{"id":26905623,"url":"https://github.com/gitfrid/mmr-py","last_synced_at":"2026-01-08T20:43:50.172Z","repository":{"id":280908310,"uuid":"943569645","full_name":"gitfrid/MMR-py","owner":"gitfrid","description":"MMR-py","archived":false,"fork":false,"pushed_at":"2025-03-31T19:53:41.000Z","size":11144,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-31T20:36:16.873Z","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}},"created_at":"2025-03-05T23:22:17.000Z","updated_at":"2025-03-31T19:53:45.000Z","dependencies_parsed_at":"2025-03-31T20:41:28.377Z","dependency_job_id":null,"html_url":"https://github.com/gitfrid/MMR-py","commit_stats":null,"previous_names":["gitfrid/mmr-py"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gitfrid%2FMMR-py","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gitfrid%2FMMR-py/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gitfrid%2FMMR-py/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/gitfrid%2FMMR-py/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/gitfrid","download_url":"https://codeload.github.com/gitfrid/MMR-py/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246628239,"owners_count":20808106,"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":"2025-04-01T10:58:20.608Z","updated_at":"2026-01-08T20:43:45.139Z","avatar_url":"https://github.com/gitfrid.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"### MMR-py \n\u003cbr\u003e\n\u003cbr\u003e\n\n**\u003csmall\u003eConfirmed reported cases, including those confirmed clinically,\nepidemiologically-linked or by laboratory investigation,\nEXCEPT for countries that have eliminated. For countries that HAVE eliminated,\ncases confirmed clinically should not be included in the sum of total cases!\u003c/small\u003e**\n\nCase incidence rate per 1M\n[Download Link immunizationdata.who.int](https://immunizationdata.who.int/global/wiise-detail-page/measles-reported-cases-and-incidence?GROUP=Countries\u0026YEAR=)\n\u003cbr\u003eVac coverage official Numbers Measles-containing vaccine 2d Dose\n[Download Link immunizationdata.who.int](https://immunizationdata.who.int/global/wiise-detail-page/measles-vaccination-coverage?CODE=ISR\u0026ANTIGEN=MCV2\u0026YEAR=)\n\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\u003eM-containing vac 2nd 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) MMR-2.py](https://github.com/gitfrid/MMR-py/blob/main/C%29%20MMR.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/MMR-py/blob/main/C)%20Dowhy%20causal%20estimate%20on%20mean%20vac%20coverage%20and%20cases%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/MMR-py/blob/main/C%29%20Dowhy%20causal%20estimate%20on%20mean%20vac%20coverage%20and%20cases%202000-2023.html) 2000-2023\n\u003cbr\u003e[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)\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) changes when the treatment (coverage) 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, M-containing vac 2nd Dose\n\nPhyton script [A) MMR-2.py](https://github.com/gitfrid/MMR-py/blob/main/A%29%20MMR-2.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/MMR-py/blob/main/Plot%20Screenshot.png width=\"1280\" height=\"auto\"\u003e\n\u003cbr\u003e\n\u003cbr\u003e\n[Download interactive html](https://github.com/gitfrid/MMR-py/blob/main/vaccination_vs_reported_cases.html) 2000-2023\n\u003cbr\u003e\n_________________________________________\n\n\u003cbr\u003e\n\u003cimg src=https://github.com/gitfrid/MMR-py/blob/main/vaccination_vs_reported_cases_1980_2023.png width=\"1280\" height=\"auto\"\u003e\n\u003cbr\u003e\n\n[Download interactive html](https://github.com/gitfrid/MMR-py/blob/main/vaccination_vs_reported_cases_1980_2023.html) 1980-2023\n\u003cbr\u003e\n\u003cbr\u003e\n_________________________________________\n\u003cbr\u003e\n\n### Vax coverage vs case incidence rate for differnt counties including trend line categories , M-containing vac 2nd 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) MMR.py](https://github.com/gitfrid/MMR-py/blob/main/B%29%20MMR.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/MMR-py/blob/main/B%29%20MMR%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/MMR-py/blob/main/B$29%20MMR%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/MMR-py/blob/main/B%29%20MMR%20falling%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/MMR-py/blob/main/B%29%20MMR%20falling%20vac%20coverage%20and%20falling%20cases%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/MMR-py/blob/main/B%29%20MMR%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/MMR-py/blob/main/B%29%20MMR%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/MMR-py/blob/main/B%29%20MMR%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/MMR-py/blob/main/B%29%20MMR%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\u003eM-containing vac 2nd Dose for years 1980-2023:\n\nWarning: 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!\n\u003cbr\u003e\n\u003cbr\u003eIncludes Dropdown menu for easy selection: \n\u003cbr\u003e\n\u003cimg src=https://github.com/gitfrid/MMR-py/blob/main/D)%20MMR%20vaccination_vs_reported_cases_dropdown_1980_2023.png width=\"1280\" height=\"auto\"\u003e\n\u003cbr\u003e\n[Download interactive html](https://github.com/gitfrid/MMR-py/blob/main/D%29%20MMR%20vaccination_vs_reported_cases_dropdown_1980-2023.html) 1980-2023\n[Download interactive html](https://github.com/gitfrid/MMR-py/blob/main/D%29%20MMR%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/MMR-py) 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%2Fmmr-py","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgitfrid%2Fmmr-py","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgitfrid%2Fmmr-py/lists"}