The demographic-scaling-model is designed to estimate in a straightforward manner the total number and prevalence of COVID-19 infections for countries worldwide.
Publication:
Bohk-Ewald, C., Dudel, C., and M. Myrskylä (2020). A demographic scaling model for estimating the total number of COVID-19 infections. This article has been accepted for publication in the International Journal of Epidemiology, dyaa198, https://doi.org/10.1093/ije/dyaa198, published by Oxford University Press. A preprint is available on medRxiv, arXiv.org, and OSF.
We provide here the Latest results as well as How to run the R source code to generate them.
For the ten countries with most reported deaths as of July 23, 2020:
The code needs to be executed in four steps, as defined by step-*.R files in the root directory.
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The
info-input-data.txtfile contains information about required input data. -
Due to copyrights you will need to download and save input data yourself. Original file names and URLs where to download them are given in the
info-input-data.txtfile. -
Input data include confirmed cases and reported deaths attributable to COVID-19 of JHU CSSE (2020), population counts and abridged life tables of UNWPP 2019, infection fatality rates by 10-year age groups as, for example, published in Verity et al. (2020), and global age distribution of COVID-19 deaths as, for example, calculated based on data of Dudel et al. (2020).
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Make sure you have set the correct working directories before you start.
Run the step-*.R scripts in the root directory in the prescribed order.
If you use this code for academic research, please cite this GitHub repository as well as the paper noted above.
Please note that this source code is an academic project. We welcome any issues and pull requests.
The source code of demographic-scaling-model is published under the GNU General Public License version 3.