Seroepidemiologic methods to identify hotspots of trachoma and predict future infection

项目摘要/摘要 背景：沙眼是由眼部沙眼衣原体感染引起的，是 传染性失明在全世界范围内，并已作为一个公共卫生问题被作为全球消除的目标 2020年。这一目标将在许多国家实现，但埃塞俄比亚的一些地区仍居高不下 尽管进行了10年的密集控制活动，但感染水平仍然很高。一小部分人口可能 沙眼感染者居多，疫源地(“热点”)在村级或以下； 运营挑战是利用现有数据准确预测他们所处的位置。机器学习的研究进展 和空间数据科学已经证明在预测的空间分辨率方面有了显著的提高 像疟疾这样的疾病。在沙眼的现有生物标志物中，儿童的免疫球蛋白抗体反应可以 能够实现更准确的预测，因为它们结合了随着时间的推移而暴露的情况，并反映了最近的传播。 目的：这项研究的主要目的是评估抗体测量是否可以确定稳定性 沙眼感染的热点，以及一种新的机器学习方法是否可以准确地预测村庄- 及时将沙眼感染水平提前(最长3年)。我们假设感染将集中在 人口和感染热点将在村庄一级。我们进一步假设抗体 对幼儿的测量将提供关于沙眼传播的稳定信息来源， 将使我们能够准确地预测未来沙眼衣原体感染水平较高的村庄。 方法：为了检验我们的假设，我们将从一个特征良好的人群中进行测量。 在埃塞俄比亚阿姆哈拉地区，40个村庄参加了由美国国立卫生研究院资助的整群随机试验(U10-EY023939)。 这项为期三年的试验旨在衡量改善水、卫生和洗手(洗手)的效果。 未用阿奇霉素治疗的沙眼感染。这项试验收集了临床和生物标记物 对大约2400名0-9岁的儿童在入学时和每年访问3岁以上的儿童进行测量 好几年了。我们将使用！-统计量来表征传播的空间尺度，它等于相对风险 在病例的不同距离内的感染。我们将使用基于排列的空间扫描统计数据来 每年使用免疫球蛋白抗体和聚合酶链式反应来确定热点，并将确定它们是否稳定。 时间到了。使用地理空间集成机器学习，我们将预测沙眼血清阳性率作为以下因素的函数 遥感、地理空间信息和有限的招生特征。我们将按以下顺序对村庄进行排序 预测血清阳性，并将评估沙眼衣原体感染的比例在排名靠前的 1、2和3年后的村庄。我们将使用预测的临床症状作为比较器来重复分析。 对未来沙眼衣原体感染做出准确、精细预测的方法的发展将奠定基础。 为未来的适应性随机试验奠定基础，该试验优先将更密集的干预分配给 注册时预测的村庄未来的感染水平会很高。

Project Summary / Abstract Background: Trachoma, caused by ocular infection with Chlamydia trachomatis, is the leading cause of infectious blindness worldwide and has been targeted for global elimination as a public health problem by 2020. This goal will be achieved in many countries, but some regions in Ethiopia maintain persistently high levels of infection despite >10 years of intensive control activities. A small proportion of the population likely harbors the majority of trachoma infections, with foci of infection (“hotspots”) at or below the village scale; the operational challenge is accurately predicting where they are with existing data. Advances in machine learning and spatial data science have demonstrated marked improvements in the spatial resolution of predictions for diseases like malaria. Among available biomarkers of trachoma, IgG antibody responses in children could enable more accurate predictions because they integrate exposure over time and reflect recent transmission. Aims: The principal aims of this study are to evaluate whether antibody measurements can identify stable hotspots of trachoma infection, and whether a novel machine learning approach can accurately predict village- level trachoma infection forward in time (up to 3 years). We hypothesize that infection will be concentrated in the population and that hotspots of infection will be at the village level. We further hypothesize that antibody measurements in young children will provide a stable source of information about trachoma transmission that will enable us to accurately predict villages with high levels of future C. trachomatis infection. Methods: To test our hypotheses, we will draw on measurements from a well characterized population across 40 villages enrolled in a NIH-funded cluster randomized trial in Ethiopia’s Amhara Region (U10-EY023939). The three-year trial is designed to measure the effect of improved water, sanitation, and handwashing (WASH) on trachoma infection in the absence of azithromycin treatment. The trial has collected clinical and biomarker measurements from approximately 2,400 children ages 0-9 years at enrollment and in annual visits over 3 years. We will characterize the spatial scale of transmission using the !-statistic, which equals the relative risk of infection within different distances of cases. We will use a permutation-based, spatial scan statistic to identify hotspots using IgG antibody and PCR measures in each year, and will determine if they are stable over time. Using geospatial ensemble machine learning, we will predict trachoma seroprevalence as a function of remotely sensed, geospatial information and limited enrollment characteristics. We will rank order villages by predicted seroprevalence, and will assess the proportion of PCR C. trachomatis infections in top-ranked villages 1, 2, and 3 years later. We will repeat the analysis using predicted clinical symptoms as a comparator. The development of methods to make accurate, fine-scale predictions of future C. trachomatis infection will lay the groundwork for a future adaptive randomized trial that preferentially allocates more intensive intervention to villages predicted at enrollment to have high future levels of infection.