https://github.com/gitfrid/pert-py
PERT-py
https://github.com/gitfrid/pert-py
Last synced: 5 months ago
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PERT-py
- Host: GitHub
- URL: https://github.com/gitfrid/pert-py
- Owner: gitfrid
- Created: 2025-03-14T23:11:59.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-31T21:00:43.000Z (about 1 year ago)
- Last Synced: 2025-03-31T22:21:35.533Z (about 1 year ago)
- Language: HTML
- Size: 5.72 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
### PERT-py
Case incidence rate per 1M
[Download Link immunizationdata.who.int](https://immunizationdata.who.int/global/wiise-detail-page/pertussis-reported-cases-and-incidence?GROUP=Countries&YEAR=)
Vac coverage official Numbers Pertussis-containing vaccine 2d Dose
[Download Link immunizationdata.who.int](https://immunizationdata.who.int/global/wiise-detail-page/diphtheria-tetanus-toxoid-and-pertussis-(dtp)-vaccination-coverage?GROUP=Countries&ANTIGEN=DTPCV3&YEAR=&CODE=)
[The recommended case definitions](https://www.who.int/publications/m/item/vaccine-preventable-diseases-surveillance-standards-pertussis)
### Disclaimer:
**The results have not been checked for errors. Neither methodological nor technical checks or data cleansing have been performed.**
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### Dowhy causal impact estimation vax coverage on case incidence rate for differnt countries,
DTP-containing vac 3rd Dose
DoWhy is a Python library for causal inference that allows modeling and testing of causal assumptions, based on a unified language for causal inference.
See the book Models, Reasoning, and Inference by Judea Pearl for deeper insights, that goes far beyond my horizon.
Phyton script [C) PERT.py](https://github.com/gitfrid/PERT-py/blob/main/C%29%20PERT.py) for visualizing the downloaded CSV data
DoWhy Library see: https://github.com/py-why/dowhy
To select or deselect all, double-click on the legend. To select a single legend, click on it once
[Download interactive html](https://github.com/gitfrid/PERT-py/blob/main/C%29%20Dowhy%20causal%20estimate%20on%20mean%20vac%20coverage%20and%20cases%20pertussis%202000-2023.html) 2000-2023
[Years for each country the dowhy estimation is based on](https://github.com/gitfrid/PERT-py/blob/main/C%29%20Dowhy%20causal%20estimate%20on%20mean%20vac%20coverage%20and%20cases%20pertussis%20valid%20years%20for%20dowhy%20calc%202000-2023.txt)
Interpretation of Causal Effect Estimation:
The causal effect estimation gives a numerical value indicating how much the outcome (reported cases per million) changes when the treatment (coverage in percentage) changes by one unit.
Positive causal effect (e.g. 0.5): For each 1% increase in coverage, reported cases expected to increase by 0.5 cases per million.
Negative causal effect (e.g. -0.5): For each 1% increase in vaccination coverage, reported cases are expected to decrease by 0.5 cases per million.
Warning: the results were not checked for confounding factors or lack of causality neither methodological errors
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### Vax coverage vs case incidence rate for differnt countries, DTP-containing vac 3rd Dose
Phyton script [A) PERT.py](https://github.com/gitfrid/PERT-py/blob/main/A%29%20PERT.py) for visualizing the downloaded CSV data
To select or deselect all countries, double-click on the legend. To select a single country, click on it once
[Download interactive html](https://github.com/gitfrid/PERT-py/blob/main/A%29%20PERT%20vaccination_vs_reported_cases%202000-2023.html) 2000-2023
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[Download interactive html](https://github.com/gitfrid/PERT-py/blob/main/A%29%20PERT%20vaccination_vs_reported_cases%201980-2023.html) 1980-2023
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### Vax coverage vs case incidence rate for differnt countries including trend line categories ,DTP-containing vac 3rd Dose 2000-2023:
Rising Coverage and Rising Cases:
Falling Coverage and Falling Cases:
Rising Coverage and Falling Cases:
Falling Coverage and Rising Cases:
Phyton script [B) PERT.py](https://github.com/gitfrid/PERT-py/blob/main/B%29%20PERT.py) for visualizing the downloaded CSV data with trend lines
**Rising Coverage and Rising Cases:**
[Download interactive html](https://github.com/gitfrid/PERT-py/blob/main/B%29%20PERT%20rising%20vac%20coverage%20and%20rising%20cases%20trend%202000-2023.html) 2000-2023
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**Falling Coverage and Falling Cases:**
[Download interactive html](https://github.com/gitfrid/PERT-py/blob/main/B%29%20PERT%20falling%20vac%20coverage%20and%20falling%20trend%202000-2023.html) 2000-2023
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**Rising Coverage and Falling Cases:**
[Download interactive html](https://github.com/gitfrid/PERT-py/blob/main/B%29%20PERT%20rising%20vac%20coverage%20and%20falling%20cases%20trend%202000-2023.html) 2000-2023
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**Falling Coverage and Rising Cases:**
[Download interactive html](https://github.com/gitfrid/PERT-py/blob/main/B%29%20PERT%20falling%20vac%20coverage%20and%20rising%20cases%20trend%202000-2023.html) 2000-2023
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### Vax coverage vs case incidence rate for differnt countries including trend line categories ,
DTP-containing vac 3rd Dose for years 1980-2023:
Warning: In order to compare the trends, DTP-containing vac 1st Dose from 1980 onwards would also have to be taken into account, which are not included here!
Includes Dropdown menu for easy selection:
[Download interactive html](https://github.com/gitfrid/PERT-py/blob/main/D%29%20PERT%20vaccination_vs_reported_cases_dropdown_1980-2023.html) 1980-2023
[Download interactive html](https://github.com/gitfrid/PERT-py/blob/main/D%29%20PERT%20vaccination_vs_reported_cases_dropdown_2000-2023.html) 2000-2023
Download Trends 1980-2023 as interactive HTML-Files from [root directory](https://github.com/gitfrid/PERT-py/tree/main) for visualizing the downloaded CSV data with trend lines
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