Improving the accuracy of malaria surveillance with serology and parasite genetic data

项目摘要 尽管迫切需要高质量的数据来规划、实施和评估疟疾控制 在干预措施方面，疟疾监测在乌干达等高负担国家特别差。疟疾 分子监测(MMS)，评估存在于生物体内的寄生虫DNA和宿主抗体 样本以获得流行病学上可操作的信息，有可能在现有的基础上改进 监测方法；然而，这些数据在研究环境之外的使用有限。一股水流 研究的重点是了解如何利用血清学和寄生虫基因数据来加强常规 疟疾监测方法和评估疟疾控制干预措施。最近，我们的团队获得了资助， 通过加强周边集水区被动监测直接测量疟疾发病率 乌干达各地的卫生设施。然后，这些集群将被随机化，以接收两种类型的 新的蚊帐，并将在每个地点进行横断面调查后12，24和36个月后- 退出的干预已经完成。这个K23项目提供了一个绝佳的机会来利用 从这些遗址的横断面调查样本来验证彩信将使我们能够 使用增强发病率比寄生虫流行率(PfPR)更准确地估计疟疾发病率 以数据为黄金标准。我们的方法将是使用现有的分子技术，包括多重 血清学分析、定量聚合酶链式反应和扩增子深度测序，从样本中产生分子指标 在这些横断面调查中收集到的；然后我们将使用这些分子指标建立统计模型 作为变量来估计发病率作为结果。目标1是使用血清学指标来改进估计 与主要以PfPR为基础的标准模型相比，儿童疟疾发病率下降了5年。目标2是 使用基于寄生虫DNA的指标来改进儿童疟疾发病率的估计&5年比较 到主要基于PfPR的标准模型。在目标3中，我们将确定最好的MMS指标集 估计发病率随时间的变化，以确定彩信指标(包括血清学和 寄生虫DNA)可以用来准确预测每个调查时间点之间的发病率变化 在每个地点年复一年。为了完成这个项目，我需要额外的指导和培训 除了在公共卫生和监测方面的现场经验外，还有血清流行病学和生物统计学方法 本提案中概述的活动。这个K23奖项将为我从一个 实验室科学家实现我的职业目标，成为一名专注于公众的分子流行病学家 健康监测，具有有效利用分子数据进行评估、开发和应用的技能 人口层面的疟疾控制和消除干预措施。我将从这个奖项中脱颖而出，为 强大的NIH R01应用程序侧重于利用分子数据来加强在以下环境中的疟疾监测 卫生基础设施差，以便更好地针对疟疾控制干预措施。

Project Abstract Despite the critical need for high quality data in order to plan, implement, and evaluate malaria control interventions, malaria surveillance is particularly poor in high burden countries such as Uganda. Malaria molecular surveillance (MMS), which evaluates parasite DNA and host antibodies present in biological samples to derive epidemiologically actionable information, has the potential to improve upon current surveillance methods; however, there is limited use of these data outside of the research setting. A current research priority is to understand how serologic and parasite genetic data can be used to enhance routine malaria surveillance methods and evaluate malaria control interventions. Recently, our team was funded to directly measure malaria incidence via enhanced passive surveillance within the catchment areas around 64 health facilities throughout Uganda. These 64 clusters will then be randomized to receive one of two types of novel bednets, and cross-sectional surveys will be performed at each site 12, 24, and 36 months after the roll- out of the intervention has been completed. This K23 project offers an outstanding opportunity to leverage samples from cross-sectional surveys at these 64 sites to test the hypothesis that MMS will enable us to estimate malaria incidence with more accuracy than parasite prevalence (PfPR), using enhanced incidence data as the gold standard. Our approach will be to use established molecular techniques, including multiplex serologic assays, qPCR, and amplicon deep-sequencing, to generate molecular metrics from samples collected in these cross-sectional surveys; we will then build statistical models using these molecular metrics as variables to estimate incidence as the outcome. Aim 1 is to use serologic metrics to improve the estimation of malaria incidence in children <5 years compared to standard models based primarily on PfPR. Aim 2 is to use parasite DNA-based metrics to improve the estimation of malaria incidence in children <5 years compared to standard models based primarily on PfPR. In Aim 3, we will identify the set of MMS metrics that best estimates changes in incidence over time to determine how changes in MMS metrics (both serologic and parasite DNA-based) between each survey timepoint can be used to accurately predict changes in incidence from year to year at each site. To complete this project, I will need additional mentorship and training in seroepidemiology and biostatistical methods in addition to field experience in public health and surveillance activities as outlined in this proposal. This K23 award will provide the crucial link in my transition from a laboratory scientist to achieving my career goal of becoming a molecular epidemiologist with a focus on public health surveillance, with the skills to effectively utilize molecular data to evaluate, develop, and apply population level interventions for malaria control and elimination. I will emerge from this award prepared for a strong NIH R01 application focused on utilizing molecular data to enhance malaria surveillance in settings with poor health infrastructure in order to better target malaria control interventions.