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

Journals

  1. Herasimova O, Herasimova O. IMPROVING THE SYSTEM OF INDICATORS FOR ASSESSING THE EPIDEMIOLOGICAL SITUATION AND STRENGTHENING RESTRICTIVE MEASURES IN THE CONDITIONS OF ADAPTIVE QUARANTINE CAUSED BY THE SPREAD OF COVID-19. Ekonomìka ì prognozuvannâ 2022;2022(1):52 View
  2. Herasimova O, Herasimova O. Improving the system of indicators for assessing the epidemiological situation and strengthening restrictive measures in the conditions of adaptive quarantine caused by the spread of COVID-19. Economy and forecasting 2022;2022(1):31 View

Books/Policy Documents

  1. Aquino Y, Shih P, Bosward R. Reference Module in Biomedical Sciences. View