MADIVA (Multimorbidity in Africa: Digital innovation, visualisation and application)

项目摘要-Madiva：非洲的多发病：数字 创新、可视化和应用研究中心 我们的研究中心将发展数据科学，以解决复杂的相互作用，个人和公共健康 挑战，以及非洲人多发性疾病的患病率不断上升。非洲的流行病转变已经 极大地改变了个人和公共卫生层面的健康风险。创伤，传染性和非传染性 传染病是主要的健康挑战；多次发生会增加个人的负担 和社区。来自日益丰富和多样化的现有数据的内在价值还没有 被剥削。我们拥有丰富的纵向流行病学数据集，部分但日益可用的临床和 实验室记录和基因组数据。正如域中常见的那样，数据是高度异质的，并且在 零碎的、残缺不全的。 该中心将由约翰内斯堡的威特沃特斯兰德大学领导，与非洲 人口与健康研究中心(肯尼亚内罗毕)，IBM非洲研究所，南非人口 研究基础设施网络和范德比尔特大学医学中心。最初的地点是一个城市 内罗毕的人口和南非布什巴克里奇的一个农村过渡地点：每个人都有丰富的 &gt；10万人的纵向数据。尽管不完整，但临床记录越来越多地被 以数字方式捕获。值得注意的是，每个地点都有来自个体子集的基因组数据。 多发病本质上是多因素的--碎片化的非关联数据阻碍了解决方案。项目1专注于 在链接上。综合数据集将有助于了解这些社区，这些社区代表着非洲的许多社区， 而制定的协议将适用于其他地方。研究人员仪表板将允许研究人员找到 数据和计划项目。诊所仪表板将帮助诊所经理和卫生官员监测和规划 有效地。项目2将以解决方案为导向，开发新的数据科学方法，以提供可行的 洞察力。我们将开发强大的方法来对个人和社区进行风险分析，使用 异类数据。我们在非洲率先使用多基因风险评分。关键的数据科学问题是 结果的可解释性和管理高维数据而不会影响性能。的一个创新方面 我们的赠款将是与公共精确健康合作的翻译工作，以提供对药品分发的见解， 药物基因组学，以及行为或可能的社会干预。 能力发展和试点项目核心将支持非洲的一套分级试点项目。 不同职业阶段的研究人员，利用我们强大的研究平台和网络。我们的目标是建立 关键领域的能力，包括数据科学的孵化和成本建模。行政部和 领导核心将提供必要的领导和项目管理以及与其他DS-I的链接 组件。数据管理和分析核心将制定协议，以便负责任地分享 数据、数据质量控制、技术支持以及创新分析技术。

Project summary – MADIVA: Multimorbidity in Africa: Digital innovation, visualisation and application Research Hub Our research hub will develop data science to address the complex interplay, individual and public health challenges, and rising prevalence of multimorbidity in Africans. Epidemiological transition in Africa has greatly changed health risks at both individual and public health levels. Trauma, infectious & non- communicable diseases are major health challenges; multiple occurrences multiply burdens for individuals and communities. The inherent value from the increasingly rich and varied existing data has not been exploited. We have rich longitudinal epidemiological data sets, partial but increasingly available clinical and laboratory records, and genomic data. The data, as is common in the domain, is highly heterogeneous, and in parts, fragmented and incomplete. The Hub will be led by the University of the Witwatersrand, Johannesburg collaborating with the African Population and Health Research Center (Nairobi, Kenya), IBM Research Africa, the South African Population Research Infrastructure Network and Vanderbilt University Medical Center. The initial sites are an urban population in Nairobi, and a rural, transitioning, site in Bushbuckridge, South Africa: each has rich longitudinal data of >100,000 individuals. Although incomplete, clinical records are increasingly being captured digitally. Notably there is genomic data from a subset of individuals at each of the sites. Multimorbidity is inherently multi-factorial — fragmented unlinked data retards solutions. Project 1 focuses on linking. Integrated data sets will aid in understanding these communities, representative of many in Africa, and protocols developed will be applicable elsewhere. A researcher dashboard will allow researchers to find data and plan projects. A clinic dashboard will help clinic managers and health officials to monitor and plan effectively. Project 2 will be solution-oriented to develop novel data science methods to provide actionable insights. We will develop robust methods for risk profiling of individuals and communities using heterogeneous data. We pioneer the use of polygenic risk scores in Africa. Key data science questions are explicability of results and managing high-dimensional data without loss of power. An innovative aspect of our grant will be translational work with public precision health to provide insights into drug dispensing, pharmacogenomics, and behavioral or possible social interventions. The Capacity Development & Pilot Project Core will support a tiered set of pilot projects for African researchers at different career stages, leveraging our strong research platforms and networks. We aim to build capacity in key areas including incubation and cost modelling for data science. The Administrative and Leadership Core will provide the necessary leadership and project management and linking to other DS-I components. The Data Management and Analysis Core will develop protocols for the responsible sharing of data, data quality control, technical support as well as innovate new analysis techniques.