ArticleNature communications2022
App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Characterization of Influenza-Like Illness Burden Using Commercial Wearable Sensor Data and Patient-Reported Outcomes: Mixed Methods Cohort Study.Journal of medical Internet research · 2023Trial
- Enhancing COVID-19 Screening Models With Epidemiological and Mobility Features: Machine-Learning Model Study.JMIR AI · 2026Article
- Time series anomaly detection in helpline call trends for early detection of COVID-19 spread across Sweden, 2020.Scientific reports · 2025Article
- Estimating the trend of COVID-19 in Norway by combining multiple surveillance indicators.PloS one · 2025Article
- Person-Centered Web-Based Mobile Health System (Symptoms) for Reporting Symptoms in COVID-19 Vaccinated Individuals: Observational Study of System, Users, and Symptoms.JMIR formative research · 2024Observational
- Mobile Phone Syndromic Surveillance for Respiratory Conditions in an Emergency (COVID-19) Context in Colombia: Representative Survey Design.Journal of medical Internet research · 2024Article
- COVID-19 Vaccine Effectiveness and Digital Pandemic Surveillance in Germany (eCOV Study): Web Application-Based Prospective Observational Cohort Study.Journal of medical Internet research · 2024Observational
- Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review.JMIR public health and surveillance · 2024Article
- The association between Long-COVID symptomology, perceived symptom burden and mental health in COVID-19 patients in Shijiazhuang, China: a population-based health survey.Frontiers in psychiatry · 2024Article
- Syndromic surveillance of population-level COVID-19 burden with cough monitoring in a hospital emergency waiting room.Frontiers in public health · 2024Article
- Association between History of Vaccination and Symptoms at Diagnosis of Coronavirus Disease 2019.JMA journal · 2023Article
- Participatory Surveillance for COVID-19 Trend Detection in Brazil: Cross-sectional Study.JMIR public health and surveillance · 2023Article
- Detecting early signals of COVID-19 outbreaks in 2020 in small areas by monitoring healthcare utilisation databases: first lessons learned from the Italian Alert_CoV project.Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin · 2023Article
- Article
- A regression discontinuity analysis of the social distancing recommendations for older adults in Sweden during COVID-19.European journal of public health · 2022Article
- COVID-19 Modeling Outcome versus Reality in Sweden.Viruses · 2022Article
- App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden.Nature communications · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
36 authors.
Funding
Abstract
The app-based COVID Symptom Study was launched in Sweden in April 2020 to contribute to real-time COVID-19 surveillance. We enrolled 143,531 study participants (≥18 years) who contributed 10.6 million daily symptom reports between April 29, 2020 and February 10, 2021. Here, we include data from 19,161 self-reported PCR tests to create a symptom-based model to estimate the individual probability of symptomatic COVID-19, with an AUC of 0.78 (95% CI 0.74-0.83) in an external dataset. These individual probabilities are employed to estimate daily regional COVID-19 prevalence, which are in turn used together with current hospital data to predict next week COVID-19 hospital admissions. We show that this hospital prediction model demonstrates a lower median absolute percentage error (MdAPE: 25.9%) across the five most populated regions in Sweden during the first pandemic wave than a model based on case notifications (MdAPE: 30.3%). During the second wave, the error rates are similar. When we apply the same model to an English dataset, not including local COVID-19 test data, we observe MdAPEs of 22.3% and 19.0% during the first and second pandemic waves, respectively, highlighting the transferability of the prediction model.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.