Estimating the Probability of a Major Outbreak from the Timing of Early Cases: An Indeterminate Problem?
Estimating the Probability of a Major Outbreak from the Timing of Early Cases: An Indeterminate Problem?
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DOI：
10.1371/journal.pone.0057878
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发表时间：
2013-03-06
期刊：
影响因子：
3.7
通讯作者：
Haydon, Daniel T.
中科院分区：
文献类型：
--
作者：
Craft, Meggan E.;Beyer, Hawthorne L.;Haydon, Daniel T.
保护生物学家，以及兽医和公共卫生官员，将大大受益于能够预测传染病的爆发是否会是重大的。如果基本再生数（R-0）的值在1和2之间，传染病暴发有合理的机会在早期阶段逐渐消失，或者在没有干预的情况下在人口中广泛传播。如果能够预测何时可能发生渐弱，就可以尽量减少对昂贵的预防性控制战略的需要。然而，即使是简单的流行病过程的可预测性在很大程度上仍然没有得到探索。在这里，我们进行了一个致命的疾病爆发的早期阶段的模拟数据的检查，并探讨如何观察到的信息可能是有用的预测重大疫情。具体来说，知道最初几个病例的死亡时间是否能让我们预测疫情是否会严重？使用两种方法，轨迹匹配和判别函数分析，我们发现，即使在我们最好的情况下（流行病学参数的准确知识，和精确的死亡时间），它是不可能可靠地预测结果的随机易感-暴露-中毒-复发（SEIR）过程。
Conservation biologists, as well as veterinary and public health officials, would benefit greatly from being able to forecast whether outbreaks of infectious disease will be major. For values of the basic reproductive number (R-0) between one and two, infectious disease outbreaks have a reasonable chance of either fading out at an early stage or, in the absence of intervention, spreading widely within the population. If it were possible to predict when fadeout was likely to occur, the need for costly precautionary control strategies could be minimized. However, the predictability of even simple epidemic processes remains largely unexplored. Here we conduct an examination of simulated data from the early stages of a fatal disease outbreak and explore how observable information might be useful for predicting major outbreaks. Specifically, would knowing the time of deaths for the first few cases allow us to predict whether an outbreak will be major? Using two approaches, trajectory matching and discriminant function analysis, we find that even in our best-case scenario (with accurate knowledge of epidemiological parameters, and precise times of death), it was not possible to reliably predict the outcome of a stochastic Susceptible-Exposed-Infectious-Recovered (SEIR) process.