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.

项目总结/摘要 霍乱是一种由霍乱弧菌感染引起的急性脱水性肠道疾病。它是地方性的， 50多个国家，影响多达300万人，每年造成10万多人死亡。重新 全球抗击霍乱的努力正在进行中，海地正在发生的大规模疫情推动了这一努力， 新一代口服霍乱疫苗。确定霍乱高风险的关键人群对于指导 这些活动。目前估计霍乱负担的方法主要基于临床报告， 很少进行微生物确认。这些方法受到暴发的散发性质的限制， 监测基础设施，以及无症状或轻度 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）可以提供一个新的途径，快速和准确的估计 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，我们使用机器学习方法对来自 孟加拉国的霍乱患者，以分类个人是否在过去30年，90年，或 360-具有较高的灵敏度和特异性。在本申请中，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 从横断面抗体谱和人口统计学资料看霍乱弧菌暴露和暴露发生率 数据使用先前从孟加拉国队列收集的数据。这些结果将使我们能够识别 抗体和人口统计学因素，这是最有用的预测时间，因为暴露。在目标2中，我们将 从海地霍乱病例和家庭接触者队列中收集纵向抗体数据， 从横断面血清监测中估计暴露发生率的模型。这群人也将使我们 比较为中度/重度病例和轻度/无症状病例开发的模型。在目标3中，我们 优化和验证适合现场的方法，以测量霍乱特异性抗体，包括使用干燥的 血斑和侧流分析。我们将使用这些方法进行概念验证的横断面血清调查。 在海地农村的方法。在完成这些目标后，我们将提供一些新的工具， 人群中对霍乱易感性的测量。这些工具将有可能改变霍乱 控制工作从目前的反应性战略转向积极主动的战略，有可能导致疾病 淘汰