Statistical methods for the analysis of multi-antibody data to inform malaria control and elimination strategies

抗体数据为疾病控制和监测提供信息的潜力正日益得到认可。流行性感冒、沙眼、淋巴丝虫病和疟疾是积极进行血清监测的传染病的例子。因此，提倡建立世界血清库，以促进下一代血清监测工具，在过去十年中，由于疟疾传播减少，疟疾死亡和病例数量减少，疟疾血清监测的相关性有所增加。在低传播环境中，旨在检测人类或蚊子感染的传统疟疾指标的估计值的不确定性可能显著增加。此外，由于这些指标受到抽样框架和疟疾传播季节性的严重影响，这一问题更加严重。血清学研究克服了这些局限性，因为它们旨在量化接触而不是感染。因此，世界组织（世卫组织）在其消除疟疾指南中正在考虑血清学评估，血清流行病学分析的普遍做法是独立处理来自多种抗体应答的数据，以估计疟疾传播强度。该项目将专注于开发多变量统计方法，克服这种方法的局限性，以充分借用多抗体数据的信息强度。总的来说，该项目有三个主要目标：（i）使用机器学习技术在多重数据中选择信息抗体，（ii）将现有的无阈值方法扩展到多变量环境，以及（iii）在疾病消除前消除的背景下研究多变量血清学结果的性能。本项目开发的统计方法也将用于其他传染病（例如，新冠肺炎（COVID-19），其中血清学评估也是其控制和消除策略的优先事项。

The potential of antibody data to inform disease control and surveillance is being increasingly recognised. Influenza, trachoma, lymphatic filariasis and malaria are examples of infectious diseases where sero-surveillance is actively performed. As a consequence, the creation of a World Serum Bank has been advocated to facilitate the next generation of sero-surveillance tools.In the last decade, the relevance of sero-surveillance for malaria has increased due to reductions in malaria transmission evidenced by decreasing numbers of malaria deaths and cases. In low transmission settings, the uncertainty in the estimates of conventional malaria metrics aimed at detecting the presence of infection in humans or mosquitoes, can increase significantly. In addition, this issue is exacerbated by the fact these metrics are strongly affected by the sampling frame and the seasonality of malaria transmission. Serological studies overcome such limitations, because they aim to quantify exposure rather than infection. As a result, serological assessment is now being considered by the World Organization (WHO) in their guidelines for malaria elimination.The prevailing practice in sero-epidemiological analyses is to estimate malaria transmission intensity treating the data from multiple antibody responses independently. This project will focus on the development of multivariate statistical methods that overcome the limitations of this approach to fully borrow the strength of information across multi-antibody data. Overall, the project has three main objectives: (i) selection of informative antibody in multiplex data using machine learning techniques, (ii) extending existing threshold-free methodology to a multivariate setting, and (iii) study of the performance of multivariate serological outcomes in the context of disease pre-elimination elimination. The developed statistical methods in this project will also be deployed to other infectious diseases (e.g., COVID-19), where serological assessment is also a priority in their control and elimination strategies.