Pockets of susceptibility resulting from spatial or social heterogeneity in vaccine coverage can drive measles outbreaks, as cases imported into such pockets are likely to cause further transmission and lead to large transmission clusters. Characterizing the dynamics of transmission is essential for identifying which individuals and regions might be most at risk. As data from detailed contact-tracing investigations are not available in many settings, we developed an R package called o2geosocial to reconstruct the transmission clusters and the importation status of the cases from their age, location, genotype and onset date. We compared our inferred cluster size distributions to 737 transmission clusters identified through detailed contact-tracing in the USA between 2001 and 2016. We were able to reconstruct the importation status of the cases and found good agreement between the inferred and reference clusters. The results were improved when the contact-tracing investigations were used to set the importation status before running the model. Spatial heterogeneity in vaccine coverage is difficult to measure directly. Our approach was able to highlight areas with potential for local transmission using a minimal number of variables and could be applied to assess the intensity of ongoing transmission in a region.

中文翻译：

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根据常规收集的监测数据对麻疹传播群进行概率重建

疫苗覆盖范围的空间或社会异质性导致的易感性小范围可能会推动麻疹暴发，因为输入到这些小范围的病例可能会导致进一步传播并导致大规模传播集群。表征传播动态对于确定哪些个人和地区可能面临的风险最大至关重要。由于在许多情况下无法获得详细的接触者追踪调查数据，我们开发了一个名为 o2geosocial 的 R 包，以根据病例的年龄、位置、基因型和发病日期重建传播群和病例的输入状态。我们将推断的集群大小分布与 2001 年至 2016 年间在美国通过详细的接触者追踪确定的 737 个传播集群进行了比较。我们能够重建病例的输入状态，并在推断的和参考的集群之间找到了很好的一致性。当在运行模型之前使用联系人追踪调查设置导入状态时，结果得到了改进。疫苗覆盖率的空间异质性难以直接衡量。我们的方法能够使用最少的变量来突出具有本地传播潜力的区域，并且可以应用于评估一个地区正在进行的传播强度。