Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study
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SciScore for 10.1101/2021.09.28.21264240: (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:Our study has several limitations. First, our definitions of contact may miss certain transmission routes, e.g., connections via healthcare workers35,36, indirect transmission over surfaces, undetected patient carriage37, or non-room/ward/building contact. Second, since our training and testing period occurred largely prior to the UK’s …
SciScore for 10.1101/2021.09.28.21264240: (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:Our study has several limitations. First, our definitions of contact may miss certain transmission routes, e.g., connections via healthcare workers35,36, indirect transmission over surfaces, undetected patient carriage37, or non-room/ward/building contact. Second, since our training and testing period occurred largely prior to the UK’s vaccination rollout, we were unable to include vaccination status as a patient variable. However, the emergence of new variants and lack of complete vaccine coverage38 means that the assumption of susceptibility is still relevant, as demonstrated by our post-surge endemic data. Third, we did not include recovered COVID-19 patients in the HOCI prediction dataset because of limited cases and uncertainty regarding future susceptibility. Fourth, our data did not include indoor ventilation and room volume, which are recognised as contributory risks to COVID-19 transmission39. However, even without explicitly accounting for ventilation, our models still proved highly predictive. Finally, in response to the pandemic, various aspects of hospital organisation were altered, including changes in screening practice, personal protective equipment, or placing of ward beds, which were not explicitly encoded as model variables. Our study highlights the predictive power that can be mined from networks of patient contacts to aid with personalised predictions of infectious disease in healthcare settings. The current study applies to respiratory virus transmission...
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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