly employs science-based mathematical models. For instance, epidemiological dynamics of TB is dominated, in general, by flow between actively and latently infected populations. Thus modelling is central in planning public health intervention. However, models are highly uncertain because they are based on observations that are geographically and temporally distinct from the population to which they are applied.
We aim to demonstrate the advantages of info-gap theory, a non-probabilistic approach to severe uncertainty when worst cases cannot be reliably identified and probability distributions are unreliable or unavailable. Info-gap is applied here to mathematical modelling of epidemics and analysis of public health decision-making.
Applying info-gap robustness analysis to tuberculosis/HIV (TB/HIV) epidemics, we illustrate the critical role of incorporating uncertainty in formulating recommendations for interventions. Robustness is assessed as the magnitude of uncertainty that can be tolerated by a given intervention. We illustrate the methodology by exploring interventions that alter the rates of diagnosis, cure, relapse and HIV infection.
We demonstrate several policy implications. Equivalence among alternative rates of diagnosis and relapse are identified. The impact of initial TB and HIV prevalence on the robustness to uncertainty is quantified. In some configurations, increased aggressiveness of intervention improves the predicted outcome but also reduces the robustness to uncertainty. Similarly, predicted outcomes may be better at larger target times, but may also be more vulnerable to model error.
The info-gap framework is useful for managing model uncertainty and is attractive when uncertainties on model parameters are extreme. When a public health model underlies guidelines, info-gap decision theory provides valuable insight into the confidence of achieving agreed-upon goals.
制定和评估公共卫生政策通常采用基于科学的数学模型。例如,结核病的流行病学动态通常由活跃感染和潜伏感染人群之间的流动所主导。因此,建模是规划公共卫生干预的核心。然而,模型具有高度不确定性,因为它们是基于与应用人群在地理和时间上都不同的观察结果建立的。
我们旨在展示信息差距理论的优势,这是一种在最坏情况无法可靠识别且概率分布不可靠或不可用时处理严重不确定性的非概率方法。信息差距理论应用于流行病学模型和公共卫生决策分析。
通过将信息差距稳健性分析应用于结核病/艾滋病(TB/HIV)流行,我们说明了在制定干预措施建议时纳入不确定性的重要性。稳健性评估为给定干预措施可以容忍的不确定性程度。我们通过探索改变诊断、治愈、复发和 HIV 感染率的干预措施来演示该方法。
我们展示了几个政策影响。确定了替代诊断和复发率之间的等效性。还量化了初始结核病和 HIV 流行率对不确定性稳健性的影响。在某些配置中,干预措施的积极性提高会改善预测结果,但也会降低对不确定性的稳健性。同样,预测结果在较大的目标时间可能会更好,但也可能更容易受到模型错误的影响。
信息差距框架可用于管理模型不确定性,在模型参数不确定性极端时具有吸引力。当公共卫生模型是指南的基础时,信息差距决策理论为实现既定目标的信心提供了有价值的见解。