NIH
Award Abstract #3R01AI127203-04S1

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

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Program Manager:

Rosemary G McKaig

Active Dates:

Awarded Amount:

$626,275

Investigator(s):

Xiaoming Li

Bankole Olatosi

Awardee Organization:

UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
South Carolina

Funding ICs:

National Institute of Allergy and Infectious Diseases (NIAID)

Abstract:

With South Carolinas 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 battle the disease. Access to such information will equip frontline health workers to continue the 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 REDCap and a mobile application (app) 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 REDCap database and mobile app that allow for coalescing relevant data in a timely fashion, combined with leveraging of statewide integrated data warehouse capabilities.

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