SACS and the COVID-19 response

Data science and COVID-19

The Research and Development faculty at Meharry’s School of Applied Computational Sciences are contributing to the COVID-19 response by connecting with and serving health care organizations with its data science expertise and first-class resources.  

Addressing COVID-19 health disparities and vaccine acceptance

SACS is also providing data analytics and visualization to support the Center for Social Determinants of Health to refine the Social Vulnerability Index (SVI) to inform our understanding of COVID-19 disparities and their potential influence on COVID-19 vaccine acceptance. Blue Cross Blue Shield is funding the study as a response to how COVID-19 is disproportionally affecting minority communities.

Improving COVID-19 diagnosis and treatment options

The School is part of the national COVID-19 Consortium of HCA Healthcare and Academia for Research Generation (CHARGE) consortium. Dr. Aize Cao, associate professor of biomedical data science, Dr. Todd Gary, director, community and research development, and Dr. Fortune Mhlanga, dean and professor of computer science and data science, are part of the world-class team. They will research HCA’s extensive COVID-19 patient data sets to pursue improvements in COVID-19 diagnosis and treatment options to achieve a better public understanding of the disease.

Using mHealth for early diagnosis and prediction of COVID-19 risk

Information from mobile health technology presents tremendous opportunities that can also help address COVID-19. Dr. Vibhuti Gupta, assistant professor of computer science and data science, is partnering with Dr. Sung Won Choi and Dr. Muneesh Tewari from the University of Michigan to harness mobile health technology for early diagnosis and prediction of COVID-19 patients. They are examining data captured from mHealth apps, wearable sensor devices, longitudinal health outcomes (patient-reported outcomes, survey questionnaires), and other test results to explore early diagnosis and prediction of COVID-19 risk among health care workers and students.

Developing risk adjustment models for COVID-19

The School’s Population Health Disparity Research Lab brings research faculty together to apply statistics and machine learning technologies to leverage big electronic health records (EHR) and informatics to improve understanding of health disparity as well as individual and population health. This discovery also has potential to develop phenotype risk adjustment models for COVID-19. Dr. Aize Cao is pursuing an NIH grant to pursue that scholarship.

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