%20PERT%20vaccination_vs_reported_cases%201980-2023.png){kind=link}
%20PERT%20vaccination_vs_reported_cases%202000-2023.png){kind=link}
%20PERT%20falling%20vac%20coverage%20and%20falling%20cases%20trend%202000-2023.png){kind=link}
%20PERT%20falling%20vac%20coverage%20and%20rising%20cases%20trend%202000-2023.png){kind=link}
%20PERT%20rising%20vac%20coverage%20and%20falling%20cases%20trend%202000-2023.png){kind=link}
%20PERT%20rising%20vac%20coverage%20and%20rising%20cases%20trend%202000-2023.png){kind=link}
%20Dowhy%20causal%20estimate%20on%20mean%20vac%20coverage%20max(1st%20or%203d%20Dose)%20and%20cases%20Pertussis%201980-2023.png){kind=link}
%20Dowhy%20causal%20estimate%20on%20mean%20vac%20coverage%20max(1st%20or%203d%20Dose)%20and%20cases%20Pertussis%202000-2023.png){kind=link}
%20PERT%20vaccination_vs_reported_cases_dropdown_1980-2023.png){kind=link}
Case incidence rate per 1M
Download Link immunizationdata.who.int
Vac coverage official Numbers Pertussis-containing vaccine 2d Dose
Download Link immunizationdata.who.int
The recommended case definitions
The results have not been checked for errors. Neither methodological nor technical checks or data cleansing have been performed.
Dowhy causal impact estimation vax coverage on case incidence rate for differnt counties,
DTP-containing vac max(1st or 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 for visualizing the downloaded CSV data
DoWhy Library see: https://github.com/py-why/dowhy
%20Dowhy%20causal%20estimate%20on%20mean%20vac%20coverage%20max(1st%20or%203d%20Dose)%20and%20cases%20Pertussis%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 2000-2023
Years for each country the dowhy estimation is based on
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
Phyton script A) PERT.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
%20PERT%20vaccination_vs_reported_cases%202000-2023.png)
Download interactive html 2000-2023
Download interactive html 1980-2023
Vax coverage vs case incidence rate for differnt counties including trend line categories ,DTP-containing vac max(1st or 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 for visualizing the downloaded CSV data with trend lines
Rising Coverage and Rising Cases:
Download interactive html 2000-2023
Falling Coverage and Falling Cases:
%20PERT%20falling%20vac%20coverage%20and%20falling%20cases%20trend%202000-2023.png)
Download interactive html 2000-2023
Rising Coverage and Falling Cases:
Download interactive html 2000-2023
Falling Coverage and Rising Cases:
Download interactive html 2000-2023
Vax coverage vs case incidence rate for differnt countries,
DTP-containing vac max(1st or 3rd Dose) for years 1980-2023:
Phyton script D) PERT.py
Includes Dropdown menu for easy selection:
Download interactive html 1980-2023
Download interactive html 2000-2023
Download as interactive HTML-Files from root directory for visualizing the downloaded CSV data