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很容易受到健康状况不佳的影响，如全国健康排名不佳，具有挑战性的农村地理和卫生专业人员短缺，2019年新型冠状病毒病（COVID-19）的影响将在该州持续很长时间。患者发病率和死亡率已经继续上升，对卫生系统和企业造成持续的经济损害。COVID-19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，再加上该疾病的新颖性质，有必要加快研究以应对这一大流行病。 随着临床医生和一线卫生工作者为拯救生命而战，创建一个加速研究的数据环境是抗击疾病的关键和必要条件。 该提案将通过联合多个国家合作伙伴并利用相关数据来发现COVID-19，从而建立加速研究和情报收集的能力。为了实现这一目标，该提案旨在（1）通过符合HIPAA的安全服务器创建一个去识别的链接数据库系统，以整理南卡罗来纳州COVID-19患者和治疗COVID-19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COVID-19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COVID-19患者短期和长期临床结局的重要预测因素。这些目标将通过与多个州机构和与COVID-19相关的利益相关者合作以及创建一个安全的符合HIPAA的数据库来实现，该数据库允许及时合并相关数据，并利用全州范围内的综合数据仓库功能。

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.