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

鉴于跨多个领域的日益复杂和庞大的数据前所未有的丰富， 数据科学家可以在利用大数据革命解决多方面问题方面发挥变革性作用， 撒哈拉以南非洲地区面临的健康挑战。然而，严重缺乏训练有素的数据科学家 和本土教育计划，以实现特定背景的培训。我们建议促进公共卫生 通过建立新的多层次健康数据科学培训计划，在东非进行研究， 由于建立了良好的伙伴关系和明确的需求，我们将重点放在埃塞俄比亚和肯尼亚。伙伴关系 哥伦比亚大学（CU，美国），阿迪斯亚的斯亚贝巴大学（AAU，埃塞俄比亚）和内罗毕大学之间 (UofN肯尼亚）将利用CU在数据科学方面的世界级优势， 在非洲联盟和尼日利亚联合国的准备和国家突出地位的基础上，加强埃塞俄比亚和肯尼亚的合作。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特定背景的MS程序 科学，旨在在资助期后可持续发展;（ii）开展教师指导计划 为有前途的东非科学家建立和加强健康数据科学能力;以及（ii）开展 一个短期培训计划，围绕有针对性的短期课程和讲习班，为广泛的 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 教师，并将在三个机构的分组进展。通过这个项目培养的技能 这将反过来加强数据科学的整体培训和研究能力。为了扩大影响范围， 短期培训将吸引来自合作政府和 非政府利益攸关方和私营部门。该计划将利用几个正在进行的研究 由小组成员或附属伙伴领导的关于环境健康、接触评估、远程监测和监测的项目 卫星数据、职业暴露、气候变化、传染病、健康监测和健康 系统监测和评估，将作为沉浸式机会，以获得实践经验 新的数据科学技术。评价和监测将跟踪培训的成功情况 计划和受训者实现其发展目标，成功完成研究 培训，科学演讲和出版物，以及MS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培训计划，将培训计划扩大到更广泛的东非地区。 邀请学员参与。我们亦会研究可否把现有的课程 开发到现有的博士课程或创建新的公共卫生数据科学博士课程。超出 教育计划和合作，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、算法的研究人员支持社区 公平和“数据科学为善”，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.