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

Surveillance Systems (462) JMIR Theme Issue: COVID-19 Special Issue (2452) Artificial Intelligence (1371) Machine Learning (1465) Outbreak and Pandemic Preparedness and Management (1727) Infectious Diseases (non-STD/STI) (1575) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517)

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
Article Authors Cited by (3) Tweetations (2) Metrics
All Time | Last Month | Last Six Months | Last Year
There are currently no tweets available for this article within the specified range.