Background: We developed a self-assessment and participatory surveillance web application for coronavirus disease (COVID-19), which was launched in France in March 2020.
Objective: Our objective was to determine if self-reported symptoms could help monitor the dynamics of the COVID-19 outbreak in France.
Methods: Users were asked questions about underlying conditions, sociodemographic status, zip code, and COVID-19 symptoms. Depending on the symptoms reported and the presence of coexisting disorders, users were told to either stay at home, contact a general practitioner (GP), or call an emergency phone number. Data regarding COVID-19-related hospitalizations were retrieved from the Ministry of Health.
Results: As of March 29, 2020, the application was opened 4,126,789 times; 3,799,535 electronic questionnaires were filled out; and 2,477,174 users had at least one symptom. In total, 34.8% (n=1,322,361) reported no symptoms. The remaining users were directed to self-monitoring (n=858,878, 22.6%), GP visit or teleconsultation (n=1,033,922, 27.2%), or an emergency phone call (n=584,374, 15.4%). Emergency warning signs were reported by 39.1% of participants with anosmia, a loss of the sense of smell (n=127,586) versus 22.7% of participants without anosmia (n=1,597,289). Anosmia and fever and/or cough were correlated with hospitalizations for COVID-19 (Spearman correlation coefficients=0.87 and 0.82, respectively; P<.001 for both).
Conclusions: This study suggests that anosmia may be strongly associated with COVID-19 and its severity. Despite a lack of medical assessment and virological confirmation, self-checking application data could be a relevant tool to monitor outbreak trends.
Trial Registration: ClinicalTrials.gov NCT04331171; https://clinicaltrials.gov/ct2/show/NCT04331171.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7295000 | PMC |
| http://dx.doi.org/10.2196/19855 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Web application using machine learning to predict cardiovascular disease and hypertension in mine workers.
Sci Rep
December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
View Article and Find Full Text PDFRNA-ModX: a multilabel prediction and interpretation framework for RNA modifications.
Brief Bioinform
November 2024
In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, 250 Wuxing Street, 110, Taipei, Taiwan.
Accurate prediction of RNA modifications holds profound implications for elucidating RNA function and mechanism, with potential applications in drug development. Here, the RNA-ModX presents a highly precise predictive model designed to forecast post-transcriptional RNA modifications, complemented by a user-friendly web application tailored for seamless utilization by future researchers. To achieve exceptional accuracy, the RNA-ModX systematically explored a range of machine learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit, and Transformer-based architectures.
View Article and Find Full Text PDFBackground: Mobile devices are now widely and extensively used by university students. With the rapid advancement in technology, mobile learning has become an important educational model, particularly for health schools' students. This research aims to investigate the attitudes of health schools' students in Jordan towards mobile learning, as well as their perceptions of mobile learning advantages and disadvantages compared to the traditional face-to-face learning model.
View Article and Find Full Text PDFApplication and interpretation of core elements of the 2015 NMOSD diagnostic criteria in routine clinical practice.
Front Immunol
December 2024
Department of Neurology, University of Virginia, Charlottesville, VA, United States.
Background: We evaluated comprehension and application of the 2015 neuromyelitis optica spectrum disorder (NMOSD) criteria core elements by neurologists in Latin America (LATAM) who routinely diagnose and care for NMOSD patients by (i) identifying typical/suggestive NMOSD syndromes, (ii) detecting typical MRI NMOSD lesions and meeting MRI dissemination in space (DIS) criteria, and (iii) evaluating historical symptoms suggestive of NMOSD.
Methods: We conducted an anonymous, voluntary, self-administered web- and case-based survey cross-sectional study from October 2023 to January 2024 of neurologists identified through the LACTRIMS database. Questions were presented first through iterative clinical cases or imaging, followed by questions directly evaluating comprehension of definitions.
Efficacy of autologous platelet concentrates for root coverage of Miller's Class I and II gingival recession defects: A systematic review and meta-analysis.
Dent Res J (Isfahan)
November 2024
Department of Periodontology, Dental Student's Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran.
Background: This systematic review and meta-analysis aimed to assess the efficacy of autologous platelet concentrate (APCs) in comparison with coronally-advanced flap alone or in combination with connective tissue graft or other biomaterials or bioactive agents for root coverage (RC) of Miller's Class I and II gingival recession defects by measuring the keratinized mucosa width (KMW).
Materials And Methods: This systematic review and meta-analysis was conducted in accordance with the preferred reporting items for systematic reviews and meta-analysis guidelines. An electronic search of the literature was conducted in PubMed, EMBASE, Scopus, Cochrane, Web of Science, Magiran, Scientific Information Database, and Irandoc for randomized clinical trials (RCTs) that used APCs for RC in their intervention group.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!