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: 2825141
• 金额: --
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2825141
• 关键词: Statistical; methods; analysis; multi; antibody

T3 - Evidence into Practice T4 Practice to Policy/PopulationSignificant progress has been made to reduce the burden of malaria globally. Despite the challenges caused by the disruption of malaria prevention and treatment services, malaria cases and deaths dropped between 2019 and 2021 according to the World Health Organization's World Malaria Report. The decrease in the burden of malaria as created low transmission settings where traditional methods for estimating malaria burden, such as parasite prevalence and entomological inoculation rates, suffer from temporal variation and low precision. To overcome these limitations, there is a growing interest in utilizing immunological data for malaria surveillance. Serology offers valuable insights into transmission intensity by quantifying prior exposure to malaria parasites. However, the lack of standardized approaches for analysing and modelling serological data poses challenges for malaria control programs.Currently, the commonly used approaches in analysing serological data are mixture models, reversible catalytic models and antibody acquisition models. While they provide valuable insights when compared to traditional malaria metrics like parasite prevalence and entomological inoculation rate, these modelling approaches have different limitations that make them prone to producing biased estimates of malaria transmission. Additionally, the prevailing practice in the analysis of sero-epidemiological data involves the estimation of the transmission of malaria from multiple antibody responses independently, with potential effects on the accuracy and precision.The limitations of the existing modelling approaches underscore the need for further refinement and development of modelling approaches to enhance their accuracy and robustness. This PhD project aims to address these challenges and contribute to the advancement of modelling strategies for the surveillance of malaria with the potential of extension to other infectious diseases in which serosurveillance is actively employed. In particular, this project will develop multivariate statistical methods that can overcome the limitations of the common practice of treating multiple antibody responses independently.

T3 -将证据转化为实践T4从实践到政策/人口在减少全球疟疾负担方面取得了重大进展。根据世界卫生组织发布的《世界疟疾报告》，尽管疟疾防治服务中断带来挑战，但2019年至2021年疟疾病例和死亡人数有所下降。疟疾负担的减少创造了低传播环境，在这种环境中，估计疟疾负担的传统方法，如寄生虫流行率和昆虫接种率，存在时间变化和精确度低的问题。为了克服这些局限性，人们越来越有兴趣利用免疫学数据进行疟疾监测。血清学通过量化先前接触疟疾寄生虫的情况，为传播强度提供了有价值的见解。然而，缺乏标准化的方法来分析和建模的血清学数据的疟疾控制程序提出了挑战，目前，常用的方法在分析血清学数据的混合物模型，可逆催化模型和抗体获得模型。虽然与寄生虫流行率和昆虫接种率等传统疟疾指标相比，这些建模方法提供了有价值的见解，但这些建模方法具有不同的局限性，使其易于对疟疾传播产生有偏见的估计。此外，分析血清流行病学数据的普遍做法涉及独立估计多种抗体反应引起的疟疾传播，这对准确性和精确性有潜在影响，现有建模方法的局限性突出表明，需要进一步完善和发展建模方法，以提高其准确性和稳健性。该博士项目旨在应对这些挑战，并有助于推进疟疾监测的建模策略，并有可能扩展到血清监测积极采用的其他传染病。特别是，该项目将开发多变量统计方法，可以克服独立治疗多种抗体反应的常见做法的局限性。