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ImperialCollegeLondon/covid19model: Nature, 2020 https://www.nature.com/articles/s41586-020-2405-7

Authors: Swapnil Mishra; Fabian Valka;

ImperialCollegeLondon/covid19model: Nature, 2020 https://www.nature.com/articles/s41586-020-2405-7

Abstract

This code is the exact code that was used in Flaxman, Mishra, Gandy et al. "Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe," Nature, 2020. https://www.nature.com/articles/s41586-020-2405-7 To run the code from the main folder in Rstudio source("base-nature.r") or from the command line Rscript base-nature.r. The code should be run in full mode to obtain results---debug mode is only to check that your environment has the required libraries; results will not be reliable as the MCMC chains will not have converged. The repository with posterior draws of the model in Flaxman, Mishra, Gandy et al. "Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe," Nature, 2020. https://www.nature.com/articles/s41586-020-2405-7 is here. This code doesn't supersede our earlier model, it is here for everyone to have direct access to code used in Flaxman, Mishra, Gandy et al. "Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe," Nature, 2020.https://www.nature.com/articles/s41586-020-2405-7. The instructions for European, Italy, Brazil, and USA code are the same as earlier (Look at version 3, version 4, version 5, version 6).

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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