Macro-level drivers of SARS-CoV-2 transmission: A data-driven analysis of factors contributing to epidemic growth during the first wave of outbreaks in the United States
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SciScore for 10.1101/2021.06.23.21259394: (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:4.3 Limitations: Some of the limitations of the study are as follows. since the study relied on macrolevel data-driven approaches, it was limited to using aggregated data at the county level. With any analysis dealing with population-level areal data, issues can arise due to individual heterogeneity, which may lead to confounding bias, …
SciScore for 10.1101/2021.06.23.21259394: (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:4.3 Limitations: Some of the limitations of the study are as follows. since the study relied on macrolevel data-driven approaches, it was limited to using aggregated data at the county level. With any analysis dealing with population-level areal data, issues can arise due to individual heterogeneity, which may lead to confounding bias, and it is not possible to draw causal inference due to the likely presence of endogeneity bias (e.g., omitted variable bias or reverse causality). Therefore, results were carefully evaluated from individual-level and clinical-based studies to draw conclusions. The use of further explanatory variables would have surely improved the study i.e. on homelessness, availability of Intensive Care Units (ICU), quality of medical facilities, and ratio of medical staff per person, but these data were not available. It is also important to note that given the unprecedented nature and scale of COVID- 19 outbreaks, data quality issues arise owing to the under-reporting of cases i.e., through under-diagnosis, lack of diagnostic tests and a lack of resources/time to carry out and implement mass testing. If data collection methods remained constant across counties over the time frame of this study, the calculation of doubling times can be a reliable measure. However, doubling times can be inflated by improving testing procedures i.e., better detection and reporting through the availability of better diagnostic tests, better sampling techniques, resource allocat...
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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