https://github.com/gitfrid/mmr-py
MMR-py
https://github.com/gitfrid/mmr-py
Last synced: 5 months ago
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MMR-py
- Host: GitHub
- URL: https://github.com/gitfrid/mmr-py
- Owner: gitfrid
- Created: 2025-03-05T23:22:17.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-31T19:53:41.000Z (about 1 year ago)
- Last Synced: 2025-03-31T20:36:16.873Z (about 1 year ago)
- Language: HTML
- Size: 10.6 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
### MMR-py
**Confirmed reported cases, including those confirmed clinically,
epidemiologically-linked or by laboratory investigation,
EXCEPT for countries that have eliminated. For countries that HAVE eliminated,
cases confirmed clinically should not be included in the sum of total cases!**
Case incidence rate per 1M
[Download Link immunizationdata.who.int](https://immunizationdata.who.int/global/wiise-detail-page/measles-reported-cases-and-incidence?GROUP=Countries&YEAR=)
Vac coverage official Numbers Measles-containing vaccine 2d Dose
[Download Link immunizationdata.who.int](https://immunizationdata.who.int/global/wiise-detail-page/measles-vaccination-coverage?CODE=ISR&ANTIGEN=MCV2&YEAR=)
### 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,
M-containing vac 2nd 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) MMR-2.py](https://github.com/gitfrid/MMR-py/blob/main/C%29%20MMR.py) for visualizing the downloaded CSV data
DoWhy Library see: https://github.com/py-why/dowhy
%20Dowhy%20causal%20estimate%20on%20mean%20vac%20coverage%20and%20cases%202000-2023.png)
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/MMR-py/blob/main/C%29%20Dowhy%20causal%20estimate%20on%20mean%20vac%20coverage%20and%20cases%202000-2023.html) 2000-2023
[Years for each country the dowhy estimation is based on](https://github.com/gitfrid/MMR-py/blob/main/C%29%20Dowhy%20causal%20estimate%20on%20mean%20vac%20coverage%20and%20cases%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) changes when the treatment (coverage) 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, M-containing vac 2nd Dose
Phyton script [A) MMR-2.py](https://github.com/gitfrid/MMR-py/blob/main/A%29%20MMR-2.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/MMR-py/blob/main/vaccination_vs_reported_cases.html) 2000-2023
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[Download interactive html](https://github.com/gitfrid/MMR-py/blob/main/vaccination_vs_reported_cases_1980_2023.html) 1980-2023
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### Vax coverage vs case incidence rate for differnt counties including trend line categories , M-containing vac 2nd 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) MMR.py](https://github.com/gitfrid/MMR-py/blob/main/B%29%20MMR.py) for visualizing the downloaded CSV data with trend lines
**Rising Coverage and Rising Cases:**
[Download interactive html](https://github.com/gitfrid/MMR-py/blob/main/B$29%20MMR%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/MMR-py/blob/main/B%29%20MMR%20falling%20vac%20coverage%20and%20falling%20cases%20trend%202000-2023.html) 2000-2023
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**Rising Coverage and Falling Cases:**
[Download interactive html](https://github.com/gitfrid/MMR-py/blob/main/B%29%20MMR%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/MMR-py/blob/main/B%29%20MMR%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 ,
M-containing vac 2nd Dose for years 1980-2023:
Warning: In order to compare the trends, M-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:
%20MMR%20vaccination_vs_reported_cases_dropdown_1980_2023.png)
[Download interactive html](https://github.com/gitfrid/MMR-py/blob/main/D%29%20MMR%20vaccination_vs_reported_cases_dropdown_1980-2023.html) 1980-2023
[Download interactive html](https://github.com/gitfrid/MMR-py/blob/main/D%29%20MMR%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/MMR-py) for visualizing the downloaded CSV data with trend lines
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