Mathematical modeling of COVID-19 in British Columbia: An age-structured model with time-dependent contact rates
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SciScore for 10.1101/2021.10.19.21265177: (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: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:A related limitation of our framework is that it requires one to specify the future weekly contact rates in order to project future cases. One way to approach this issue would be to use contact rates from previous weeks and assume these will be representative of future contact rates. Such projections would be interpreted as a possible scenario if future contact rates remained similar to historical levels. A distinct benefit of our model is that …
SciScore for 10.1101/2021.10.19.21265177: (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: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:A related limitation of our framework is that it requires one to specify the future weekly contact rates in order to project future cases. One way to approach this issue would be to use contact rates from previous weeks and assume these will be representative of future contact rates. Such projections would be interpreted as a possible scenario if future contact rates remained similar to historical levels. A distinct benefit of our model is that future projections can be made based on clear assumptions about contact rates in the underlying population, as opposed to more challenging assumptions about changes in transmission rates (which could be due to many factors). Our modeling framework is based on deterministic ODE equations, which are well suited to capturing smooth changes in transmission but may not be suitable for sudden fluctuations. The sensitivity to underlying contact rates can be a limitation if contact data are, for any reason (including poor data quality) unstable. Fluctuating contact rates could lead to unrealistic predictions as the model tries to capture these dynamics with a series of small exponential growths and decays. This appears to be the case for our 18-24 age group, which typically had unstable contact rates due to small numbers of survey respondents of this age. We show the importance of including population-based contact rates in modeling of COVID-19. In this study, we used self-reported rates of close contact from the BC-Mix survey which began duri...
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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