App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID Symptom Study Sweden
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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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SciScore for 10.1101/2021.06.16.21258691: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:A limitation of the CSSS app is that, owing to limited resources, it is only available in Swedish, which precludes inclusion of non-Swedish speakers, who may be at high risk of COVID-19 infection 30. It is also possible that participants were more likely to join the study and report daily if they experienced symptoms associated with COVID-19 than if they were healthy, potentially inflating COVID-19 prevalence estimates. We sought to …
SciScore for 10.1101/2021.06.16.21258691: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:A limitation of the CSSS app is that, owing to limited resources, it is only available in Swedish, which precludes inclusion of non-Swedish speakers, who may be at high risk of COVID-19 infection 30. It is also possible that participants were more likely to join the study and report daily if they experienced symptoms associated with COVID-19 than if they were healthy, potentially inflating COVID-19 prevalence estimates. We sought to reduce this bias by excluding the first seven days of data collected for each participant. However, the regional ranking of hospital admissions was comparable or slightly less strongly correlated to those based on testing. We observed a peak in app-based COVID-19 prevalence estimates in mid-September 2020 with no corresponding peak in any disease-specific COVID-19 national register data. The prevalence of loss of smell and/or taste, sore throat, and headache were similarly elevated in NOVUS. The FoHM also noted acute respiratory infections symptom reporting at this time 31. Weekly laboratory analyses of respiratory viruses later indicated a high incidence of common colds caused by rhinoviruses in September 2020 32. Hence, the specificity of the CSSS data was compromised when prevalence of other pathogens with similar symptomatology to COVID-19 was elevated. We therefore developed a prediction model, which permitted time-varying coefficients conditional on the PCR test results during a given period. This model yielded results more consistent with t...
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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