Limits to forecasting precision for outbreaks of directly transmitted diseases
Limits to forecasting precision for outbreaks of directly transmitted diseases
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DOI：
10.1371/journal.pmed.0030003
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发表时间：
2006-01-01
期刊：
影响因子：
15.8
通讯作者：
Drake, JM
中科院分区：
文献类型：
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作者：
Drake, JM
背景传染病暴发预警系统是流行病生态理论的重要应用。早期预警系统预测的一个关键变量是最终的疫情规模。然而，对于直接传播的疾病，疫情发展的随机接触过程对预测最终规模的精度造成了根本性的限制。方法和结果我研究了在简单随机流行病中，预期的最终疫情规模和疫情最终规模的变异系数如何与控制有效性和传染性接触率相关。作为示例，我使用九种直接传播疾病的基本繁殖率 (R-0) 的观察范围数据对该模型进行了参数化。我还提出了一种新模型的结果，即具有延迟发作干预的简单随机流行病，其中最初的超临界疫情（R-0 > 1）在延迟后得到控制。结论 对于任何清除率小于传染性接触率约 2.41 倍的疫情，亚临界病例中最终疫情规模的变异系数（R-0 < 1）将大于 1，这意味着对于许多传染性疾病，最终爆发规模的精确预测将是遥不可及的。在延迟发病模型中，变异系数（CV）一般较大（CV > 1），并且随着疫情开始和干预之间的延迟以及平均疫情规模的增加而增加。这些结果表明，传染病早期预警系统不应仅仅专注于预测疫情爆发规模，而应考虑疫情爆发的其他特征，例如疾病出现的时间。
Background Early warning systems for outbreaks of infectious diseases are an important application of the ecological theory of epidemics. A key variable predicted by early warning systems is the final outbreak size. However, for directly transmitted diseases, the stochastic contact process by which outbreaks develop entails fundamental limits to the precision with which the final size can be predicted.Methods and Findings I studied how the expected final outbreak size and the coefficient of variation in the final size of outbreaks scale with control effectiveness and the rate of infectious contacts in the simple stochastic epidemic. As examples, I parameterized this model with data on observed ranges for the basic reproductive ratio (R-0) of nine directly transmitted diseases. I also present results from a new model, the simple stochastic epidemic with delayed-onset intervention, in which an initially supercritical outbreak (R-0 > 1) is brought under control after a delay.Conclusion The coefficient of variation of final outbreak size in the subcritical case (R-0 < 1) will be greater than one for any outbreak in which the removal rate is less than approximately 2.41 times the rate of infectious contacts, implying that for many transmissible diseases precise forecasts of the final outbreak size will be unattainable. In the delayed-onset model, the coefficient of variation (CV) was generally large (CV > 1) and increased with the delay between the start of the epidemic and intervention, and with the average outbreak size. These results suggest that early warning systems for infectious diseases should not focus exclusively on predicting outbreak size but should consider other characteristics of outbreaks such as the timing of disease emergence.