Estimating the relative probability of direct transmission between infectious disease patients
Estimating the relative probability of direct transmission between infectious disease patients
复制标题
DOI：
10.1093/ije/dyaa031
复制
发表时间：
2020-06-01
影响因子：
7.7
通讯作者：
White, Laura F.
中科院分区：
文献类型：
--
作者：
Leavitt, Sarah, V;Lee, Robyn S.;White, Laura F.
背景：估计传染病参数，如序列间隔(原发病例和继发病例之间的症状间隔时间)和生殖数量(原发病例产生继发病例的平均数量)，对于了解传染病动态非常重要。许多估计方法需要通过直接传播来连接病例，这对大多数疾病来说是一项困难的任务。方法：使用具有详细遗传和/或接触调查数据的病例子集来建立可能传播事件的训练集，建立一个模型，从人口统计学、空间和临床数据估计所有病例对的相对传播概率。我们的方法是基于朴素贝叶斯的机器学习分类算法，它使用训练数据集中的观察频率来估计给定协变量集的一对被链接的概率。结果：在仿真中，我们发现利用案例之间的遗传距离来定义训练传输事件的概率能够高精度地区分真实链接对和未链接对(接收器工作曲线下面积值为95%)。此外，只有一小部分病例，根据样本大小，10%-50%，需要有详细的基因数据，我们的方法才能很好地执行。我们展示了如何使用这些概率来估计平均有效生殖数量，并将我们的方法应用于德国汉堡的一次结核病暴发。结论：当只有一部分病例具有丰富的接触调查和/或遗传数据时，我们的方法是一种新的方法来推断任何数据集中的传播动态。
Background: Estimating infectious disease parameters such as the serial interval (time between symptom onset in primary and secondary cases) and reproductive number (average number of secondary cases produced by a primary case) are important in understanding infectious disease dynamics. Many estimation methods require linking cases by direct transmission, a difficult task for most diseases.Methods: Using a subset of cases with detailed genetic and/or contact investigation data to develop a training set of probable transmission events, we build a model to estimate the relative transmission probability for all case-pairs from demographic, spatial and clinical data. Our method is based on naive Bayes, a machine learning classification algorithm which uses the observed frequencies in the training dataset to estimate the probability that a pair is linked given a set of covariates.Results: In simulations, we find that the probabilities estimated using genetic distance between cases to define training transmission events are able to distinguish between truly linked and unlinked pairs with high accuracy (area under the receiver operating curve value of 95%). Additionally, only a subset of the cases, 10-50% depending on sample size, need to have detailed genetic data for our method to perform well. We show how these probabilities can be used to estimate the average effective reproductive number and apply our method to a tuberculosis outbreak in Hamburg, Germany.Conclusions: Our method is a novel way to infer transmission dynamics in any dataset when only a subset of cases has rich contact investigation and/or genetic data.