Identifying novel factors associated with COVID-19 transmission and fatality using the machine learning approach
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SciScore for 10.1101/2020.06.10.20127472: (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:This method overcame the limitation of previous univariate analyses that ignored the interdependence between different factors. Second, we identified novel factors associated with COVID19, including the unitary state governing system as a positive predictor of COVID-19 cases and deaths, blood type B as a protective factor for COVID-19 …
SciScore for 10.1101/2020.06.10.20127472: (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:This method overcame the limitation of previous univariate analyses that ignored the interdependence between different factors. Second, we identified novel factors associated with COVID19, including the unitary state governing system as a positive predictor of COVID-19 cases and deaths, blood type B as a protective factor for COVID-19 risk, the negative associations between the prevalence of HIV, influenza and pneumonia, and chronic lower respiratory diseases and COVID-19 risk, and the positive associations between economic development, education expenditure, obesity, and condition of unimproved water sources and COVID-19 risk. Third, we confirmed some controversial factors associated with COVID-19, including smoking and vitamin D intake as protective factors and blood type A as a risk factor for COVID-19. Finally, this study demonstrates that age, climate, social distancing, economic development, health investment, weight, nutrient intake, influenza, and race are the most prominent factors for COVID-19. This study has several limitations. First, because the capacity for testing for COVID-19 patients varies among different countries, the reported COVID-19 cases may not fully represent the actual situation of COVID-19 outbreaks in some countries that could affect the accuracy of our predictive models. Second, the sample size is not sufficiently large in terms of the number of predictors we used. As a result, the β-coefficients of some variables were small, so that their associ...
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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