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-4.7-any)smidm_1.0.tar.gz(r-4.6-any)
smidm_1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
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.

3.60 score 16 scripts 649 downloads 11 exports 18 dependencies

Last updated from:55c28e6f41. Checks:4 OK. Indexed: yes.

Target	Result	Time	Files	Syslog
linux-devel-x86_64	OK	142
source / vignettes	OK	219
linux-release-x86_64	OK	145
wasm-release	OK	152

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 generics glue lifecycle magrittr pillar pkgconfig R6 Rcpp RcppArmadillo rlang tibble tidyselect utf8 vctrs withr

Statistical Modelling for Infectious Disease Management - Contacts

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

Rendered fromcontacts.Rmdusingknitr::rmarkdown

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

Statistical Modelling for Infectious Disease Management - Contagious period

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 | Literature

Rendered fromcontagious_period.Rmdusingknitr::rmarkdown

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

Statistical Modelling for Infectious Disease Management - Infection period

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

Rendered frominfection_period.Rmdusingknitr::rmarkdown

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

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

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

Rendered fromfuture_infections.Rmdusingknitr::rmarkdown

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

Statistical Modelling for Infectious Disease Management - Risk assessment group quarantine

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

Rendered fromrisk_assessment_group_quarantine.Rmdusingknitr::rmarkdown

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

Citation

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

Usage by other packages (reverse dependencies)