Back to Search Start Over

An open repository of real-time COVID-19 indicators.

Authors :
Reinhart, Alex
Brooks, Logan
Jahja, Maria
Rumack, Aaron
Jingjing Tang
Agrawal, Sumit
Al Saeed, Wael
Arnold, Taylor
Basu, Amartya
Bien, Jacob
Cabrera, Ángel A.
Chin, Andrew
Eu Jing Chua
Clark, Brian
Colquhoun, Sarah
DeFries, Nat
Farrow, David C.
Forlizzi, Jodi
Grabman, Jed
Gratzl, Samuel
Source :
Proceedings of the National Academy of Sciences of the United States of America; 12/21/2021, Vol. 118 Issue 51, p1-8, 8p
Publication Year :
2021

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
00278424
Volume :
118
Issue :
51
Database :
Complementary Index
Journal :
Proceedings of the National Academy of Sciences of the United States of America
Publication Type :
Academic Journal
Accession number :
154590433
Full Text :
https://doi.org/10.1073/pnas.2111452118