Estimating Cholera Burden with Cross-sectional Immunologic Data

项目摘要/摘要 霍乱是一种急性脱水腹泻疾病，由霍乱弧菌感染引起。它在中国是地方病 在50多个国家，每年有多达300万人受到影响，造成100 000多人死亡。一个新的 全球抗击霍乱的努力正在进行中，海地持续的大规模疫情推动了这一努力，现在又得到了 新一代口服霍乱疫苗。识别霍乱高危关键人群对于指导 这些活动。目前估计霍乱负担的方法主要基于临床报告 很少有微生物确认。这些方法受到疫情零星性质的限制，很差 监测基础设施，以及基本不确定的无症状或轻度 有症状的病例。迫切需要改进检测霍乱暴露和风险的方法。检测 血清免疫反应的监测(血清监测)可以为快速和准确的评估提供新的途径 霍乱的暴露和风险。我们目前还不了解免疫学和临床参数是什么。 最能预测最近的暴露，也不是不同地方性疾病地区的免疫反应 是相似的。在初步研究中，我们使用了机器学习方法来处理来自 对孟加拉国的霍乱患者进行分类，以确定个人是否在过去30年、90年或 360天，具有较高的敏感性和特异性。在这一应用中，我们建议使用纵向抗体 反应动力学，来自具有不同遗传和流行病学特征的人群，与新的统计学配对 和机器学习方法，以提供可推广的工具来估计暴露于 来自横断面血清调查的霍乱弧菌。在目标1中，我们将开发模型来估计自 霍乱弧菌暴露与横断面抗体谱和人口学的暴露发生率 数据使用以前从孟加拉国的一个队列收集的数据。这些结果将使我们能够识别 抗体和人口统计因素对预测自接触以来的时间最有用。在目标2中，我们将 从海地一群霍乱病例和家庭接触者中收集纵向抗体数据，以开发 通过横断面血清监测估算暴露发病率的模型。这批人也将使我们能够 比较中/重度病例和轻度/无症状病例的模型。在《目标3》中，我们将 优化和验证现场适应的方法来测量霍乱特异性抗体，包括使用干的 血斑和侧向血流量分析。我们将使用以下内容进行概念验证横断面血清调查 方法在海地农村地区进行。在完成这些目标后，我们将提供一些新的工具来 衡量人群中霍乱易感性的指标。这些工具将有可能改变霍乱 控制努力从当前的反应性战略转向主动性战略，有可能导致疾病 淘汰赛。

Project Summary/Abstract Cholera is an acute dehydrating diarrheal disease caused by infection with Vibrio cholerae. It is endemic in over 50 countries, affecting up to 3 million people and causing more than 100,000 deaths annually. A renewed global effort to fight cholera is underway, catalyzed by the large on-going epidemic in Haiti and now aided by new generation oral cholera vaccines. Identifying key populations at high risk of cholera is essential to guide these activities. Current methods to estimate cholera burden are largely based on clinical reporting with infrequent microbiological confirmation. These methods are limited by the sporadic nature of outbreaks, poor surveillance infrastructure, and fundamental uncertainties in the number of asymptomatic or mildly symptomatic cases. Improved methods of detecting cholera exposure and risk are urgently needed. Detection of immune responses in serum (serosurveillance) can provide a new avenue for rapid and accurate estimates of cholera exposure and risk. We currently do not understand what immunological and clinical parameters are most predictive of recent exposure, nor whether immune responses in areas with different levels of endemicity are similar. In preliminary studies, we have used machine learning methods on antibody response data from cholera patients in Bangladesh to classify whether individuals had been exposed in the previous 30-, 90-, or 360-days with high sensitivity and specificity. In this application, we propose to use longitudinal antibody response kinetics, from populations with diverse genetic and epidemiologic profiles, paired with novel statistical and machine learning approaches to provide generalizable tools to estimate the incidence of exposure to Vibrio cholerae from cross sectional serosurveys. In Aim 1, we will develop models to estimate the time since exposure to Vibrio cholerae and exposure incidence from cross-sectional antibody profiles and demographic data using previously collected data from a cohort in Bangladesh. These results will allow us to identify the antibodies and demographic factors that are most useful for prediction of time-since-exposure. In Aim 2, we will collect longitudinal antibody data from a cohort of cholera cases and household contacts in Haiti to develop models for estimating exposure incidence from cross-sectional serosurveillance. This cohort will also enable us to compare the models developed for moderate/severe cases and mild/asymptomatic cases. In Aim 3, we will optimize and validate field-adapted methods to measure cholera-specific antibodies, including the use of dried blood spot and lateral flow assays. We will conduct a proof-of-concept cross-sectional serosurvey using these methods in rural Haiti. Upon the completion of these aims, we will have provided a number of new tools for measure of susceptibility to cholera in a population. These tools will have the potential to transform cholera control efforts from the current reactive strategies to proactive ones, with the potential to contribute to disease elimination.