Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application: a prospective, observational study
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Article activity feed
-
SciScore for 10.1101/2020.10.26.20219659: (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
Software and Algorithms Sentences Resources All analysis was done with Python 3.7. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Several limitations to our work must be acknowledged. The app users are not a representative sample of the wider population for which we aim to make an inference. There is a clear shift in age and gender compared to the general population, our …
SciScore for 10.1101/2020.10.26.20219659: (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
Software and Algorithms Sentences Resources All analysis was done with Python 3.7. Pythonsuggested: (IPython, RRID:SCR_001658)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Several limitations to our work must be acknowledged. The app users are not a representative sample of the wider population for which we aim to make an inference. There is a clear shift in age and gender compared to the general population, our users tend to live in less deprived area12, and we have few users reporting from key sites such as care homes and hospitals. We account for some population differences when producing prevalence estimates using specific census adjusted population strata, but the number of invited tests do not allow us to do this when calculating incidence. Differences in reported symptoms across age groups23 would likely lead to different prediction models of COVID-19 positivity, and the model’s performance will vary with the prevalence of other infections with symptoms that overlap with COVID-19, such as flu. Furthermore, the app population is less racially and ethnically diverse than the general population.24. Reliance on user self-reporting can also introduce bias into our results - for instance, users who are very sick may be less likely to report than those with mild symptoms. Other sources of error include collider bias25 arising from a user’s probability of using the app being dependent on their likelihood of having COVID-19, potentially biasing our estimates of incidence and prevalence. We showed a sensitivity analysis that attempts to understand the effect of health-seeking behaviour, but acknowledge there are many other biases that may affect o...
Results from TrialIdentifier: We found the following clinical trial numbers in your paper:
Identifier Status Title NCT04331509 Recruiting COVID-19 Symptom Tracker 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.
-
-
-