Input description (Google slides)
Status tracking sheet (Google sheets)
Download the country boundaries shapefile from HDX and place in Inputs/$COUNTRY_ISO3/Shapefile/. Unzip the contents
into a directory with the same name as the shapefile, and add this name to the config file.
Commit the shapefile to the repository.
To run, execute:
python Generate_SADD_exposure_from_tiff.py -dThe -d flag is for downloading the WolrdPop file the first time you run.
Make sure you have run the exposure script for the country.
Check GHS for the grid square numbers that cover the country and add these to the config file.
Download food security data from IPC. Select the country and only data from 2020, save the excel file to 'Inputs/$COUNTRY_ISO3/IPC'. Add the filename to the config file, and commit the excel file to the repository.
Add solid fuels, raised blood pressure, and diabetes data to the config file, if available.
To run, execute:
python Generate_vulnerability_file.py -dThe -d flag is for downloading and mosaicing the GHS data the first time you run.
Urban / rural data is taken from GHS. We use the GHS-SMOD raster at 1 km resolution to determine which cells within a province are urban vs rural. The description of the classifcations can be found here. We take anything denser than suburban (class 21 or above) to be urban, and the rest to be rural. For more information about the definition of urban vs rural settlements, see here.
Then we use the GHS-POP raster to calculate the number of people per urban or rural cell, and compute the fraction of the population residing in urban cells.
Contact matrices are extracted from this paper on PLOS Computational Biology. The contact matrices used are taken from the all_locations dataset which is available in two different files: MUestimates_all_locations_1.xlsx (containing data for Algeria to Morocco) and MUestimates_all_locations_2.xlsx (containing data for Mozambique to Zimbabwe).
For some of the countries the contact matrix is not directly available and we are usign a country in the same region and with similat socioeconomic indicators as proxy, as also done by LSHTM. Specifically:
| Country | Contact Matrix used |
|---|---|
| Afghanistan | Pakistan |
| Sudan | Ethiopia |
| South Sudan | Uganda |
| DRC | Zambia |
| Somalia | Kenya |
| Haiti | available on PLOS |
The contact matrices are categorised into 5-year age intervals {1,2,...,16} which don't correspond exactly to the age classes in WorldPop. Classes are matched in the following way:
| WorlpPop Class | Contact Matrix class |
|---|---|
| class 0 (0 to 1) | class X1 (0 to 4) |
| class 1 (1 to 4) | class X1 (0 to 4) |
| class 5 (5 to 9) | class X2 (5 to 9) |
| class 10 (10 to 14) | class X3 (10 to 14) |
| class 15 (15 to 19) | class X4 (15 to 19) |
| class 20 (20 to 24) | class X5 (20 to 24) |
| class 25 (25 to 29) | class X6 (25 to 29) |
| class 30 (30 to 34) | class X7 (30 to 34) |
| class 35 (35 to 39) | class X8 (35 to 39) |
| class 40 (40 to 44) | class X9 (40 to 44) |
| class 45 (45 to 49) | class X10 (45 to 49) |
| class 50 (50 to 54) | class X11 (50 to 54) |
| class 55 (55 to 59) | class X12 (55 to 59) |
| class 60 (60 to 64) | class X13 (60 to 64) |
| class 65 (65 to 69) | class X14 (65 to 69) |
| class 70 (70 to 74) | class X15 (70 to 74) |
| class 75 (70 to 79) | class X16 (75+) |
| class 80 (80+) | class X16 (75+) |
The graph collects the COVID-19 case data, mobility data, contact matrix, population data, and vulnerability data into a single file.
To run, execute:
python Generate_graph.py -m [path to mobility csv] [Country ISO code]The -m flag points at mobility data for that country located in this repository.