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

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
  3. Faccin M, Geenen C, Happaerts M, Ombelet S, Migambi P, André E. Analyzing Satellite Imagery to Target Tuberculosis Control Interventions in Densely Urbanized Areas of Kigali, Rwanda: Cross-Sectional Pilot Study. JMIR Public Health and Surveillance 2025;11:e68355 View
  4. Bosward M, Braunack-Mayer A, Frost M, Carter S. The emergence and future of precision public health: a scoping review. Health Policy and Technology 2025;14(5):101056 View

Books/Policy Documents

  1. Aquino Y, Shih P, Bosward R. International Encyclopedia of Public Health. View