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

Surveillance Systems (430) JMIR Theme Issue: COVID-19 Special Issue (2394) Artificial Intelligence (1216) Machine Learning (1401) Outbreak and Pandemic Preparedness and Management (1679) Infectious Diseases (non-STD/STI) (1490) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1484)

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

Andrew Deonarine   1 , MSc, MHSc, MD, PhD ;   Genevieve Lyons   1 , MSc ;   Chirag Lakhani   1 , PhD ;   Walter De Brouwer   1 , PhD

1 XY.ai, Cambridge, MA, United States

Corresponding Author:

  • Andrew Deonarine, MSc, MHSc, MD, PhD
  • XY.ai
  • 56 JFK Street
  • Cambridge, MA, 02138
  • United States
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  • Email: andrew@xy.ai