PDF(Pubmed)
Abstract：
Buruli ulcer (BU) is a neglected tropical disease caused by infection of subcutaneous tissue with Mycobacterium ulcerans. BU is commonly reported across rural regions of Central and West Africa but has been increasing dramatically in temperate southeast Australia around the major metropolitan city of Melbourne, with most disease transmission occurring in the summer months. Previous research has shown that Australian native possums are reservoirs of M. ulcerans and that they shed the bacteria in their fecal material (excreta). Field surveys show that locales where possums harbor M. ulcerans overlap with human cases of BU, raising the possibility of using possum excreta surveys to predict the risk of disease occurrence in humans.
We thus established a highly structured 12 month possum excreta surveillance program across an area of 350 km2 in the Mornington Peninsula area 70 km south of Melbourne, Australia. The primary objective of our study was to assess using statistical modeling if M. ulcerans surveillance of possum excreta provided useful information for predicting future human BU case locations.
Over two sampling campaigns in summer and winter, we collected 2,282 possum excreta specimens of which 11% were PCR positive for M. ulcerans-specific DNA. Using the spatial scanning statistical tool SaTScan, we observed non-random, co-correlated clustering of both M. ulcerans positive possum excreta and human BU cases. We next trained a statistical model with the Mornington Peninsula excreta survey data to predict the future likelihood of human BU cases occurring in the region. By observing where human BU cases subsequently occurred, we show that the excreta model performance was superior to a null model trained using the previous year\'s human BU case incidence data (AUC 0.66 vs 0.55). We then used data unseen by the excreta-informed model from a new survey of 661 possum excreta specimens in Geelong, a geographically separate BU endemic area to the southwest of Melbourne, to prospectively predict the location of human BU cases in that region. As for the Mornington Peninsula, the excreta-based BU prediction model outperformed the null model (AUC 0.75 vs 0.50) and pinpointed specific locations in Geelong where interventions could be deployed to interrupt disease spread.
This study highlights the One Health nature of BU by confirming a quantitative relationship between possum excreta shedding of M. ulcerans and humans developing BU. The excreta survey-informed modeling we have described will be a powerful tool for the efficient targeting of public health responses to stop BU.
This research was supported by the National Health and Medical Research Council of Australia and the Victorian Government Department of Health (GNT1152807 and GNT1196396).
We thus established a highly structured 12 month possum excreta surveillance program across an area of 350 km2 in the Mornington Peninsula area 70 km south of Melbourne, Australia. The primary objective of our study was to assess using statistical modeling if M. ulcerans surveillance of possum excreta provided useful information for predicting future human BU case locations.
Over two sampling campaigns in summer and winter, we collected 2,282 possum excreta specimens of which 11% were PCR positive for M. ulcerans-specific DNA. Using the spatial scanning statistical tool SaTScan, we observed non-random, co-correlated clustering of both M. ulcerans positive possum excreta and human BU cases. We next trained a statistical model with the Mornington Peninsula excreta survey data to predict the future likelihood of human BU cases occurring in the region. By observing where human BU cases subsequently occurred, we show that the excreta model performance was superior to a null model trained using the previous year\'s human BU case incidence data (AUC 0.66 vs 0.55). We then used data unseen by the excreta-informed model from a new survey of 661 possum excreta specimens in Geelong, a geographically separate BU endemic area to the southwest of Melbourne, to prospectively predict the location of human BU cases in that region. As for the Mornington Peninsula, the excreta-based BU prediction model outperformed the null model (AUC 0.75 vs 0.50) and pinpointed specific locations in Geelong where interventions could be deployed to interrupt disease spread.
This study highlights the One Health nature of BU by confirming a quantitative relationship between possum excreta shedding of M. ulcerans and humans developing BU. The excreta survey-informed modeling we have described will be a powerful tool for the efficient targeting of public health responses to stop BU.
This research was supported by the National Health and Medical Research Council of Australia and the Victorian Government Department of Health (GNT1152807 and GNT1196396).
