Are college campuses superspreaders? A data-driven modeling study
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
Abstract
Article activity feed
-
-
SciScore for 10.1101/2020.12.18.20248490: (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 We apply Bayes’ rule to obtain the posterior distribution of the parameters using the prior distributions from Table 2, and the reported cases themselves, which we infer approximately by employing the NO-U-Turn sampler NUTS (Hoffman & Gelman 2014) implementation of the Python package PyMC3 (Salvatier et al. 2016).
Pythonsuggested: (IPython, RRID:SCR_001658)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 …SciScore for 10.1101/2020.12.18.20248490: (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 We apply Bayes’ rule to obtain the posterior distribution of the parameters using the prior distributions from Table 2, and the reported cases themselves, which we infer approximately by employing the NO-U-Turn sampler NUTS (Hoffman & Gelman 2014) implementation of the Python package PyMC3 (Salvatier et al. 2016).
Pythonsuggested: (IPython, RRID:SCR_001658)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:Limitations: Although we can robustly identify several universal trends, our approach has a few limitations: First, since most colleges report case data on a weekly rather than on a daily basis, the selection of our 30 colleges is likely biased towards institutions that value regular testing and transparent reporting. Second, since the SEIR model is known to perform poorly for low case numbers, we limited our analysis to institutions with more than 100 cases, which may induce an additional implicit bias. Third, since the true on-campus population was often unreported, we approximated the population by the total enrollment, which is likely an overestimate that results in an underestimate of the maximum incidence and recovered fraction. Fourth, instead of accounting for move-in effects through initial conditions, our method collectively represents outbreak dynamics in terms of the time-varying effective reproduction number R(t), which could artificially inflate the reproduction in early fall. Last, although we were able to identify universal trends from 30 individual campuses with campusspecific enrollment, living situation, and type of education, its is virtually impossible to exclude neighborhood outbreak dynamics, county- and state-wide mitigation strategies, national holidays and seasonality. This implies that our observed trends are not necessarily a one-to-one predictor for the COVID-19 dynamics upon campus reopening after the winter break.
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.
-
