Appropriate models for the management of infectious diseases.
Appropriate models for the management of infectious diseases.
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DOI：
10.1371/journal.pmed.0020174
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发表时间：
2005-07
期刊：
影响因子：
15.8
通讯作者：
Keeling MJ
中科院分区：
文献类型：
--
作者：
Wearing HJ;Rohani P;Keeling MJ
数学模型已成为流行病学家的宝贵管理工具，既阐明了观察到的动态机制，又对不同控制措施的有效性进行定量预测。在这里，我们解释了这两个重要模型核心的两个重要但很大程度上被忽略的假设引入了实质性偏见。
首先，我们使用分析方法表明（i）忽略潜在时期或（ii）使指数分布的潜在和感染性周期（包括潜在时期）的共同假设始终导致低估感染的基本生殖比率爆发数据。然后，我们通过将流行病模型与流感爆发的数据拟合在一起来说明这些要点。最后，我们记录了关于模型结构的这种不现实的先验假设是如何对潜在管理选项结果的系统性过度预测的。
这项工作旨在强调，在开发用于公共卫生使用的模型时，我们需要仔细注意经典框架中嵌入的内在假设。
传染病模型中当前的两个偏见可能会大大影响它们在预测疾病破裂的实用性。
Mathematical models have become invaluable management tools for epidemiologists, both shedding light on the mechanisms underlying observed dynamics as well as making quantitative predictions on the effectiveness of different control measures. Here, we explain how substantial biases are introduced by two important, yet largely ignored, assumptions at the core of the vast majority of such models. First, we use analytical methods to show that (i) ignoring the latent period or (ii) making the common assumption of exponentially distributed latent and infectious periods (when including the latent period) always results in underestimating the basic reproductive ratio of an infection from outbreak data. We then proceed to illustrate these points by fitting epidemic models to data from an influenza outbreak. Finally, we document how such unrealistic a priori assumptions concerning model structure give rise to systematically overoptimistic predictions on the outcome of potential management options. This work aims to highlight that, when developing models for public health use, we need to pay careful attention to the intrinsic assumptions embedded within classical frameworks. Two current biases in infectious disease models may substantially affect their usefulness in predicting disease oubreaks.
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