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:
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')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:55c28e6f41. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Nov 06 2024 |
| R-4.5-linux | OK | Nov 06 2024 |
Exports:calculate_likelihood_negative_testscalculate_posterior_no_infectionscalculate_prior_infectionsget_expected_total_infectionsget_incubation_day_distributionget_infection_densityget_infectiousness_densityget_misc_infection_densityget_serial_interval_densityget_test_sensitivitiespredict_future_infections
Dependencies:clidplyrextraDistrfansigenericsgluelifecyclemagrittrpillarpkgconfigR6Rcpprlangtibbletidyselectutf8vctrswithr
Statistical Modelling for Infectious Disease Management - Contacts
Rendered fromcontacts.Rmdusingknitr::rmarkdownon Nov 06 2024.Last update: 2022-08-27
Started: 2022-08-27
Statistical Modelling for Infectious Disease Management - Contagious period
Rendered fromcontagious_period.Rmdusingknitr::rmarkdownon Nov 06 2024.Last update: 2022-08-27
Started: 2022-08-27
Statistical Modelling for Infectious Disease Management - Infection period
Rendered frominfection_period.Rmdusingknitr::rmarkdownon Nov 06 2024.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 Nov 06 2024.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 Nov 06 2024.Last update: 2022-08-27
Started: 2022-08-27
Readme and manuals
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 |