Using Machine Learning to Optimize COVID-19 Predictions

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With the current COVID-19 pandemic impacting many aspects of our lives, understanding the data and models around COVID-19 data are ever more crucial. Understanding the potential number of cases impacts the guidance around our policies (needing more hospital ICU beds, when to ease stay at home orders, when to open schools, etc.). In this session, we will focus on some exploratory data analysis to understand the accuracy of these models. We will then use machine learning models to improve them.

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About Scott Black


Scott is a Solution Architect with Databricks focusing on the public sector. He has an extensive background in database management and data engineering initially in e-commerce and healthcare. For the last 10 years he has focused on helping state and federal governments solve their toughest data challenges.

About Denny Lee


Denny Lee is a Developer Advocate at Databricks. He is a hands-on distributed systems and data sciences engineer with extensive experience developing internet-scale infrastructure, data platforms, and predictive analytics systems for both on-premise and cloud environments. He also has a Masters of Biomedical Informatics from Oregon Health and Sciences University and has architected and implemented powerful data solutions for enterprise Healthcare customers. His current technical focuses include Distributed Systems, Apache Spark, Deep Learning, Machine Learning, and Genomics.