ScreenIT
The Automated Screening Working Groups is a group of software engineers and biologists passionate about improving scientific manuscripts on a large scale. Our members have created tools that check for common problems in scientific manuscripts, including information needed to improve transparency and reproducibility. We have combined our tools into a single pipeline, called ScreenIT. We're currently using our tools to screen COVID preprints.
Latest preprint reviews
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The coronavirus disease ( COVID ‐19) pandemic: simulation‐based assessment of outbreak responses and postpeak strategies
This article has 1 author:Reviewed by ScreenIT
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Sequential Data Assimilation of the Stochastic SEIR Epidemic Model for Regional COVID-19 Dynamics
This article has 4 authors:Reviewed by ScreenIT
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Quantifying population contact patterns in the United States during the COVID-19 pandemic
This article has 2 authors:Reviewed by ScreenIT
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Association between ABO blood groups and clinical outcome of coronavirus disease 2019: Evidence from two cohorts
This article has 15 authors:Reviewed by ScreenIT
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Artificial intelligence–enabled rapid diagnosis of patients with COVID-19
This article has 28 authors:Reviewed by ScreenIT
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A simple method to estimate flow restriction for dual ventilation of dissimilar patients: The BathRC model
This article has 9 authors:Reviewed by ScreenIT
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Estimating the fraction of unreported infections in epidemics with a known epicenter: An application to COVID-19
This article has 3 authors:Reviewed by ScreenIT
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Estimates of regional infectivity of COVID-19 in the United Kingdom following imposition of social distancing measures
This article has 8 authors:Reviewed by ScreenIT
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A COVID-19 Risk Assessment for the US Labor Force
This article has 7 authors:Reviewed by ScreenIT
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Markovian Random Walk Modeling and Visualization of the Epidemic Spread of COVID-19
This article has 2 authors:Reviewed by ScreenIT