摘要：
■布鲁里溃疡（BU）是一种被忽视的热带病，由溃疡分枝杆菌感染皮下组织引起。BU通常在中非和西非的农村地区报道，但在澳大利亚东南部主要大都市墨尔本周围的温带地区却急剧增加，大多数疾病传播发生在夏季。先前的研究表明，澳大利亚本土负鼠是溃疡分枝杆菌的储库，它们在粪便（排泄物）中排出细菌。实地调查显示，负鼠携带溃疡分枝杆菌的地区与BU的人类病例重叠，提高了使用负鼠排泄物调查来预测人类疾病发生风险的可能性。
■因此，我们在墨尔本以南70公里的莫宁顿半岛地区建立了高度结构化的12个月负鼠排泄物监测计划，澳大利亚。我们研究的主要目的是使用统计模型评估负鼠排泄物的溃疡监测是否为预测未来人类BU病例位置提供了有用的信息。
■在夏季和冬季的两次采样活动中，我们收集了2,282份负鼠排泄物标本，其中11％的溃疡分枝杆菌特异性DNAPCR阳性。使用空间扫描统计工具SaTScan,我们观察到非随机的，溃疡分枝杆菌阳性负鼠排泄物和人类BU病例的共相关聚类。接下来，我们使用莫宁顿半岛排泄物调查数据训练了一个统计模型，以预测该地区未来发生人类BU病例的可能性。通过观察人类BU病例随后发生的地方，我们显示,排泄物模型性能优于使用前一年的人类BU病例发生率数据(AUC0.66vs0.55)训练的空模型.然后，我们使用了对吉朗的661个负鼠排泄物标本进行的新调查中的排泄物信息模型所看不到的数据，墨尔本西南部的一个地理上分开的BU特有地区，前瞻性地预测该地区人类BU病例的位置。至于莫宁顿半岛,基于排泄物的BU预测模型的性能优于空模型(AUC0.75vs0.50),并确定了吉朗的特定位置,在这些位置可以部署干预措施以阻断疾病传播.
■这项研究通过确认溃疡分枝杆菌的负鼠排泄物脱落与人类发展BU之间的定量关系，强调了BU的“一种健康”性质。我们描述的排泄物调查知情模型将是有效定位公共卫生对策以阻止BU的强大工具。
■这项研究得到了澳大利亚国家卫生与医学研究委员会和维多利亚州政府卫生部的支持（GNT1152807和GNT1196396）。
■因此，我们在墨尔本以南70公里的莫宁顿半岛地区建立了高度结构化的12个月负鼠排泄物监测计划，澳大利亚。我们研究的主要目的是使用统计模型评估负鼠排泄物的溃疡监测是否为预测未来人类BU病例位置提供了有用的信息。
■在夏季和冬季的两次采样活动中，我们收集了2,282份负鼠排泄物标本，其中11％的溃疡分枝杆菌特异性DNAPCR阳性。使用空间扫描统计工具SaTScan,我们观察到非随机的，溃疡分枝杆菌阳性负鼠排泄物和人类BU病例的共相关聚类。接下来，我们使用莫宁顿半岛排泄物调查数据训练了一个统计模型，以预测该地区未来发生人类BU病例的可能性。通过观察人类BU病例随后发生的地方，我们显示,排泄物模型性能优于使用前一年的人类BU病例发生率数据(AUC0.66vs0.55)训练的空模型.然后，我们使用了对吉朗的661个负鼠排泄物标本进行的新调查中的排泄物信息模型所看不到的数据，墨尔本西南部的一个地理上分开的BU特有地区，前瞻性地预测该地区人类BU病例的位置。至于莫宁顿半岛,基于排泄物的BU预测模型的性能优于空模型(AUC0.75vs0.50),并确定了吉朗的特定位置,在这些位置可以部署干预措施以阻断疾病传播.
■这项研究通过确认溃疡分枝杆菌的负鼠排泄物脱落与人类发展BU之间的定量关系，强调了BU的“一种健康”性质。我们描述的排泄物调查知情模型将是有效定位公共卫生对策以阻止BU的强大工具。
■这项研究得到了澳大利亚国家卫生与医学研究委员会和维多利亚州政府卫生部的支持（GNT1152807和GNT1196396）。
