Mathematical modeling of the SARS-CoV-2 epidemic in Qatar and its impact on the national response to COVID-19
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Article activity feed
-
-
SciScore for 10.1101/2020.11.08.20184663: (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
Software and Algorithms Sentences Resources Mathematical modeling analyses were conducted in MATLAB R2019a (Boston/MA/USA) [29] whereas statistical analyses were performed in STATA/SE 16.1 (College Station, TX) [30]. MATLABsuggested: (MATLAB, RRID:SCR_001622)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 study has limitations. Model estimates are contingent on the validity and generalizability of input data. …
SciScore for 10.1101/2020.11.08.20184663: (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
Software and Algorithms Sentences Resources Mathematical modeling analyses were conducted in MATLAB R2019a (Boston/MA/USA) [29] whereas statistical analyses were performed in STATA/SE 16.1 (College Station, TX) [30]. MATLABsuggested: (MATLAB, RRID:SCR_001622)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 study has limitations. Model estimates are contingent on the validity and generalizability of input data. Our estimates were based on current SARS-CoV-2 natural history and disease progression parameters, but our understanding of this infection is still evolving. Available input data were most complete at the national level. We did not have sufficient data about social networks of different national subpopulations and patterns of mixing between those subpopulations to factor them into the model. Despite these limitations, our model, tailored to the complexity of the epidemic in Qatar, was able to reproduce observed epidemic trends, and to provide useful and consequential predictions and insights about infection transmission and healthcare needs. Importantly, the modeling estimates successfully influenced the national response. In conclusion, Qatar experienced a large SARS-CoV-2 epidemic, but avoided a burdensome epidemic, such as that unfolding in other counties. Mathematical modeling played an influential role in guiding the national public-health response by characterizing and understanding the epidemic, forecasting healthcare needs, predicting the impact of social and physical distancing restrictions, and rationalizing and justifying the easing of restrictions. While this article illustrates a successful case study, the modeling tools employed here can be adapted and applied in other countries to guide SARS-CoV-2 epidemic control, preparedness for the current or futur...
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.
-
