Data and Code for: Optimal Targeted Lockdownsin a Multi-Group SIR Model
Principal Investigator(s): View help for Principal Investigator(s) Daron Acemoglu, MIT; Victor Chernozhukov, MIT; Ivan Werning, MIT; Michael Whinston, MIT
Version: View help for Version V1
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Project Citation:
Acemoglu, Daron, Chernozhukov, Victor, Werning, Ivan, and Whinston, Michael. Data and Code for: Optimal Targeted Lockdownsin a Multi-Group SIR Model. Nashville, TN: American Economic Association [publisher], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-11-23. https://doi.org/10.3886/E130626V1
Project Description
Summary:
View help for Summary
This is the code repository for "Optimal Targeted Lockdowns in a Multi-Group SIR Model", (AER Insights: Revision).
DATA
The fatality parameters used in computation are based on Ferguson, NM, D. Laydon, G. Nedjati-Gilani, N. Imai, K Ainslie, M. Baguelin, S. Bhatia, A. Boonyasiri, Z. Cucunubá, G. Cuomo-Dannenburg, and A. Dighe, “Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand,” March 2020. Imperial College COVID-19 Response Team. As commented in the paper, these parameters are in line with those from South Korea and the Diamond Princess cruise (see Acemoglu D., V. Chernozhukov, I. Werning, and M. D. Whinston, “A multi-risk SIR model with optimally targeted lockdown,” Technical Report, National Bureau of Eco- nomic Research 2020, for details and discussion). The latter data is not used in the paper.
COMPUTATIONAL CODE FILES.
The code files are the Python Notebooks:
Optimal3GPolicy-v6.ipynb produces Figures 3, 4, 5, A1, A3, A4, A5.2.
Optimal3G-SEIR-v6.ipynb produces Figures A6 an A8.3.
Optimal4GPolicy-OldWorking-v6.ipynb produces Figure A2.4.
Optimal3G-CustomContactMatrix.ipynb produces Figure A7.
The code is available under a Creative Commons Non-commercial license.
EXECUTION
We executed the files as follow: We have uploaded these notebooks to Google Colab Cloud https://colab.research.google.com/ and executed them online in the cloud.
To execute the code, the user can follow a similar approach. The user will need to modify the notebook cell that mounts the Google drive and supply her our own default paths for where to store computational output, figures: The path "/content/drive/My Drive/Covid/Lockdown/" has to be modified to user’s working folder. In that folder, the user must create the following subfolders: models/, figs/, summaryres/, results/. This is needed to make sure that the code executes properly. Alternatively, the code can be executed on a local machine with a local Python installation.
ALTERNATIVE SUGGESTED EXECUTION
The Python notebooks can be accessible online directly at:
https://colab.research.google.com/drive/16zhsso-NzNbxn9C_MMP4lqzdVA2N-xxt?usp=sharing https://colab.research.google.com/drive/1Ogi15qeU0vVm1eUq66c3B7M1nPiL-ZtE?usp=sharinghttps://colab.research.google.com/drive/15RYgCU6esEdOfOSA6LK7kRV5BZmQmVD3?usp=sharing https://colab.research.google.com/drive/1AvOOsR_3w-r0eciToP758dLkQRdSVm6H?usp=sharing
They can be cloned and executed directly in Google Colab Cloud.
DATA
The fatality parameters used in computation are based on Ferguson, NM, D. Laydon, G. Nedjati-Gilani, N. Imai, K Ainslie, M. Baguelin, S. Bhatia, A. Boonyasiri, Z. Cucunubá, G. Cuomo-Dannenburg, and A. Dighe, “Impact of non-pharmaceutical interventions (NPIs) to reduce COVID-19 mortality and healthcare demand,” March 2020. Imperial College COVID-19 Response Team. As commented in the paper, these parameters are in line with those from South Korea and the Diamond Princess cruise (see Acemoglu D., V. Chernozhukov, I. Werning, and M. D. Whinston, “A multi-risk SIR model with optimally targeted lockdown,” Technical Report, National Bureau of Eco- nomic Research 2020, for details and discussion). The latter data is not used in the paper.
COMPUTATIONAL CODE FILES.
The code files are the Python Notebooks:
Optimal3GPolicy-v6.ipynb produces Figures 3, 4, 5, A1, A3, A4, A5.2.
Optimal3G-SEIR-v6.ipynb produces Figures A6 an A8.3.
Optimal4GPolicy-OldWorking-v6.ipynb produces Figure A2.4.
Optimal3G-CustomContactMatrix.ipynb produces Figure A7.
The code is available under a Creative Commons Non-commercial license.
EXECUTION
We executed the files as follow: We have uploaded these notebooks to Google Colab Cloud https://colab.research.google.com/ and executed them online in the cloud.
To execute the code, the user can follow a similar approach. The user will need to modify the notebook cell that mounts the Google drive and supply her our own default paths for where to store computational output, figures: The path "/content/drive/My Drive/Covid/Lockdown/" has to be modified to user’s working folder. In that folder, the user must create the following subfolders: models/, figs/, summaryres/, results/. This is needed to make sure that the code executes properly. Alternatively, the code can be executed on a local machine with a local Python installation.
REQUIREMENTS We used Google Colab Cloud https://colab.research.google.com/ with default runtime hardware setting to execute the notebooks. Execution via local installation will require Python 3.6.1 or higher and Gekko Optimization suite (ver 1.1.0; see https://gekko.readthedocs.io/en/latest/ ).
ALTERNATIVE SUGGESTED EXECUTION
The Python notebooks can be accessible online directly at:
https://colab.research.google.com/drive/16zhsso-NzNbxn9C_MMP4lqzdVA2N-xxt?usp=sharing https://colab.research.google.com/drive/1Ogi15qeU0vVm1eUq66c3B7M1nPiL-ZtE?usp=sharinghttps://colab.research.google.com/drive/15RYgCU6esEdOfOSA6LK7kRV5BZmQmVD3?usp=sharing https://colab.research.google.com/drive/1AvOOsR_3w-r0eciToP758dLkQRdSVm6H?usp=sharing
They can be cloned and executed directly in Google Colab Cloud.
Scope of Project
JEL Classification:
View help for JEL Classification
D58 Computable and Other Applied General Equilibrium Models
D58 Computable and Other Applied General Equilibrium Models
Data Type(s):
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program source code
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