World Health Organization Nigeria, Abuja, Federal Capital Territory (FCT), Nigeria.
Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom.
J Infect Dis. 2022 Apr 19;225(8):1377-1386. doi: 10.1093/infdis/jiaa175.
Environmental surveillance (ES) for poliovirus is increasingly important for polio eradication, often detecting circulating virus before paralytic cases are reported. The sensitivity of ES depends on appropriate selection of sampling sites, which is difficult in low-income countries with informal sewage networks.
We measured ES site and sample characteristics in Nigeria during June 2018-May 2019, including sewage physicochemical properties, using a water-quality probe, flow volume, catchment population, and local facilities such as hospitals, schools, and transit hubs. We used mixed-effects logistic regression and machine learning (random forests) to investigate their association with enterovirus isolation (poliovirus and nonpolio enteroviruses) as an indicator of surveillance sensitivity.
Four quarterly visits were made to 78 ES sites in 21 states of Nigeria, and ES site characteristic data were matched to 1345 samples with an average enterovirus prevalence among sites of 68% (range, 9%-100%). A larger estimated catchment population, high total dissolved solids, and higher pH were associated with enterovirus detection. A random forests model predicted "good" sites (enterovirus prevalence >70%) from measured site characteristics with out-of-sample sensitivity and specificity of 75%.
Simple measurement of sewage properties and catchment population estimation could improve ES site selection and increase surveillance sensitivity.
环境监测（ES）对于脊髓灰质炎病毒越来越重要，通常在报告麻痹病例之前就能检测到循环病毒。ES 的灵敏度取决于采样点的适当选择，而在污水管网不规范的低收入国家，这一点很难做到。
我们在 2018 年 6 月至 2019 年 5 月期间在尼日利亚测量了 ES 地点和样本特征，包括使用水质探头测量污水的理化性质、流量、集水区人口以及医院、学校和交通枢纽等当地设施。我们使用混合效应逻辑回归和机器学习（随机森林）来研究它们与肠道病毒分离（脊髓灰质炎病毒和非脊髓灰质炎肠道病毒）的关联，作为监测灵敏度的指标。
在尼日利亚的 21 个州的 78 个 ES 地点进行了四次季度访问，并将 ES 地点特征数据与 1345 个样本进行了匹配，平均每个地点的肠道病毒检出率为 68%（范围为 9%-100%）。估计的集水区人口较多、总溶解固体较高和 pH 值较高与肠道病毒检测有关。随机森林模型从测量的地点特征预测“良好”的地点（肠道病毒检出率>70%），其样本外灵敏度和特异性为 75%。
简单测量污水性质和集水区人口估计可以改进 ES 地点选择并提高监测灵敏度。
