Evaluating and optimizing COVID-19 vaccination policies: a case study of Sweden
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
We evaluate the efficiency of vaccination scenarios for COVID-19 by analysing a data-driven mathematical model. Healthcare demand and incidence are investigated for different scenarios of transmission and vaccination schemes. Our results suggest that reducing the transmission rate affected by invading virus strains, seasonality and the level of prevention, is most important. Second to this is timely vaccine deliveries and expeditious vaccination management. Postponing vaccination of antibody-positive individuals reduces also the disease burden, and once risk groups have been vaccinated, it is best to continue vaccinating in a descending age order.
Article activity feed
-
SciScore for 10.1101/2021.04.07.21255026: (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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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…
SciScore for 10.1101/2021.04.07.21255026: (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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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.
-