This is the home of Delphi's epidemiological data API. See our API documentation for details on the available data sets, APIs, and clients.
We are working on collecting several new data sources that may be useful for nowcasting and forecasting ILI during the COVID-19 pandemic. Each of these will make these available as soon as individually possible, through our covidcast endpoint.
For a list of many other data sources relevant to COVID-19, publicly available through external sites, we have compiled a simple spreadsheet.
Note: apart from the COVID-19-specific data sources described above, the rest of this repository was built to support modeling and forecasting efforts surrounding seasonal influenza (and dengue). All other data sources than those in our covidcast endpoint should be held to great scrutiny (if they are even to be considered at all in this pandemic period), since they were designed to serve as indicators of typical seasonal ILI (influenza-like illness), and certainly not pandemic ILI or CLI (covid-like illness).
If you are interested in contributing:
- For development of the API itself, see the development guide.
- To suggest changes, additions, or other ways to improve, open an issue describing your idea.
We hope that this API is useful to others outside of our group, especially for epidemiological and other scientific research. If you use this API and would like to cite it, we would gratefully recommend the following copy:
David C. Farrow, Logan C. Brooks, Aaron Rumack, Ryan J. Tibshirani, Roni Rosenfeld (2015). Delphi Epidata API. https://github.com/cmu-delphi/delphi-epidata
- Cook, Samantha, et al. "Assessing Google flu trends performance in the United States during the 2009 influenza virus A (H1N1) pandemic." PloS one 6.8 (2011): e23610.
- Broniatowski, David A., Michael J. Paul, and Mark Dredze. "National and local influenza surveillance through Twitter: an analysis of the 2012-2013 influenza epidemic." (2013): e83672.
- Dredze, Mark, et al. "HealthTweets. org: A Platform for Public Health Surveillance using Twitter." AAAI Conference on Artificial Intelligence. 2014.
- Generous, Nicholas, et al. "Global disease monitoring and forecasting with Wikipedia." (2014): e1003892.
- Hickmann, Kyle S., et al. "Forecasting the 2013–2014 Influenza Season Using Wikipedia." (2015): e1004239.
- McIver, David J., and John S. Brownstein. "Wikipedia usage estimates prevalence of influenza-like illness in the United States in near real-time." PLoS Comput Biol 10.4 (2014): e1003581.