Data, Biostatistics & Modeling Core

数据、生物统计学

• 批准号: 10368195
• 负责人: SIMON CAUCHEMEZ
• 金额: $ 3.58万
• 依托单位: WALTER AND ELIZA HALL INST MEDICAL RES
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-28 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10368195
• 关键词: Data; Biostatistics; Modeling; Core

The Data, Biostatistics & Modelling Core will act as a repository for data collected during the field-based projects, enabling efficient linking of epidemiological, molecular, immunological and other biological data measured from the participants. Mathematical models will play an integral role: analysing collected data to generate hypotheses which will be verified or falsified against subsequently collected data. In Project 1: Epidemiology, data collected during the initial cross-sectional surveys in Papau New Guinea, Thailand and Cambodia will be used to inform the design of various surveillance strategies. The potential effectiveness of these strategies as interventions to reduce malaria transmission will be simulated using data driven mathematical models of P. vivax and P. falciparum transmission. The most promising strategies will then be taken forward for testing in field trials. Mathematical models will also make an important contribution to Project 3: Relapse. Samples collected during longitudinal studies will be genotyped to provide a detailed understanding of the dynamics of P. vivax and P. falciparum infection. The combination of epidemiological data and genotyping methods will not allow for definitive classification of P. vivax infections into relapses, re-infections, or recrudescences. Instead, this classification will be done probabilistically using a specially designed within-host mathematical model. This model will generate probabilistic phenotypes which can be compared to a wide range of potential genotypic markers.

数据、生物统计和建模核心将作为实地调查期间收集的数据的储存库。 项目，能够有效地链接流行病学、分子、免疫学和其他生物数据 从参与者的角度来衡量。 数学模型将发挥不可或缺的作用：分析收集的数据，以产生假设， 根据随后收集的数据进行验证或伪造。在项目1：流行病学中， 在巴布亚新几内亚、泰国和柬埔寨进行的初步横断面调查将用于为设计提供信息 各种监视策略。这些战略作为干预措施， 将使用间日疟原虫和恶性疟原虫的数据驱动数学模型来模拟疟疾传播 传输最有希望的策略将在田间试验中进行测试。 数学模型也将对项目3：复发作出重要贡献。期间采集的样品 纵向研究将进行基因分型，以提供对间日疟原虫和间日疟原虫动态的详细了解。 恶性疟原虫感染流行病学数据和基因分型方法的结合将不允许 将间日疟原虫感染明确分类为复发、再感染或复发。相反， 将使用专门设计的宿主内数学模型以概率方式进行分类。这 模型将产生概率表型，可以与广泛的潜在基因型进行比较， 标记。