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Search: WFRF:(Cadet J.) > (2020-2022)

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1.
  • Sudre, C. H., et al. (author)
  • Symptom clusters in COVID-19 : A potential clinical prediction tool from the COVID Symptom Study app
  • 2021
  • In: Science Advances. - : American Association for the Advancement of Science (AAAS). - 2375-2548. ; 7:12
  • Journal article (peer-reviewed)abstract
    • As no one symptom can predict disease severity or the need for dedicated medical support in coronavirus disease 2019 (COVID-19), we asked whether documenting symptom time series over the first few days informs outcome. Unsupervised time series clustering over symptom presentation was performed on data collected from a training dataset of completed cases enlisted early from the COVID Symptom Study Smartphone application, yielding six distinct symptom presenta- tions. Clustering was validated on an independent replication dataset between 1 and 28 May 2020. Using the first 5 days of symptom logging, the ROC-AUC (receiver operating characteristic – area under the curve) of need for respiratory sup- port was 78.8%, substantially outperforming personal characteristics alone (ROC-AUC 69.5%). Such an approach could be used to monitor at-risk patients and predict medical resource requirements days before they are required. 
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2.
  • Kennedy, Beatrice, 1982-, et al. (author)
  • App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden
  • 2022
  • In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 13:1
  • Journal article (peer-reviewed)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.
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