Empowering Indian Health Researchers with Computational Modeling Tools

 匹兹堡大学全球健康研究和研究培训eCapacity计划的目标是通过信息和通信技术的创新，利用建模和模拟来评估替代疾病控制策略的健康和经济影响，从而加强印度基于证据的数据驱动决策。为了使匹兹堡大学公共卫生动态实验室（PHDL）开发的方法和课程的优势全球化，我们认为必须让当地公共卫生专家带头开发，运行和分析模型。我们的目标是提高研究人员使用计算建模的能力，不仅在我们的Fogarty培训基地SHARE INIDA（D43 TW009078），而且在印度的多个Fogarty培训基地。在介绍阶段1，为期两天的研讨会（35名与会者），分享印度，海得拉巴，将介绍综合决策支持途径的概念，以及基本的建模软件和工具。本次研讨会将展示研究，使数据驱动的决策使用尖端的方法，包括数学，网络和基于代理的建模。五名印度研究人员和一名建模支持人员将在匹兹堡大学PHDL为期一周的研讨会上接受深入的计算建模实践培训。这个关于复杂的基于代理的建模的研讨会将使用我们的开源建模平台FRED（重建流行病动力学的框架）。这一经验将促进建立一个新的合作网络，在印度研究人员和PHDL研究人员之间进行建模研究。将颁发两个小型建模开发补助金。第二阶段将侧重于可持续性。两个小型研究补助金将帮助维持新开发的合作建模项目。两名PHDL建模人员将返回印度访问小额赠款的接受者并巩固合作。为期两周的公共卫生计算建模短期课程，包括匹兹堡研讨会的教育材料和免费软件，将由CR Rao研究所的印度教师介绍，每个培训班有25名参与者。第二次介绍性研讨会有35名新参与者，由新培训的印度建模人员领导，将激发对建模的新一轮兴趣。我们的目标是解决印度未满足的需求。建设研究人员使用尖端但适当的技术解决政策相关问题的能力。该教育计划将增加印度福格蒂附属研究人员对模型和建模基本方法所需数据类型的了解。参加匹兹堡研讨会并通过小额研究补助金继续使用建模的学员预计将继续应用计算建模方法来解决对印度重要的公共卫生问题。预计这些学员还将在印度和PHDL教师中领导计算建模合作。在未来，该计划预计将导致资助的研究，帮助印度公共卫生的数据驱动决策。

 DESCRIPTION (provided by applicant): The goal of the University of Pittsburgh Global Health Research and Research Training eCapacity Initiative is to enhance evidence-based, data-driven decision-making in India through innovations in information and communication technology using modeling and simulation to assess the health and economic impacts of alternative disease control strategies. To globalize the advantages of methods and curricula developed in the Public Health Dynamics Laboratory (PHDL) at the University of Pittsburgh, we believe it is essential to have local public health experts take the lead in developing, running and analyzing the models. Our objective is to enhance the capacity of researchers to use computational modeling, not only at our Fogarty training site, SHARE INIDA (D43 TW009078), but also at multiple Fogarty training sites in India. In the introductory Phase 1, a two-day symposium (35 participants), SHARE INDIA, Hyderabad, will introduce the concept of the integrated decision support pathway, and basic modeling software and tools. This symposium will showcase research that enables data-driven decision making using cutting-edge methods including mathematical, network, and agent-based modeling. Five Indian investigators plus one modeling support staff will receive in-depth, hands- on training in computational modeling during a week-long workshop at the University of Pittsburgh PHDL. This workshop on complex agent-based modeling will use our open-source modeling platform, FRED (Framework for Reconstructing Epidemic Dynamics). This experience will foster establishment of a new collaborative network for modeling studies among Indian researchers and with PHDL researchers. Two small modeling developmental grants will be awarded. Phase 2 will focus on sustainability. Two small research grants will help sustain newly developed, collaborative modeling projects. Two PHDL modelers will return to India to visit the recipients of the small grants and solidify collaboratios. Two week-long short courses on computational modeling in public health, incorporating educational materials and free software from the Pittsburgh Workshop, will be presented by Indian faculty at CR Rao Institute targeting 25 participants each. A second introductory symposium for 35 new participants, led by newly trained Indian modelers, will stimulate a new wave of interest in modeling. Our aims address an unmet need in Indiai.e. building capacity of researchers to use cutting-edge, yet appropriate technology to address policy-relevant questions. The education program will result in increased knowledge among Fogarty-affiliated researchers in India of the kinds of data needed to inform models and basic methods in modeling. Trainees who attend the workshops in Pittsburgh and sustain their use of modeling through small research grants are expected to continue to apply computational modeling methods to address Public Health questions of importance to India. These trainees are also expected to lead computational modeling collaborations within India and with PHDL faculty. In the future, the program is expected to result in funded research that aids data-driven decision-making in Public Health in India.