Coronavirus (COVID-19) Spike in Georgia: An Epidemiologic Study of Data, Modelling, and Policy Implications to Understand the Gender-and Race- Specific Variations
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SciScore for 10.1101/2021.12.09.21267571: (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:As noted in other predictive modeling studies on COVID-19, there are some challenges and limitations of predictive models. Data obtained early on in epidemics may be limited due to under-detection of cases, inconsistent detection of cases, reporting delays, and poor documentation–all of which affect the quality of model output.39 This is …
SciScore for 10.1101/2021.12.09.21267571: (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:As noted in other predictive modeling studies on COVID-19, there are some challenges and limitations of predictive models. Data obtained early on in epidemics may be limited due to under-detection of cases, inconsistent detection of cases, reporting delays, and poor documentation–all of which affect the quality of model output.39 This is particularly true of the COVID-19 pandemic, where numerous cases were asymptomatic, leading to varying hypotheses as to the true prevalence of COVID-19 due to undetected cases.40 Predictive models lead to the possible exclusion of undocumented, unconfirmed cases due to lack of access to medical care, which could then lead to under-representation of some of the most vulnerable populations.41,42 Another limitation may be potential bias due to factors such as the accuracy of diagnostic tests, lasting immunity, reinfections, and population characteristics.39 The limitations of the present study are now stated as follows: he comorbidities of the chronic diseases were unknown because the data were only reflected with yes, no, and unknown; the GDPH did not release any gender specific data for cases and chronic diseases for race/ethnicity; and there were a very limited number of unidentified datasets publicly available. The study findings will help for interventions and develop policy briefs for future of pandemic, and they can be generalized to the population with geographic and gender-and race-specific similarities.
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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