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An open repository of real-time COVID-19 indicators.
Reinhart, Alex; Brooks, Logan; Jahja, Maria; Rumack, Aaron; Tang, Jingjing; Agrawal, Sumit; Al Saeed, Wael; Arnold, Taylor; Basu, Amartya; Bien, Jacob; Cabrera, Ángel A; Chin, Andrew; Chua, Eu Jing; Clark, Brian; Colquhoun, Sarah; DeFries, Nat; Farrow, David C; Forlizzi, Jodi; Grabman, Jed; Gratzl, Samuel; Green, Alden; Haff, George; Han, Robin; Harwood, Kate; Hu, Addison J; Hyde, Raphael; Hyun, Sangwon; Joshi, Ananya; Kim, Jimi; Kuznetsov, Andrew; La Motte-Kerr, Wichada; Lee, Yeon Jin; Lee, Kenneth; Lipton, Zachary C; Liu, Michael X; Mackey, Lester; Mazaitis, Kathryn; McDonald, Daniel J; McGuinness, Phillip; Narasimhan, Balasubramanian; O'Brien, Michael P; Oliveira, Natalia L; Patil, Pratik; Perer, Adam; Politsch, Collin A; Rajanala, Samyak; Rucker, Dawn; Scott, Chris; Shah, Nigam H; Shankar, Vishnu.
  • Reinhart A; Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213; areinhar@stat.cmu.edu.
  • Brooks L; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Jahja M; Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Rumack A; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Tang J; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Agrawal S; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Al Saeed W; Google.org Fellows, Google LLC, Mountain View, CA 94043.
  • Arnold T; Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Basu A; Linguistics Program, University of Richmond, Richmond, VA 23173.
  • Bien J; Information Networking Institute, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Cabrera ÁA; Department of Data Sciences and Operations, University of Southern California, Los Angeles, CA 90089.
  • Chin A; Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Chua EJ; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Clark B; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Colquhoun S; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • DeFries N; Google.org Fellows, Google LLC, Mountain View, CA 94043.
  • Farrow DC; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Forlizzi J; Google.org Fellows, Google LLC, Mountain View, CA 94043.
  • Grabman J; Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Gratzl S; Google.org Fellows, Google LLC, Mountain View, CA 94043.
  • Green A; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Haff G; Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Han R; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Harwood K; Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Hu AJ; Google.org Fellows, Google LLC, Mountain View, CA 94043.
  • Hyde R; Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Hyun S; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Joshi A; Google.org Fellows, Google LLC, Mountain View, CA 94043.
  • Kim J; Department of Data Sciences and Operations, University of Southern California, Los Angeles, CA 90089.
  • Kuznetsov A; Computer Science Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • La Motte-Kerr W; School of Natural Sciences and Mathematics, University of Texas at Dallas, Richardson, TX 75080.
  • Lee YJ; Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Lee K; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Lipton ZC; College of Fine Arts, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Liu MX; Department of Psychology, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Mackey L; Department of Statistics, University of California, Davis, CA 95616.
  • Mazaitis K; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • McDonald DJ; Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA 15213.
  • McGuinness P; Microsoft Research New England, Cambridge, MA 02142.
  • Narasimhan B; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • O'Brien MP; Department of Statistics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
  • Oliveira NL; Google.org Fellows, Google LLC, Mountain View, CA 94043.
  • Patil P; Department of Statistics, Stanford University, Stanford, CA 94305.
  • Perer A; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305.
  • Politsch CA; Google.org Fellows, Google LLC, Mountain View, CA 94043.
  • Rajanala S; Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Rucker D; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Scott C; Department of Statistics & Data Science, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Shah NH; Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213.
  • Shankar V; Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, PA 15213.
Proc Natl Acad Sci U S A ; 118(51)2021 12 21.
Article in English | MEDLINE | ID: covidwho-1569345
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
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ABSTRACT
The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Health Status Indicators / Databases, Factual / COVID-19 Type of study: Observational study / Prognostic study / Randomized controlled trials Limits: Humans Country/Region as subject: North America Language: English Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Health Status Indicators / Databases, Factual / COVID-19 Type of study: Observational study / Prognostic study / Randomized controlled trials Limits: Humans Country/Region as subject: North America Language: English Year: 2021 Document Type: Article