In previous posts, we discussed our massive ongoing symptom surveys that have reached over 12 million people in the U.S. since April 2020, in partnership with Facebook and Google. Another one of our major data initiatives is based on partnerships with healthcare systems, granting us access to various aggregate statistics from hospital records and insurance claims covering 10-15% of the United States population. From these data, we can extract informative indicators that can be early indicators of COVID activity. This post focuses on one indicator in particular, based on outpatient visits, and demonstrates both the challenges and promises associated with medical records data.

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One of our primary initiatives at the Delphi COVIDcast project has been to curate a diverse set of COVID-related data streams, and to make them freely available through our COVIDcast Epidata API. These include both novel signals that we have collected and analyzed ourselves, such as our symptom survey distributed by Facebook to its users, Google’s symptom survey whose results are delivered to us, the percentage of doctor’s visits due to COVID-like illness, and results from Quidel’s antigen tests; and also existing signals, such as the confirmed case counts and deaths reported by USA Facts and Johns Hopkins University. The COVIDcast API freely provides researchers and decision-makers with the data they need to conduct their work, and is conveniently accessible via easy-to-use Python and R packages.

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Since April 2020, in collaboration with Facebook, partner universities, and public health officials, we’ve been conducting a massive daily survey to monitor the spread and impact of the COVID-19 pandemic in the United States. Our survey, advertised by Facebook, is taken by about 74,000 people each day. Respondents provide information about COVID-related symptoms, contacts, risk factors, and demographics, allowing us to examine county-level trends across the US. We believe that this combination of detail and scale has never before been available in a public health emergency. In this post, we’ll share some of our initial survey findings, show you how to access the data, and highlight some of the exciting new directions that we’re pursuing.

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Hello from the Delphi research group at Carnegie Mellon University! We’re a group of faculty, students, and staff, based primarily out of CMU together with strong collaborators from other universities and industry. Our group was founded in 2012 to advance the theory and practice of epidemic forecasting. Since March 2020, we have refocused efforts towards helping combat the COVID-19 pandemic, by supporting informed decision-making at federal, state, and local levels of government and in the healthcare sector. Until now, we’ve been pretty “heads down” with our work, and slow to communicate what we’ve been up to. But at last … Delphi finally has a blog! This first post serves as an introduction of sorts. Future posts will dive deeper into our various projects.

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