An Open Repository of Real-Time COVID-19 Indicators
Author:
Reinhart AlexORCID, Brooks Logan, Jahja Maria, Rumack Aaron, Tang Jingjing, Agrawal Sumit, Saeed Wael Al, 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, Motte-Kerr Wichada La, 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.ORCID, Shankar Vishnu, Sharpnack James, Shemetov Dmitry, Simon Noah, Smith Benjamin Y., Srivastava Vishakha, Tan Shuyi, Tibshirani Robert, Tuzhilina Elena, Van Nortwick Ana Karina, Ventura Valérie, Wasserman Larry, Weaver Benjamin, Weiss Jeremy C., Whitman Spencer, Williams Kristin, Rosenfeld Roni, Tibshirani Ryan J.
Abstract
AbstractThe 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 de-identified 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 is 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.
Publisher
Cold Spring Harbor Laboratory
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