Smart Cloud for COVID-19 Prevention and Control

用于 COVID-19 预防和控制的智能云

• 批准号: 554170-2020
• 负责人: Wang, Mea
• 金额: $ 3.64万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=705386
• 关键词: Smart; Cloud; COVID; 19; Prevention

As we are entering the first of the three-stage provincial and federal reopening phases, AHS urgently needs to have an effective way to prevent and control future waves of the COVID-19 epidemic until we have a vaccine or completely contain the virus. The objective of this project is to design and develop a Smart (augmented-intelligent) Cloud solution to address this urgent need. There are many forms of prevention and control, in this project, we focus on two specific urgent needs: predictions of COVID-19 outbreaks and recommendations for preventing and controlling outbreaks. We achieve this by fusing COVID-19 information and data from various sources, performing deep data analysis and learning, and providing predictions and recommendations for COVID-19 outbreaks using machine learning techniques. Alberta Health Services (AHS), as Canada's first and largest provincewide, fully-integrated health system, tracks COVID-19 cases for Alberta and parts of Saskatchewan, B.C. and the Northwest Territories. In addition, AHS has direct access to contact tracing data from the ABTraceTogether app - the first and so-far the only contact tracing app adapted in Canada with over 140,000 downloads and growing. Our partnership with AHS will grant us credential access to COVID-19 data from a very large geographical area and contact tracing data, which gives us a unique advantage to lead the research in COVID-19 prevention and control. We will work closely with our collaborators from AHS to design and develop a Smart Cloud solution with three intended outcomes: targeted predictions of outbreaks, timely recommendations for prevention and control, and reports on effectiveness of medical responses. These will enable healthcare professionals, health data scientists, government authorities to make informed decisions and to plan early and efficiently. Patients will receive better treatment and care. All leads to less negative and dramatic impacts in our general public.

随着我们进入省级和联邦级重新开放三个阶段中的第一个阶段，AHS 迫切需要一种有效的方法来预防和控制未来几波 COVID-19 疫情，直到我们研制出疫苗或完全遏制该病毒。 该项目的目标是设计和开发智能（增强智能）云解决方案来解决这一迫切需求。 防控的形式有很多种，在这个项目中，我们重点关注两个具体的迫切需求：COVID-19疫情的预测和疫情防控的建议。 我们通过融合来自不同来源的 COVID-19 信息和数据、执行深度数据分析和学习，并使用机器学习技术为 COVID-19 爆发提供预测和建议来实现这一目标。 艾伯塔省卫生服务中心 (AHS) 是加拿大第一个也是最大的全省综合卫生系统，追踪艾伯塔省和不列颠哥伦比亚省萨斯喀彻温省部分地区的 COVID-19 病例。和西北地区。 此外，AHS 还可以直接访问来自 ABTraceTogether 应用程序的接触者追踪数据，这是第一个也是迄今为止唯一一款在加拿大适用的接触者追踪应用程序，下载量超过 140,000 次，并且还在不断增长。 我们与 AHS 的合作伙伴关系将使我们能够访问来自非常大的地理区域的 COVID-19 数据和接触者追踪数据，这使我们具有独特的优势来领导 COVID-19 预防和控制的研究。 我们将与 AHS 的合作者密切合作，设计和开发智能云解决方案，以实现三个预期成果：有针对性的疫情预测、及时的预防和控制建议以及医疗应对有效性报告。 这些将使医疗保健专业人员、健康数据科学家、政府当局能够做出明智的决策并尽早有效地进行规划。 患者将得到更好的治疗和护理。 所有这些都会减少对我们公众的负面和巨大影响。