{
  "_id": "6a1e7e781d7bb097a0a65c13",
  "Package": "smidm",
  "Title": "Statistical Modelling for Infectious Disease Management",
  "Version": "1.0",
  "Authors@R": "c(\nperson(\"Max\", \"Westphal\", role = c(\"aut\"),\nemail = \"max.westphal@mevis.fraunhofer.de\",\ncomment = c(ORCID = \"0000-0002-8488-758X\")),\nperson(\"Stefanie\", \"Grimm\", role = c(\"aut\"),\nemail = \"stefanie.grimm@itwm.fraunhofer.de\"),\nperson(\"Sonja\", \"Jäckle\", role = c(\"aut\", \"cre\"),\nemail = \"sonja.jaeckle@mevis.fraunhofer.de\",\ncomment = c(ORCID = \"0000-0002-2908-299X\")),\nperson(\"Rieke\", \"Alpers\", role = c(\"aut\"),\nemail = \"rieke.alpers@mevis.fraunhofer.de\",\ncomment = c(ORCID = \"0000-0001-8317-1435\")),\nperson(c(\"Hong\", \"Phuc\"), \"Truong\", role = c(\"aut\"),\nemail = \"hong.phuc.truong@itwm.fraunhofer.de\"),\nperson(\"Amelie\", \"Lucker\", role = c(\"ctb\"),\nemail = \"amelie.lucker@itwm.fraunhofer.de\"),\nperson(\"Fraunhofer MEVIS\", role = c(\"cph\")),\nperson(\"Fraunhofer ITWM\", role = c(\"cph\")))",
  "Description": "Statistical models for specific coronavirus disease 2019\nuse cases at German local health authorities. All models of\nStatistical modelling for infectious disease management 'smidm'\nare part of the decision support toolkit in the 'EsteR'\nproject. More information is published in Sonja Jäckle, Rieke\nAlpers, Lisa Kühne, Jakob Schumacher, Benjamin Geisler, Max\nWestphal \"'EsteR' – A Digital Toolkit for COVID-19 Decision\nSupport in Local Health Authorities\" (2022)\n<doi:10.3233/SHTI220799> and Sonja Jäckle, Elias Röger, Volker\nDicken, Benjamin Geisler, Jakob Schumacher, Max Westphal \"A\nStatistical Model to Assess Risk for Supporting COVID-19\nQuarantine Decisions\" (2021) <doi:10.3390/ijerph18179166>.",
  "License": "BSD_3_clause + file LICENSE",
  "Encoding": "UTF-8",
  "RoxygenNote": "7.2.1",
  "Config/testthat/edition": "3",
  "VignetteBuilder": "knitr",
  "URL": "https://gitlab.cc-asp.fraunhofer.de/ester/smidm",
  "BugReports": "https://gitlab.cc-asp.fraunhofer.de/ester/smidm/-/issues",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-02 06:52:59 UTC",
    "User": "root"
  },
  "Author": "Max Westphal [aut] (<https://orcid.org/0000-0002-8488-758X>),\nStefanie Grimm [aut], Sonja Jäckle [aut, cre]\n(<https://orcid.org/0000-0002-2908-299X>), Rieke Alpers [aut]\n(<https://orcid.org/0000-0001-8317-1435>), Hong Phuc Truong\n[aut], Amelie Lucker [ctb], Fraunhofer MEVIS [cph], Fraunhofer\nITWM [cph]",
  "Maintainer": "Sonja Jäckle <sonja.jaeckle@mevis.fraunhofer.de>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2022-08-27 07:40:02 UTC",
  "RemoteUrl": "https://github.com/cran/smidm",
  "RemoteRef": "HEAD",
  "RemoteSha": "55c28e6f418f51667715b4e60a4a0a4330c17171",
  "MD5sum": "654914ceae2652c11e685b57a61cdbf1",
  "_user": "cran",
  "_type": "src",
  "_file": "smidm_1.0.tar.gz",
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  "_created": "2026-06-02T06:52:59.000Z",
  "_published": "2026-06-02T06:55:52.102Z",
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  "_status": "success",
  "_host": "GitHub-Actions",
  "_upstream": "https://github.com/cran/smidm",
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    "author": "Sonja Jäckle <sonja.jaeckle@mevis.fraunhofer.de>",
    "committer": "cran-robot <csardi.gabor+cran@gmail.com>",
    "message": "version 1.0\n",
    "time": 1661586002
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  "_maintainer": {
    "name": "Sonja Jäckle",
    "email": "sonja.jaeckle@mevis.fraunhofer.de",
    "orcid": "0000-0002-2908-299X"
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  "_owner": "cran",
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  "_updates": [],
  "_tags": [],
  "_stars": 0,
  "_userbio": {
    "uuid": 6899542,
    "type": "organization",
    "name": "cran",
    "description": "Unofficial read-only mirror of all CRAN R packages"
  },
  "_downloads": {
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/smidm"
  },
  "_searchresults": 11,
  "_rbuild": "4.6.0",
  "_assets": [
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    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/readme.html",
    "extra/readme.md",
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    "manual.pdf"
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  "_cranurl": false,
  "_releases": [
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      "version": "1.0",
      "date": "2022-08-27"
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  "_exports": [
    "calculate_likelihood_negative_tests",
    "calculate_posterior_no_infections",
    "calculate_prior_infections",
    "get_expected_total_infections",
    "get_incubation_day_distribution",
    "get_infection_density",
    "get_infectiousness_density",
    "get_misc_infection_density",
    "get_serial_interval_density",
    "get_test_sensitivities",
    "predict_future_infections"
  ],
  "_help": [
    {
      "page": "calculate_likelihood_negative_tests",
      "title": "Overall likelihood",
      "topics": [
        "calculate_likelihood_negative_tests"
      ]
    },
    {
      "page": "calculate_likelihood_negative_tests_k",
      "title": "Likelihood K",
      "topics": [
        "calculate_likelihood_negative_tests_k"
      ]
    },
    {
      "page": "calculate_posterior_no_infections",
      "title": "Negative analysis probability",
      "topics": [
        "calculate_posterior_no_infections"
      ]
    },
    {
      "page": "calculate_prior_infections",
      "title": "A priori probability of further Infections",
      "topics": [
        "calculate_prior_infections"
      ]
    },
    {
      "page": "generate_data_extended",
      "title": "Generate data extended",
      "topics": [
        "generate_data_extended"
      ]
    },
    {
      "page": "get_expected_total_infections",
