BDD CIS: Big Data Driven Clinical Informatics & Surveillance - A Multimodal Database Focused Clinical, Community, & Multi-Omics Surveillance Plan for COVID19

由于南卡罗来纳州的人口已经容易受到健康状况不佳的影响，国民健康排名不佳，挑战农村地理和卫生专业人员短缺，新型冠状病毒疾病2019(新冠肺炎)的影响将在该州长期存在。患者发病率和死亡率已经在继续增加，对卫生系统和企业的经济损害也在持续。新冠肺炎在南卡罗来纳州和美国的传播速度和地理传播速度令人担忧，再加上该疾病的新奇性质，有理由加快研究以抗击这一大流行。在临床医生和一线卫生工作者奋力挽救生命之际，创建一个加快研究的数据环境是关键，也是抗击这种疾病的必要之举。该提议将联合多个国家合作伙伴，并利用相关数据发现新冠肺炎周围的问题，从而建设加快研究和情报收集的能力。为了实现这一目标，这项建议旨在(1)通过符合HIPAA标准的安全服务器创建一个未识别的链接数据库系统，以整理南卡罗来纳州新冠肺炎患者和治疗新冠肺炎患者的卫生工作者的监测、临床、多组学和地理空间数据；(2)检查新冠肺炎的自然历史，包括传播动力学、疾病进展和地理空间可视化；以及(3)使用机器学习算法确定南卡罗来纳州新冠肺炎患者短期和长期临床结果的重要预测因素。这些目标将通过与多个与新冠肺炎相关的州机构和利益攸关方的合作来实现，并创建一个安全的、符合《国际公共部门会计准则》的数据库，以便及时合并相关数据，并利用全州范围的综合数据仓库能力。

With South Carolina’s population already being vulnerable to poor health as evidenced by poor national health rankings, challenging rural geography and health professional shortages, the impact of the novel Coronavirus Disease 2019 (COVID-19) will be long lasting in the state. Patient morbidity and mortality rates already continue to increase, with ongoing economic damage to health systems and businesses. The speed of transmission and geographical spread of COVID-19 across South Carolina and the United States is alarming, which combined with the novel nature of the disease justifies the need for accelerated research to combat this pandemic. As clinicians and frontline health workers battle to save lives, creating a data environment that accelerates research is key, and necessary to fight against the disease. This proposal will build the capacity for accelerated research and intelligence gathering by coalescing multiple state partners and leveraging relevant data for discoveries around COVID-19. To accomplish this, this proposal aims to (1) create a de-identified linked database system via a HIPAA compliant secure server to collate surveillance, clinical, multi-omics and geospatial data on both COVID-19 patients and health workers treating COVID-19 patients in South Carolina; (2) examine the natural history of COVID-19 including transmission dynamics, disease progression, and geospatial visualization; and (3) identify important predictors of short- and long-term clinical outcomes of COVID-19 patients in South Carolina using machine learning algorithms. These aims will be accomplished through collaborations with multiple state agencies and stakeholders relevant to COVID-19 and the creation of a secure HIPAA compliant database that allow for coalescing relevant data in a timely fashion, combined with leveraging of statewide integrated data warehouse capabilities.