دورية أكاديمية
App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden
| العنوان: | App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden |
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| المؤلفون: | Kennedy, Beatrice, Fitipaldi, Hugo, Hammar, Ulf, Maziarz, Marlena, Tsereteli, Neli, Oskolkov, Nikolay, Varotsis, Georgios, Franks, Camilla A., Nguyen, Diem, Spiliopoulos, Lampros, Adami, Hans-Olov, Björk, Jonas, Engblom, Stefan, Fall, Katja, Grimby-Ekman, Anna, Litton, Jan-Eric, Martinell, Mats, Oudin, Anna, Sjöström, Torbjörn, Timpka, Toomas, Sudre, Carole H., Graham, Mark S., du Cadet, Julien Lavigne, Chan, Andrew T., Davies, Richard, Ganesh, Sajaysurya, May, Anna, Ourselin, Sébastien, Pujol, Joan Capdevila, Selvachandran, Somesh, Wolf, Jonathan, Spector, Tim D., Steves, Claire J., Gomez, Maria F., Franks, Paul W., Fall, Tove |
| بيانات النشر: | Uppsala universitet, Molekylär epidemiologi Uppsala universitet, Science for Life Laboratory, SciLifeLab Uppsala universitet, Avdelningen för beräkningsvetenskap Uppsala universitet, Tillämpad beräkningsvetenskap Uppsala universitet, Allmänmedicin och preventivmedicin Springer Nature |
| سنة النشر: | 2022 |
| المجموعة: | Uppsala University: Publications (DiVA) |
| مصطلحات موضوعية: | Public Health, Global Health, Social Medicine and Epidemiology, Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi |
| الوصف: | 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. ; eSSENCE - An eScience Collaboration |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| العلاقة: | Nature Communications, 2022, 13; orcid:0000-0002-0066-4814; orcid:0000-0002-9680-5772; orcid:0000-0002-3614-1732; orcid:0000-0002-6060-6229; orcid:0000-0003-2071-5866; http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-473382Test; PMID 35449172; ISI:000785003900026 |
| DOI: | 10.1038/s41467-022-29608-7 |
| الإتاحة: | https://doi.org/10.1038/s41467-022-29608-7Test http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-473382Test |
| حقوق: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.5F6B0F16 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1038/s41467-022-29608-7 |
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