      "title": "Expected number of total symptomatic infections",
      "topics": [
        "get_expected_total_infections"
      ]
    },
    {
      "page": "get_incubation_day_distribution",
      "title": "Vector of day-specific probabilities of disease outbreak",
      "topics": [
        "get_incubation_day_distribution"
      ]
    },
    {
      "page": "get_infection_density",
      "title": "Dataframe with dates and probability of infection",
      "topics": [
        "get_infection_density"
      ]
    },
    {
      "page": "get_infectiousness_density",
      "title": "Dataframe with dates and infectiousness probability",
      "topics": [
        "get_infectiousness_density"
      ]
    },
    {
      "page": "get_misc_infection_density",
      "title": "Dataframe with dates and probability of infection",
      "topics": [
        "get_misc_infection_density"
      ]
    },
    {
      "page": "get_serial_interval_density",
      "title": "Dataframe with dates and contact symptom begin probability",
      "topics": [
        "get_serial_interval_density"
      ]
    },
    {
      "page": "get_test_sensitivities",
      "title": "Generate info",
      "topics": [
        "get_test_sensitivities"
      ]
    },
    {
      "page": "p_onePrimaryMore",
      "title": "One more primary a priori probability",
      "topics": [
        "p_onePrimaryMore"
      ]
    },
    {
      "page": "predict_future_infections",
      "title": "Prediction of future infections per day",
      "topics": [
        "predict_future_infections"
      ]
    }
  ],
  "_readme": "https://github.com/cran/smidm/raw/HEAD/README.md",
  "_rundeps": [
    "cli",
    "dplyr",
    "extraDistr",
    "generics",
    "glue",
    "lifecycle",
    "magrittr",
    "pillar",
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  "_vignettes": [
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      "source": "contacts.Rmd",
      "filename": "contacts.html",
      "title": "Statistical Modelling for Infectious Disease Management - Contacts",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Question",
        "Generating a data frame with dates and illness probability of contacts using get_serial_interval_density",
        "Inputs",
        "Methodology",
        "Output",
        "Visualization example of the data frame of \\newline get_serial_interval_density",
        "Literature"
      ],
      "created": "2022-08-27 07:40:02",
      "modified": "2022-08-27 07:40:02",
      "commits": 1
    },
    {
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      "filename": "contagious_period.html",
      "title": "Statistical Modelling for Infectious Disease Management - Contagious period",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Question",
        "Generating a data frame with dates and infectiousness probability using \\newline get_infectiousness_density",
        "Inputs",
        "Methodology",
        "Output",
        "Visualization example of the data frame of \\newline get_infectiousness_density",
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      ],
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    {
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      "filename": "infection_period.html",
      "title": "Statistical Modelling for Infectious Disease Management - Infection period",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Question",
        "Generating a data frame with dates and infection probability using \\newline get_infection_density for one person",
        "Inputs",
        "Methodology",
        "Output",
        "Generating a data frame with dates and probability of infection using \\newline get_misc_infection_density for several persons",
        "Visualization example of the data frame of \\newline get_infection_density",
        "Visualization example of the data frame of \\newline get_misc_infection_density",
        "Literature"
      ],
      "created": "2022-08-27 07:40:02",
      "modified": "2022-08-27 07:40:02",
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    },
    {
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      "filename": "future_infections.html",
      "title": "Statistical Modelling for Infectious Disease Management - Prediction of future infections in a group",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Question",
        "Calculating a prediction of the total number of infections with get_expected_total_infections",
        "Inputs",
        "Methodology",
        "Output",
        "Generating a vector with number of people starting to show symptoms on each day using \\newline predict_future_infections",
        "An example for visualizing the output of \\newline predict_future_infections",
        "Literature"
      ],
      "created": "2022-08-27 07:40:02",
      "modified": "2022-08-27 07:40:02",
      "commits": 1
    },
    {
      "source": "risk_assessment_group_quarantine.Rmd",
      "filename": "risk_assessment_group_quarantine.html",
      "title": "Statistical Modelling for Infectious Disease Management - Risk assessment group quarantine",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Question:",
        "Calculating the probability that nobody is infected given the negative test results using \\newline calculate_posterior_no_infections",
        "Inputs",
        "Methodology",
        "Output",
        "Calculating the likelihood using calculate_likelihood_negative_tests",
        "Calculating the priori probability distribution of further infections using \\newline calculate_prior_infections",
        "Outputs",
        "Visualization example of all date inputs on a time scale",
        "Literature"
      ],
      "created": "2022-08-27 07:40:02",
      "modified": "2022-08-27 07:40:02",
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