CovidRiskResponse

Code & model files for Lim et al. (2023), Why Similar COVID-19 Policies Resulted in Different Outcomes: How Responsiveness and Culture Influenced Mortality Rates

For any questions please contact Tse Yang Lim

Analysis Code

Contains the Python code used for data pre-processing, model estimation, analysis of results, and data visualization, as well as separate Python code for cultural constructs regressions and R code for Figure S2.

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.

Data

Contains:

1. Vensim data files (.vdf) used in model estimation,
2. the input .tab files processed from OurWorldInData based on the code in Analysis Code,
3. an input .tab file showing countries by % of people fully or partially vaccinated within the analysis time horizon, processed from OurWorldInData using the code in Analysis Code,
4. the .xlsx file used to calculate country-specific IFRs based on age distributions (see S2 of the Supplement for explanation),
5. an .RData file drawn from Oxford CGRT used to produce Figure S2 in the Supplement, and
6. a .csv file containing Hofstede's cultural construct values appended to model estimation results.

Results

Contains summary output .tab files from the model estimation presented in the paper and the supplement, as well as .jpg versions of all figures used, which can be reproduced using the code in Analysis Code. Includes full country-by-country parameter estimates accompanying Supplement S4c, as well as other sensitivity analyses in Supplement S4.

Vensim Files

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), as well as sub-model used for illustrative parameter sensitivity analysis.