Real-time spatial health surveillance: Mapping the UK COVID-19 epidemic
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SciScore for 10.1101/2020.08.17.20175117: (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: 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:Also, by recognising and exploiting spatial correlation in the underlying prevalence surface, geospatial statistical methods can deliver substantially more precise estimation of local prevalence than classical methods that implicitly assume independence of outcomes in different spatial units [23] We acknowledge the limitations that come …
SciScore for 10.1101/2020.08.17.20175117: (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: 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:Also, by recognising and exploiting spatial correlation in the underlying prevalence surface, geospatial statistical methods can deliver substantially more precise estimation of local prevalence than classical methods that implicitly assume independence of outcomes in different spatial units [23] We acknowledge the limitations that come with using self-reported symptom data from an app used voluntarily. Firstly, confirmation that self-reported symptoms did indeed represent COVID-19 disease was not possible at a UK level, although the multi-symptom algorithm utilised was generated using predictive regression modelling comparing symptoms to self-reported reverse transcription polymerase chain reaction SARS-CoV-2 test results [16]. Secondly, the individuals included in the studied population are not a random sample of the UK population, potentially presenting a source of collider bias due to the link between age and app usage[24], nor are they necessarily representative with respect to other factors that are either known, or thought likely, to affect susceptibility; for example, gender or ethnicity. The inclusion in the model of LSOA-level covariate information is a potential route to controlling for these at LSOA level, although not at individual level. For example, in the current COVID-19 context information on the age distribution of app users would allow adjustment for the potentially non-representative sub-population of active app users. For environmentally driven health ou...
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.
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