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

Mobile Health (mhealth) (3110) Public (e)Health, Digital Epidemiology and Public Health Informatics (840) mHealth for Data Collection and Research (1054) Behavioural Surveillance for Public Health (248) Innovative Methods in Public Health and Surveillance (354)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46026, first published .
Predictors of COVID-19 From a Statewide Digital Symptom and Risk Assessment Tool: Cross-Sectional Study

Predictors of COVID-19 From a Statewide Digital Symptom and Risk Assessment Tool: Cross-Sectional Study

Predictors of COVID-19 From a Statewide Digital Symptom and Risk Assessment Tool: Cross-Sectional Study

Authors of this article:

Benjamin L Schooley1 Author Orcid Image ;   Abdulaziz Ahmed2 Author Orcid Image ;   Justine Maxwell2 Author Orcid Image ;   Sue S Feldman2 Author Orcid Image
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Tom Bellis Tom Bellis @tommybellis

Predictors of COVID-19 From a Statewide Digital Symptom and Risk Assessment Tool: Cross-Sectional Study https://t.co/XJSfyyaJMr https://t.co/7jBFhdtPST

3:28 PM · Jul 25, 2023

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JMIR Publications JMIR Publications @jmirpub

New in JMIR: Predictors of #COVID19 #coronavirus From a Statewide #Digital Symptom and Risk Assessment Tool: Cross-Sectional #Study https://t.co/ZB3btLFchS https://t.co/VixH2Er0nN

3:51 PM · Jul 25, 2023

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Journal of Medical Internet Research Journal of Medical Internet Research @JMIR_

RT @jmirpub: New in JMIR: Predictors of #COVID19 #coronavirus From a Statewide #Digital Symptom and Risk Assessment Tool: Cross-Sectional #…

3:51 PM · Jul 25, 2023

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