Code & model files for Rahmandad, Lim & Sterman (2020), Estimating COVID-19 under-reporting across 86 nations: implications for projections and control (formerly: Estimating the global spread of COVID-19)

For any questions please contact Tse Yang Lim

Contains the Python code used for data pre-processing and model estimation, in .ipynb and .py formats.

Important: The model estimation code is intended to work with an experimental parallelised Vensim engine. With appropriate modifications to the main function calls (but not the analytical procedure), the same analysis can be run on regular commercially available Vensim DSS, though it will take much longer. Please contact Tom Fiddaman for information on the experimental Vensim engine.

Contains Vensim data files (.vdf) used in model estimation, as well as the raw .csv files assembled from various sources (JHU CSSE, OWID, World Bank, etc.) that are fed into the data pre-processing algorithm.

Archived version of repo containing files used in the pre-print version of the paper (SSRN, MedRxiv), released 24 June 2020.

Contains output files from the model estimation presented in the paper, as well as Matlab code used for graphing of results, and output files from various robustness and sensitivity analyses (including additional sensitivity analysis results accompanying Supplement S7).

Contains the main Vensim model file (.mdl) and other supplementary Vensim files used for model estimation (e.g. optimization control, payoff definition, savelist files, and so on). Also includes two sub-models used for further analysis.