COVID-19 and Hospital Capacity Planning


How can data science help to address the COVID-19 crisis? One of the most important ways data science can help is in hospital capacity planning by predicting—for any given hospital—how many beds will be needed, which patients will be discharged, and how much personal protective equipment medical professionals will need.

To this end, a team of data scientists at Penn Medicine, in close collaboration with epidemiologists, have built an open-source tool called CHIME that makes such predictions and is being used by decision-makers at Penn Medicine’s network of hospitals in Pennsylvania and New Jersey. In this webinar, Dr. Hugo Bowne-Anderson (DataCamp) and Michael Becker (Penn Medicine) discuss the impact of the COVID-19 crisis on hospitals, how data science can make key predictions, how such predictions are used by decision-makers, and how you can help.


Hugo Bowne-Anderson

Head of Data Science Evangelism and Marketing at Coiled; Previously a Data Scientist at DataCamp

Hugo Bowne-Anderson is Head of Data Science Evangelism and VP of Marketing at Coiled, a company that makes it simple for organizations to scale their data science seamlessly. He has extensive experience as a data scientist, educator, evangelist, content marketer, and data strategy consultant at DataCamp, the online education platform for all things data. He has experience teaching basic to advanced data science topics at institutions such as Yale University and Cold Spring Harbor Laboratory, conferences such as SciPy, PyCon, and ODSC and with organizations such as Data Carpentry. He has developed over 30 courses on the DataCamp platform, impacting over 500,000 learners worldwide through his own courses. He also created the weekly data industry podcast DataFramed, which he hosted and produced for 2 years. He is committed to spreading data skills, access to data science tooling, and open-source software, both for individuals and the enterprise.

Connect with Hugo on LinkedIn and Twitter.

Michael Becker

Senior Data Scientist, Penn Medicine

Connect with Michael on LinkedIn and Twitter.

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