{"id":26905620,"url":"https://github.com/gitfrid/pert-py","last_synced_at":"2026-02-02T17:05:51.169Z","repository":{"id":282548965,"uuid":"948762915","full_name":"gitfrid/PERT-py","owner":"gitfrid","description":"PERT-py","archived":false,"fork":false,"pushed_at":"2025-03-31T21:00:43.000Z","size":5996,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-31T22:21:35.533Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/gitfrid.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-03-14T23:11:59.000Z","updated_at":"2025-03-31T21:00:46.000Z","dependencies_parsed_at":"2025-03-15T11:38:44.994Z","dependency_job_id":"b7f1e988-717b-484c-8729-e12c8f4e2fc5","html_url":"https://github.com/gitfrid/PERT-py","commit_stats":null,"previous_names":["gitfrid/pert-py"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/gitfrid/PERT-py","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gitfrid%2FPERT-py","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gitfrid%2FPERT-py/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gitfrid%2FPERT-py/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gitfrid%2FPERT-py/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/gitfrid","download_url":"https://codeload.github.com/gitfrid/PERT-py/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gitfrid%2FPERT-py/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259402052,"owners_count":22851868,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-04-01T10:58:20.005Z","updated_at":"2026-02-02T17:05:46.149Z","avatar_url":"https://github.com/gitfrid.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"### PERT-py \n\u003cbr\u003e\n\u003cbr\u003e\n\nCase incidence rate per 1M\n[Download Link immunizationdata.who.int](https://immunizationdata.who.int/global/wiise-detail-page/pertussis-reported-cases-and-incidence?GROUP=Countries\u0026YEAR=)\n\u003cbr\u003eVac coverage official Numbers Pertussis-containing vaccine 2d Dose\n[Download Link immunizationdata.who.int](https://immunizationdata.who.int/global/wiise-detail-page/diphtheria-tetanus-toxoid-and-pertussis-(dtp)-vaccination-coverage?GROUP=Countries\u0026ANTIGEN=DTPCV3\u0026YEAR=\u0026CODE=)\n\u003cbr\u003e[The recommended case definitions](https://www.who.int/publications/m/item/vaccine-preventable-diseases-surveillance-standards-pertussis)\n### Disclaimer:\n**The results have not been checked for errors. Neither methodological nor technical checks or data cleansing have been performed.**\n_________________________________________\n\n### Dowhy causal impact estimation vax coverage on case incidence rate for differnt countries, \u003cbr\u003eDTP-containing vac 3rd Dose\n\n\u003cbr\u003e\n\u003cp\u003eDoWhy is a Python library for causal inference that allows modeling and testing of causal assumptions, based on a unified language for causal inference.\n\u003cstrong\u003eSee the book \u003cem\u003eModels, Reasoning, and Inference\u003c/em\u003e by Judea Pearl for deeper insights, that goes far beyond my horizon.\u003c/strong\u003e\u003c/p\u003e\n\u003cbr\u003e\n\nPhyton script [C) PERT.py](https://github.com/gitfrid/PERT-py/blob/main/C%29%20PERT.py) for visualizing the downloaded CSV data\n\u003cbr\u003eDoWhy Library see: https://github.com/py-why/dowhy\n\n\u003cbr\u003e\n\u003cimg src=https://github.com/gitfrid/PERT-py/blob/main/C%29%20Dowhy%20causal%20estimate%20on%20mean%20vac%20coverage%20and%20cases%20pertussis%202000-2023.png width=\"1280\" height=\"auto\"\u003e\n\u003cbr\u003e\nTo select or deselect all, double-click on the legend. To select a single legend, click on it once\n\u003cbr\u003e\n\n\u003cbr\u003e[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\n\u003cbr\u003e[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)\n\u003cbr\u003e\n\u003cbr\u003e\n\nInterpretation of Causal Effect Estimation:\n\nThe 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.\n\n    Positive causal effect (e.g. 0.5): For each 1% increase in coverage, reported cases expected to increase by 0.5 cases per million.\n    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.