This paper is in the following e-collection/theme issue:

Surveillance Systems (489) JMIR Theme Issue: COVID-19 Special Issue (2484) Artificial Intelligence (1658) Machine Learning (1679) Outbreak and Pandemic Preparedness and Management (1776) Infectious Diseases (non-STD/STI) (1646) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1527)

Published on in Vol 7, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26604, first published .
Identifying Communities at Risk for COVID-19–Related Burden Across 500 US Cities and Within New York City: Unsupervised Learning of the Coprevalence of Health Indicators

Identifying Communities at Risk for COVID-19–Related Burden Across 500 US Cities and Within New York City: Unsupervised Learning of the Coprevalence of Health Indicators

Identifying Communities at Risk for COVID-19–Related Burden Across 500 US Cities and Within New York City: Unsupervised Learning of the Coprevalence of Health Indicators

Authors of this article:

Andrew Deonarine1 Author Orcid Image ;   Genevieve Lyons1 Author Orcid Image ;   Chirag Lakhani1 Author Orcid Image ;   Walter De Brouwer1 Author Orcid Image
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JMIR Public Health: Identifying Communities at Risk for #COVID19 #coronavirus–Related Burden Across 500 US Cities a… https://t.co/3GPM5VsZMf

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RT @jmirpub: JMIR Public Health: Identifying Communities at Risk for #COVID19 #coronavirus–Related Burden Across 500 US Cities and Within N…

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JMIR Public Health and Surveillance JMIR Public Health and Surveillance @JMIR_PH

RT @jmirpub: JMIR Public Health: Identifying Communities at Risk for #COVID19 #coronavirus–Related Burden Across 500 US Cities and Within N…

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