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

Statistical methods for the analysis of multi-antibody data to inform malaria control and elimination strategies
分析多抗体数据的统计方法,为疟疾控制和消除策略提供信息
批准号:
2766609
负责人:
金额:
$0.0万
依托单位:
依托单位国家:
英国
项目类别:
Studentship
财政年份:
2022
资助国家:
英国
项目状态:
未结题
起止时间:
2022 至 --

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中文摘要
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英文摘要
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.
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复杂图像处理中的自由非连续问题及其水平集方法研究
  • 批准号:
    60872130
  • 项目类别:
    面上项目
  • 资助金额:
    28.0万元
  • 批准年份:
    2008
  • 负责人:
    刘国才
  • 依托单位:
Computational Methods for Analyzing Toponome Data