Data, Biostatistics & Modeling Core

数据、生物统计和建模核心将作为实地工作期间收集的数据的储存库 项目，使流行病学、分子、免疫学和其他生物数据能够有效地联系起来 从参与者那里测量。 数学模型将发挥不可或缺的作用：分析收集的数据以生成假设，这些假设将 对照随后收集的数据进行核实或伪造。在项目1：流行病学中，在 将使用Papau新几内亚、泰国和柬埔寨的初步横断面调查为设计提供参考 各种监视策略。这些战略作为干预措施的潜在有效性 将使用间日疟原虫和恶性疟原虫的数据驱动的数学模型来模拟疟疾的传播 变速箱。然后，最有希望的战略将在实地试验中进行测试。 数学模型也将对项目3：复发做出重要贡献。在此期间收集的样本 将进行纵向研究，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，这是 分类将使用专门设计的宿主内部数学模型以概率方式进行。这 模型将生成可与多种潜在基因类型进行比较的概率表型 记号笔。

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.