COVID-19 Modelling: The Effects of Social Distancing
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Abstract
The purpose of this article is to reach all those who find it difficult to become well informed about the steps that have been implemented to tackle the COVID-19 pandemic and to spark discussion and thought. Here, we use simple stochastic simulations to evaluate different approaches taken to manage the crisis. We then compare these results with updated data of what really happened in the UK and in South Africa. The initial simulations aligned well with how the pandemic has evolved throughout five months following lockdown. The models are, as expected, not fully accurate, but exact enough to be used as a guideline to the evolution of the disease in both high- and middle-income countries. This is shown through simulations formed by an open source code, which allows evaluation of the outcomes from different intervention scenarios or conditions.
Article activity feed
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SciScore for 10.1101/2020.03.29.20046870: (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: We detected the following sentences addressing limitations in the study:Finally, one caveat. This is just a model (and all models are wrong [17]) – further validation and stress testing, including sensitivity analysis of the results to the choice of parameters should be done to understand the limitations of the model predictions.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results …
SciScore for 10.1101/2020.03.29.20046870: (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: We detected the following sentences addressing limitations in the study:Finally, one caveat. This is just a model (and all models are wrong [17]) – further validation and stress testing, including sensitivity analysis of the results to the choice of parameters should be done to understand the limitations of the model predictions.
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.
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SciScore for 10.1101/2020.03.29.20046870: (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
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).
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure …
SciScore for 10.1101/2020.03.29.20046870: (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
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).
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, please follow this link.
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