Advancing Public Health Research in Eastern Africa through Data Science Training (APHREA-DST)

考虑到许多领域中前所未有的大量日益复杂和海量的数据 健康，数据科学家可以在利用大数据革命来解决多 解决了撒哈拉以南非洲的卫生挑战。然而，中国严重缺乏训练有素的数据科学家。 以及本土教育项目，以实现针对具体情况的培训。我们建议促进公众健康 通过建立新的卫生数据科学多层次培训计划在东非进行研究，最初 由于建立了良好的伙伴关系并显示出需求，因此将重点放在埃塞俄比亚和肯尼亚。合伙关系 哥伦比亚大学(CU，美国)、亚的斯亚贝巴大学(AAU，埃塞俄比亚)和内罗毕大学 (UofN，肯尼亚)将利用CU在数据科学方面的世界级优势，增强 埃塞俄比亚和肯尼亚，在非盟和UOFN的准备和全国突出地位的基础上再接再厉。使用面对面 和远程培训模式，我们将(I)在公共卫生数据方面开发新的针对具体情况的MS计划 科学，旨在远远超过资助期的可持续发展；(Ii)开展教师指导计划 为有前途的东非科学家建立和加强卫生数据科学方面的能力；和(2)开展 围绕有针对性的短期课程和研讨会构建的短期培训方案，涉及范围广泛 实习生。教师指导机制最初将从CU和东非之间的伙伴关系开始 教职员工，并将进入三个机构的小组。通过该计划培养的技能 将反过来加强数据科学方面的整体培训和研究能力。要将触角扩大到 科学界和政策界，短期培训将吸引来自政府和 非政府利益攸关方和私营部门。该计划将利用几项正在进行的研究 由团队成员或附属合作伙伴领导的环境健康、暴露评估、远程项目 卫星数据、职业暴露、气候变化、传染病、健康监测和健康 系统监控和评估，这将被用作沉浸机会，以实现动手体验 为学员提供新的数据科学技术。评估和监测将跟踪培训的成功情况 方案和受训人员实现其发展目标的情况，顺利完成研究 培训、科学报告和出版物，以及硕士学位课程的可持续性和增长。 在第五年，我们将通过分享课程和课程，将培训计划扩大到更广泛的东非地区 邀请受训人员参与。我们亦会探讨把现有课程合并的可行性。 发展到现有的博士课程或创建新的公共卫生数据科学博士项目。超越了 教育项目和合作，我们的项目旨在培养长期的区域合作， 终身学习技能，以及致力于开放科学、算法和研究人员的支持性社区 公平和“数据科学向善”，最终导致更好的公共卫生实践。

Given the unprecedented abundance of increasingly complex and voluminous data across many domains of health, data scientists could play a transformative role in exploiting the big data revolution to address the multi- pronged health challenges in sub-Saharan Africa. However, there is a severe lack of well-trained data scientists and home-grown educational programs to enable context-specific training. We propose to advance public health research in Eastern Africa by establishing new multi-tiered training programs in health data science, with initial focus on Ethiopia and Kenya due to well-established partnerships and demonstrated needs. A partnership between Columbia University (CU, USA), Addis Ababa University (AAU, Ethiopia) and University of Nairobi (UofN, Kenya) will leverage world class strengths in data science at CU to enhance the overall capacity in Ethiopia and Kenya by building upon the readiness and national prominence of AAU and UofN. Using in-person and distance modes of training, we will (i) develop new context-specific MS programs in public health data science, designed to be sustainable well beyond the funding period; (ii) undertake a faculty mentoring program to build and strengthen capacity in health data science for promising Eastern African scientists; and (ii) conduct a short-term training program structured around targeted short courses and workshops for a wide spectrum of trainees. The faculty mentoring mechanism will initially start with partnerships between CU and East African faculty, and will progress into groupings across the three institutions. The skills developed through this program will in turn strengthen the overall training and research capacity in data science. To broaden the reach into the scientific and policy community, the short-term training will engage trainees from partnering governmental and non-governmental stakeholders and the private sector. The program will leverage several ongoing research projects led by team members or affiliated partners on environmental health, exposure assessment, remote satellite data, occupational exposures, climate change, infectious diseases, health surveillance, and health system monitoring and evaluation, which will be used as immersion opportunities to enable hands-on experience with new data science techniques for trainees. Evaluation and monitoring will track the success of the training programs and of the trainees’ achievement of their development goals, successful completion of the research training, scientific presentations and publications, and the sustainability and growth of the MS degree programs. In Year 5, we will broaden the training program to the wider East Africa region through sharing of curricula and inviting trainees for engagement. We will also explore the feasibility of incorporating the courses we have developed into existing PhD curricula or creating new PhD programs in public health data science. Beyond the educational programs and collaborations, our project is designed to cultivate long-term regional collaboration, lifelong learning skills, and a supportive community of researchers committed to open science, algorithmic fairness, and “data science for good,” ultimately leading to better public health practice.