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

Formative Evaluation of Digital Health Interventions (3453) JMIR Theme Issue: COVID-19 Special Issue (2564) Human Factors and Usability Case Studies (1068) Machine Learning (2230) Clinical Information and Decision Making (2070) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1546)

Published on in Vol 6, No 9 (2022): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35114, first published .
Exploring Socioeconomic Status as a Global Determinant of COVID-19 Prevalence, Using Exploratory Data Analytic and Supervised Machine Learning Techniques: Algorithm Development and Validation Study

Exploring Socioeconomic Status as a Global Determinant of COVID-19 Prevalence, Using Exploratory Data Analytic and Supervised Machine Learning Techniques: Algorithm Development and Validation Study

Exploring Socioeconomic Status as a Global Determinant of COVID-19 Prevalence, Using Exploratory Data Analytic and Supervised Machine Learning Techniques: Algorithm Development and Validation Study

Authors of this article:

Luke Winston1 Author Orcid Image ;   Michael McCann1 Author Orcid Image ;   George Onofrei2 Author Orcid Image
Article Authors Cited by (5) Tweetations (3) Metrics

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  2. Nery L, de Paula Nicomedes N, de Godoy Camargo P, de Outeiro S, Filho L, de Farias C, Silva D. Analysis of Models to Estimate Morbidity Rates of Respiratory Diseases Through Deep Learning. Tropical Medicine & International Health 2026 View

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