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， 在采取干预措施的同时，疟疾监测在乌干达等高负担国家尤其薄弱。疟疾 分子监测（MMS），评估生物体内存在的寄生虫DNA和宿主抗体， 样本，以获得流行病学上可行的信息，有可能改善目前的 监测方法;然而，在研究环境之外，这些数据的使用有限。电流 研究的重点是了解血清学和寄生虫遗传数据如何用于加强常规 疟疾监测方法和评价疟疾控制干预措施。最近，我们的团队得到了资助， 通过在64个左右的集水区加强被动监测，直接测量疟疾发病率 乌干达各地的医疗设施。然后，这64个集群将被随机化以接收两种类型的 新的蚊帐，并在每个地点进行横断面调查后12个月，24个月和36个月， 干预已经完成。这个K23项目提供了一个绝佳的机会， 从这64个地点的横截面调查中抽取样本，以检验MMS将使我们能够 使用增强的发病率，比寄生虫流行率更准确地估计疟疾发病率 数据为黄金标准。我们的方法将是使用已建立的分子技术，包括多重 血清学检测、qPCR和扩增子深度测序，以从样本中生成分子指标 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。目的1是使用血清学指标来改善估计 与主要基于PfPR的标准模型相比，目标二是 使用基于寄生虫DNA的指标，以改善对5岁以下儿童疟疾发病率的估计， 到主要基于PfPR的标准模型。在目标3中，我们将确定最适合 估计发病率随时间的变化，以确定MMS指标（血清学和 基于寄生虫DNA）可用于准确预测发病率的变化 每年在每个地点。为了完成这个项目，我将需要额外的指导和培训， 血清流行病学和生物统计学方法，以及公共卫生和监测方面的实地经验 本提案所述的活动。这个K23奖将为我从一个 实验室科学家，以实现我的职业目标，成为一个分子流行病学家，重点是公众 健康监测，具有有效利用分子数据的技能，以评估，开发和应用 在人口一级采取干预措施，控制和消除疟疾。我将从这个奖项中脱颖而出， 强大的NIH R 01应用程序侧重于利用分子数据，以加强疟疾监测的设置， 卫生基础设施薄弱，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.