Package: smidm 1.0

Sonja Jäckle

smidm: Statistical Modelling for Infectious Disease Management

Statistical models for specific coronavirus disease 2019 use cases at German local health authorities. All models of Statistical modelling for infectious disease management 'smidm' are part of the decision support toolkit in the 'EsteR' project. More information is published in Sonja Jäckle, Rieke Alpers, Lisa Kühne, Jakob Schumacher, Benjamin Geisler, Max Westphal "'EsteR' – A Digital Toolkit for COVID-19 Decision Support in Local Health Authorities" (2022) <doi:10.3233/SHTI220799> and Sonja Jäckle, Elias Röger, Volker Dicken, Benjamin Geisler, Jakob Schumacher, Max Westphal "A Statistical Model to Assess Risk for Supporting COVID-19 Quarantine Decisions" (2021) <doi:10.3390/ijerph18179166>.

Authors:Max Westphal [aut], Stefanie Grimm [aut], Sonja Jäckle [aut, cre], Rieke Alpers [aut], Hong Phuc Truong [aut], Amelie Lucker [ctb], Fraunhofer MEVIS [cph], Fraunhofer ITWM [cph]

smidm_1.0.tar.gz
smidm_1.0.tar.gz(r-4.5-noble)smidm_1.0.tar.gz(r-4.4-noble)
smidm_1.0.tgz(r-4.4-emscripten)smidm_1.0.tgz(r-4.3-emscripten)
smidm.pdf |smidm.html✨
smidm/json (API)

# Install 'smidm' in R:

install.packages('smidm', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.40 score 713 downloads 11 exports 18 dependencies

Last updated 3 years agofrom:55c28e6f41. Checks:3 OK. Indexed: yes.

Target	Result	Latest binary
Doc / Vignettes	OK	Mar 06 2025
R-4.5-linux	OK	Mar 06 2025
R-4.4-linux	OK	Mar 06 2025

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

Dependencies:cli dplyr extraDistr fansi generics glue lifecycle magrittr pillar pkgconfig R6 Rcpp rlang tibble tidyselect utf8 vctrs withr

Statistical Modelling for Infectious Disease Management - Contacts

Rendered fromcontacts.Rmdusingknitr::rmarkdownon Mar 06 2025.

Last update: 2022-08-27
Started: 2022-08-27

Statistical Modelling for Infectious Disease Management - Contagious period

Rendered fromcontagious_period.Rmdusingknitr::rmarkdownon Mar 06 2025.

Last update: 2022-08-27
Started: 2022-08-27

Statistical Modelling for Infectious Disease Management - Infection period

Rendered frominfection_period.Rmdusingknitr::rmarkdownon Mar 06 2025.

Last update: 2022-08-27
Started: 2022-08-27

Statistical Modelling for Infectious Disease Management - Prediction of future infections in a group

Rendered fromfuture_infections.Rmdusingknitr::rmarkdownon Mar 06 2025.

Last update: 2022-08-27
Started: 2022-08-27

Statistical Modelling for Infectious Disease Management - Risk assessment group quarantine

Rendered fromrisk_assessment_group_quarantine.Rmdusingknitr::rmarkdownon Mar 06 2025.

Last update: 2022-08-27
Started: 2022-08-27

Citation

To cite package ‘smidm’ in publications use:

Westphal M, Grimm S, Jäckle S, Alpers R, Truong HP (2022). smidm: Statistical Modelling for Infectious Disease Management. R package version 1.0, https://CRAN.R-project.org/package=smidm.

Corresponding BibTeX entry:

  @Manual{,
    title = {smidm: Statistical Modelling for Infectious Disease
      Management},
    author = {Max Westphal and Stefanie Grimm and Sonja Jäckle and
      Rieke Alpers and Hong Phuc Truong},
    year = {2022},
    note = {R package version 1.0},
    url = {https://CRAN.R-project.org/package=smidm},
  }

Readme and manuals

Smidm - Statistical Modelling for Infectious Disease Management

Overview

Smidm implements statistical models and visualizations to support decision making by health authorities w.r.t. the COVID-19 pandemic. The application can be viewed here.

Installation

This package can be installed for developers with access to this repository with this command:

install.packages(c("devtools", "rmarkdown"))
devtools::install_git(
  "https://gitlab.cc-asp.fraunhofer.de/ester/smidm.git",
  ref = "main",
  build_vignettes = TRUE
  )

The ref argument can be used to specify which version/branch should be installed.

Getting started

Several vignettes have been compiled to illustrate the functionality of the package. An overview can be displayed via:

vignette(package = "smidm")

To display an individual vignette, e.g. for the prediction when contacts of an infected person will start to show symptoms, utilize the following command:

vignette(topic = "contacts", package = "smidm")

Conventions

As style guide for this project the tidyverse style guide is used.

Version numbers of the package are given by the Semantic Versioning.

The default branch is main. For adding new features, you need to create a new branch. On the main branch is no pushing, only merging.

Development and research

The research project was funded from 15.05.2020 - 14.12.2020 within the Fraunhofer Anti-Corona Program and from 01.07.2021 - 30.06.2022 within the program Prevention and Care of Epidemic Infections with Innovative Medical Technology by the Federal Ministry of Education and Research.

In addition to the R-package a web application was built, which is available at https://ester.fraunhofer.de/.

Authors and contact

The project was developed by Fraunhofer Institute for Industrial Mathematics ITWM, Fraunhofer Institute for Digital Medicine MEVIS and Leibniz Institute for Prevention Research and Epidemiology BIPS in cooperation with the health authority Berlin-Reinickendorf.

If you have any questions, feedback, issues/PR, you can contact us via ester-info@itwm.fraunhofer.de.

For further contact information, please visit the website https://www.itwm.fraunhofer.de/en/departments/fm/data-science.html.

License

Licensed under the BSD 3-Clause License.

Help Manual

Help page	Topics
Overall likelihood	calculate_likelihood_negative_tests
Likelihood K	calculate_likelihood_negative_tests_k
Negative analysis probability	calculate_posterior_no_infections
A priori probability of further Infections	calculate_prior_infections
Generate data extended	generate_data_extended
Expected number of total symptomatic infections	get_expected_total_infections
Vector of day-specific probabilities of disease outbreak	get_incubation_day_distribution
Dataframe with dates and probability of infection	get_infection_density
Dataframe with dates and infectiousness probability	get_infectiousness_density
Dataframe with dates and probability of infection	get_misc_infection_density
Dataframe with dates and contact symptom begin probability	get_serial_interval_density
Generate info	get_test_sensitivities
One more primary a priori probability	p_onePrimaryMore
Prediction of future infections per day	predict_future_infections

Usage by other packages (reverse dependencies)