Estimating Cholera Burden with Cross-sectional Immunologic Data

用横截面免疫学数据估计霍乱负担

• 批准号: 10132972
• 负责人: Daniel Ted Leung
• 金额: $ 66.34万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-25 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10132972
• 关键词: Acute; Address; Affect; Africa; Antibodies; Antibody Response; Area; Bangladesh; Bangladeshi; Biological Assay; Blood; Caring; Cessation of life; Cholera; Cholera Vaccine; Clinical; Collaborations; Computer Models; Computing Methodologies; Country; Data; Decision Making; Demographic Factors; Detection; Development; Devices; Diarrhea; Disease; Disease Outbreaks; Disease Surveillance; Enrollment; Epidemic; Epidemiology; Exposure to; Funding; Future; Generations; Genetic; Haiti; Haitian; Health care facility; Health system; Household; Immune response; Immunologics; Immunology; Incidence; Individual; Infection; Infrastructure; Intervention; Joints; Kinetics; Laboratories; Lateral; Link; Logistics; Measurement; Measures; Methods; Microbiology; Modeling; Nature; Oral; Patients; Population; Populations at Risk; Predisposition; Reporting; Research Infrastructure; Research Personnel; Resources; Risk; Rural; Sanitation; Sensitivity and Specificity; Serology; Serum; Specificity; Spottings; Standardization; Surveillance Methods; System; Time; Translations; Uncertainty; United States National Institutes of Health; Vibrio cholerae; Vibrio cholerae O1; Vibrio cholerae infection; Water; age related; base; cohort; diarrheal disease; disorder risk; fighting; high risk; improved; insight; lateral flow assay; machine learning method; novel; pathogen; predictive modeling; serosurveillance; serosurvey; statistical and machine learning; statistics; tool

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.

项目概要/摘要 霍乱是由霍乱弧菌感染引起的一种急性脱水性腹泻病。它流行于 50 多个国家，影响多达 300 万人，每年造成超过 10 万人死亡。一个更新的 在海地当前大规模流行病的推动下，全球抗击霍乱的努力正在进行中，现在得到了援助 新一代口服霍乱疫苗。确定霍乱高危人群对于指导霍乱至关重要 这些活动。目前估计霍乱负担的方法主要基于临床报告 很少进行微生物学确认。这些方法受到疫情的零星性、较差的 监测基础设施以及无症状或轻度症状人数的基本不确定性 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 血清中的免疫反应（血清监测）可以为快速准确的估计提供新途径 霍乱暴露和风险。我们目前不了解免疫学和临床参数是什么 最能预测最近的暴露情况，也不能预测不同流行程度地区的免疫反应 是相似的。在初步研究中，我们对来自以下来源的抗体反应数据使用了机器学习方法： 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，具有高灵敏度和特异性。在此应用中，我们建议使用纵向抗体 来自具有不同遗传和流行病学特征的人群的反应动力学，与新的统计数据相结合 和机器学习方法提供通用工具来估计接触的发生率 横断面血清调查中的霍乱弧菌。在目标 1 中，我们将开发模型来估计自 霍乱弧菌暴露以及横截面抗体谱和人口统计的暴露发生率 数据使用之前从孟加拉国的一个队列中收集的数据。这些结果将使我们能够确定 对于预测暴露后时间最有用的抗体和人口统计因素。在目标 2 中，我们将 从海地的一组霍乱病例和家庭接触者中收集纵向抗体数据，以开发 根据横断面血清监测估计暴露发生率的模型。这个队列也将使我们 比较针对中度/重度病例和轻度/无症状病例开发的模型。在目标 3 中，我们将 优化和验证测量霍乱特异性抗体的现场适应方法，包括使用干燥的 血斑和侧流检测。我们将使用这些进行概念验证横断面血清调查 海地农村的方法。完成这些目标后，我们将提供许多新工具 衡量人群对霍乱易感性的指标。这些工具将有可能改变霍乱 控制工作从当前的反应性策略转向主动性策略，有可能导致疾病 消除。