“Data is the life blood in public health, and models inform action.” Dr. Tom Frieden, former CDC Director
Data Science

COVID-19 underscores the importance of data and analytics

November 17, 2020
Data science is accelerating our learnings from the COVID-19 pandemic.
Vice President, Marketing

In conjunction with KDD 2020, Geneia’s chief analytics and technology officer Fred Rahmanian had the opportunity to join a prestigious group of healthcare leaders to discuss Emerging Data Science Challenges in the era of COVID-19. Moderated by Bharat Rao, PhD, leader, Healthcare and Life Sciences Data Analytics at KPMG, the panel featured:

  • Tom Frieden, MD, MPH, former CDC Director, President and CEO, Resolve to Save Lives
  • Professor Tanya Berger-Wolf, Director, Translational Data Institute, Ohio State University
  • Kash Patel, MS, Chief Digital Health Officer, Penn Medicine
  • Professor Geoff Webb, Research Director, Monash University Data Futures Institute
Knowledge discovery in databases 2020 - speakers

As Fred emphasized, data science is accelerating our learnings and recovery from the COVID-19 pandemic. One of many examples is the COVID-19 severe impacts model created by the Geneia Data Intelligence Lab. The model predicts which patients are at high risk for developing severe adverse health outcomes if they contract COVID-19, insights that healthcare organizations can use to educate and intervene with identified patients.

Dr. Tom Frieden: “Data is the life blood in public health, and models inform action.”

Dr. Frieden discussed his experience creating and monitoring a dashboard of data-driven interventions during the Ebola crisis. He also reminded listeners about an infamous model that predicted more than one million cases of Ebola in the three months following the first known case in the United States, a model that demonstrated how urgent it was to mobilize. In the words of Frieden, “Data is the life blood in public health, and models inform action.”

Penn Medicine’s chief digital health officer Kash Patel shared his organization’s focus on data during the initial surge of the COVID-19 pandemic. “After the first week, we took a deep dive into the data and quickly built a dashboard for all of our hospitals and clinics, something we reviewed twice a day.” Over time, the dashboard became increasingly sophisticated, including the number of ventilators and amount of PPE being used and ultimately patient outcomes. Early on, their dashboard helped Penn Medicine to discover the importance of co-morbidities in COVID-19 patient outcomes and of pronation to shorten patient hospital stays.

Another panelist, Professor Webb, discussed Melbourne, Australia’s use of data science to recognize and plan for COVID-19 impacts. Despite far fewer care trips, the number of traffic accidents has not declined. Their analysis revealed that more people are biking, resulting in additional car-bike accidents, a behavior change the city now expects to continue.

As COVID-19 surges across the country, data and predictive analytics are being used for “better allocating resources with regards to testing, personal protective equipment, medications and more.” Researchers from Texas A&M University created a deep learning model that predicts the growth of COVID-19 cases for each county in the coming seven days with 64 percent accuracy. “The team trained the model using COVID-19 data from March through May 2019, focusing on four factors that influence the spread of disease both spatially and temporally:

  • Population attributes (population density),
  • Population activities, (e.g., adherence to social distancing guidelines),
  • Mobility (moving from more infected places to less infected ones) and
  • Disease spread attributes (such as reproduction number.)”

Undoubtedly, the focus on COVID-19 data and analytics will give rise to some new interesting new applications. Google, for example, has pledged millions of dollars to support COVID-19 AI and data analytics projects with a focus on four areas:

  1. Monitoring and forecasting disease spread
  2. Improving health equity and minimizing secondary effects of the pandemic
  3. Slowing transmission by advancing the science of contact tracing and environmental sensing
  4. Supporting healthcare workers

At the Geneia Data Intelligence Lab, we are intrigued at the possibility of using data about patients who chose to use of telehealth during the pandemic to improve patient engagement. To say we’re enthusiastic about the future of data and analytics innovations that emerge from the COVID-19 pandemic is a BIG understatement.