Can Covid-19 be understood through the lens of data analysis? By using artificial intelligence, variables previously going unused could be seen in a new light.

Press play to learn:

• If viral load dictates how contagious an individual may be
• The accuracy of the initial data surrounding the Covid-19 pandemic
• The data gaps that still need to be explored

Sally Embrey, DataRobot’s Vice President of Public Health and Health Technologies, shares her experience using data modeling to better understand the Covid-19 pandemic.

By focusing on viral loads and the variables in the pandemic that may cause asymptomatic spread, hotspots could start being identified before they became a threat. By understanding the chain of events from initial infection to the first signs of symptoms, populations under threat could better understand how to combat the virus within their community.

Using data sources collected from publicly available databases from around the country and globally, the consistency and accuracy of the data could reach the level needed for useful predictions and analysis. Due to this new availability of quality research, the work from DataRobot has been able to be used on the federal level and beyond.

To learn more, visit datarobot.com/covid/.

Episode also available on Apple Podcasts: apple.co/30PvU9C