Distinctive trajectories of the COVID-19 epidemic by age and gender: A retrospective modeling of the epidemic in South Korea
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Abstract
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SciScore for 10.1101/2020.05.27.20114819: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There were some limitations in this study. First, our study excluded cases from the city of Daegu (over 6000 cases) because detail information about cases from that city was not released to the public. Although it was unlikely to bias our results, information from such a large outbreak could provide some additional insights …
SciScore for 10.1101/2020.05.27.20114819: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your code and data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:There were some limitations in this study. First, our study excluded cases from the city of Daegu (over 6000 cases) because detail information about cases from that city was not released to the public. Although it was unlikely to bias our results, information from such a large outbreak could provide some additional insights on how the epidemic unfolded among people of different age and gender. However, during the early stage of epidemic, little gender and age stratified data were publicly available, and most individual level data from other regions were incomplete as well. Second, we employed statistical methods to examine the trajectories of epidemic. There were two perspectives to model the epidemic process (Hethcote 2000; Unkel et al. 2012). One common approach was to model the process based on the mechanisms of the epidemic. For example, the Susceptible-Exposed-Infectious-Removed (SEIR) model and its variants had been used to assess the dynamic of epidemic, obtain epidemic parameters, and evaluate the impact of various control measures on the epidemic (Kucharski et al. 2020; Peak et al. 2017; Prem et al. 2020; Yu 2020b). Agent-based models were also used to simulate the epidemic process and assess the effects of various interventions (Ferguson et al. 2020; Wu et al. 2020). The other perspective was based on traditional statistical models. Non-linear models such as generalized logistic growth model (Chowell 2017) were used to model the growth of the epidemic and estimate t...
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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