Seroepidemiologic methods to identify hotspots of trachoma and predict future infection

项目总结/摘要 背景：沙眼是由沙眼衣原体引起的眼部感染， 全球范围内的传染性失明，并已成为全球消除公共卫生问题的目标， 2020.这一目标将在许多国家实现，但埃塞俄比亚的一些地区持续保持高水平。 尽管开展了>10年的密集防治活动，但感染率仍然很低。一小部分人可能 大多数沙眼感染者都在这些地区，感染点（“热点”）的规模等于或低于村庄规模; 运营挑战是准确预测他们在现有数据中的位置。机器学习的进步 和空间数据科学已经证明了预测的空间分辨率的显着改善， 像疟疾这样的疾病在可用的沙眼生物标志物中，儿童的IgG抗体应答可能 能够更准确地预测，因为它们整合了随时间的暴露并反映了最近的传播。 目的：本研究的主要目的是评估抗体测量是否可以识别稳定的 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， 水平沙眼感染向前的时间（长达3年）。我们假设感染会集中在 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C水平高的村庄。沙眼感染 方法：为了验证我们的假设，我们将利用来自一个特征良好的人群的测量结果， 40个村庄参加了在埃塞俄比亚阿姆哈拉地区进行的NIH资助的随机分组试验（U10-EY 023939）。 这项为期三年的试验旨在衡量改善水、卫生和洗手（WASH）的效果 在没有阿奇霉素治疗的情况下对沙眼感染的影响。该试验收集了临床和生物标志物 从大约2,400名0-9岁的儿童在入组时和3年以上的年度访视中进行测量 年我们将使用！-统计，等于相对风险 在不同距离的病例中感染。我们将使用基于置换的空间扫描统计数据来 每年使用IgG抗体和PCR方法确定热点，并确定这些热点是否在 时间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 遥感、地理空间信息和有限的登记特点。我们将按照以下顺序排列村庄 预测血清阳性率，并将评估PCR C的比例。沙眼感染率最高 1、2、3年后的村庄。我们将使用预测的临床症状作为比较物重复分析。 对未来C.沙眼感染将奠定 未来适应性随机试验的基础，优先分配更密集的干预措施， 在登记时预测未来感染水平较高的村庄。

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.