\n    Warning: the results were not checked for confounding factors or lack of causality neither methodological errors\n\n_________________________________________\n\u003cbr\u003e\n\n### Vax coverage vs case incidence rate for differnt countries, DTP-containing vac 3rd Dose\n\nPhyton script [A) PERT.py](https://github.com/gitfrid/PERT-py/blob/main/A%29%20PERT.py) for visualizing the downloaded CSV data\n\n\nTo select or deselect all countries, double-click on the legend. To select a single country, click on it once\n\u003cbr\u003e\n\u003cimg src=https://github.com/gitfrid/PERT-py/blob/main/A%29%20PERT%20vaccination_vs_reported_cases%202000-2023.png width=\"1280\" height=\"auto\"\u003e\n\u003cbr\u003e\n\u003cbr\u003e\n[Download interactive html](https://github.com/gitfrid/PERT-py/blob/main/A%29%20PERT%20vaccination_vs_reported_cases%202000-2023.html) 2000-2023\n\u003cbr\u003e\n_________________________________________\n\n\u003cbr\u003e\n\u003cimg src=https://github.com/gitfrid/PERT-py/blob/main/A%29%20PERT%20vaccination_vs_reported_cases%201980-2023.png width=\"1280\" height=\"auto\"\u003e\n\u003cbr\u003e\n\n[Download interactive html](https://github.com/gitfrid/PERT-py/blob/main/A%29%20PERT%20vaccination_vs_reported_cases%201980-2023.html) 1980-2023\n\u003cbr\u003e\n\u003cbr\u003e\n_________________________________________\n\u003cbr\u003e\n\n### Vax coverage vs case incidence rate for differnt countries including trend line categories ,DTP-containing vac 3rd Dose 2000-2023:\n    Rising Coverage and Rising Cases:\n    Falling Coverage and Falling Cases:\n    Rising Coverage and Falling Cases:\n    Falling Coverage and Rising Cases:\n\n\u003cbr\u003e\n\nPhyton 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 \n\u003cbr\u003e\n\n\n**Rising Coverage and Rising Cases:**\n\u003cbr\u003e\n\u003cimg src=https://github.com/gitfrid/PERT-py/blob/main/B%29%20PERT%20rising%20vac%20coverage%20and%20rising%20cases%20trend%202000-2023.png width=\"1280\" height=\"auto\"\u003e\n\u003cbr\u003e\n\n[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\n\u003cbr\u003e\n_________________________________________\n\n**Falling Coverage and Falling Cases:**\n\u003cbr\u003e\n\u003cimg src=https://github.com/gitfrid/PERT-py/blob/main/B%29%20PERT%20falling%20vac%20coverage%20and%20falling%20trend%202000-2023.png width=\"1280\" height=\"auto\"\u003e\n\u003cbr\u003e\n\n[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\n\u003cbr\u003e\n\n_________________________________________\n\n**Rising Coverage and Falling Cases:**\n\u003cbr\u003e\n\u003cimg src=https://github.com/gitfrid/PERT-py/blob/main/B%29%20PERT%20rising%20vac%20coverage%20and%20falling%20cases%20trend%202000-2023.png width=\"1280\" height=\"auto\"\u003e\n\u003cbr\u003e\n\n[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\n\u003cbr\u003e\n\n_________________________________________\n\n**Falling Coverage and Rising Cases:**\n\u003cbr\u003e\n\u003cimg src=https://github.com/gitfrid/PERT-py/blob/main/B%29%20PERT%20falling%20vac%20coverage%20and%20rising%20cases%20trend%202000-2023.png width=\"1280\" height=\"auto\"\u003e\n\u003cbr\u003e\n\n[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\n\u003cbr\u003e\n_________________________________________\n\u003cbr\u003e\n\n### Vax coverage vs case incidence rate for differnt countries including trend line categories , \u003cbr\u003eDTP-containing vac 3rd Dose for years 1980-2023:\n\nWarning: 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!\n\u003cbr\u003e\n\u003cbr\u003eIncludes Dropdown menu for easy selection: \n\u003cbr\u003e\n\u003cimg src=https://github.com/gitfrid/PERT-py/blob/main/D%29%20PERT%20vaccination_vs_reported_cases_dropdown_1980_2023.png width=\"1280\" height=\"auto\"\u003e\n\u003cbr\u003e\n[Download interactive html](https://github.com/gitfrid/PERT-py/blob/main/D%29%20PERT%20vaccination_vs_reported_cases_dropdown_1980-2023.html) 1980-2023\n[Download interactive html](https://github.com/gitfrid/PERT-py/blob/main/D%29%20PERT%20vaccination_vs_reported_cases_dropdown_2000-2023.html) 2000-2023\n\u003cbr\u003eDownload 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 \n\u003cbr\u003e\n_________________________________________\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgitfrid%2Fpert-py","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgitfrid%2Fpert-py","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgitfrid%2Fpert-py/lists"}