For most pathogens, testing procedures can be used to distinguish between different strains with which individuals are infected. Due to the growing availability of such data, multistrain models have increased in popularity over the past few years. Quantifying the interactions between different strains of a pathogen is crucial in order to obtain a more complete understanding of the transmission process, but statistical methods for this type of problem are still in the early stages of development. Motivated by this demand, we construct a stochastic epidemic model that incorporates additional strain information and propose a statistical algorithm for efficient inference. The model improves upon existing methods in the sense that it allows for both imperfect diagnostic test sensitivities and strain misclassification. Extensive simulation studies were conducted in order to assess the performance of our method, while the utility of the developed methodology is demonstrated on data obtained from a longitudinal study of Escherichia coli O157:H7 strains in feedlot cattle.

中文翻译：

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贝叶斯推断多株流行病及其在肥育场牛ESCHERICHIA COLI O157：H7中的应用

对于大多数病原体，可以使用测试程序来区分感染个体的不同菌株。由于此类数据的可用性不断提高，在过去的几年中，多应变模型越来越受欢迎。为了更全面地了解传播过程，量化病原体不同菌株之间的相互作用至关重要，但是针对此类问题的统计方法仍处于开发的早期阶段。受此需求的驱使，我们构建了一个随机流行病模型，其中包含了更多的应变信息，并提出了一种用于有效推理的统计算法。该模型在允许不完善的诊断测试敏感性和菌株错误分类的意义上改进了现有方法。育肥牛中的大肠杆菌O157：H7菌株。