Evaluation of the EsteR Toolkit: Sensitivity Analysis of the Statistical Models

This repository contains the data and code for the sensitivity analysis of the statistical models used in the EsteR toolkit which is described in Alpers et al. 2022: Evaluation of the Ester Toolkit for COVID-19 Decision Support: Sensitivity Analysis and Usability Study. JMIR Preprints. DOI: 10.2196/preprints.44549 (Currently under review). With this code, the plots in the simulation report in Multimedia Appendix 1 were created.

Reproducing the Sensitivity Analysis with Snakemake

Requirements

The requirements.yml file can be used to create a conda environment with the snakemake package for workflow management and all required R packages for the analysis. The environment can be created via

mamba env create -f requirements.yml

(or conda if mamba is not installed) and activated via

conda activate ester_env

Running the whole analysis automatically

Above you can see the graph of jobs with their respective wildcards and the dependencies between them to recreate the plots from the simulation report. In order to create the DAG type

snakemake --dag | dot -Tsvg > dag.svg

and to execute all jobs type

snakemake -c1 all 

into your terminal. (-c1 can be replaced by any other number of cores you want to use. With one core, the analysis takes about 15-20 minutes to complete.)

Inside the EsteR-sensitivity-analysis folder, Snakemake will automatically create a new folder called results where all simulation outcomes, calculated metrics and plots will be saved.

Going through the analysis step-by-step

If you want to execute only certain jobs from the Snakefile, we will shortly describe the functionality of each Snakemake rule and give a list of accepted wildcards. For more detailed information about the use cases, scenarios and metrics see the main paper. To run a specific job, you can type "snakemake -c1 path-to-desired-output" into the terminal, where the wildcards need to be replaced by a valid expression. E.g. to create the plot for the use case infection spread (with the only valid scenario _ and metric deltapred) type

snakemake -c1 results/plots/infection_spread___deltapred.png

1. find_simulation_parameters_: The sensitivity analysis of three use cases uses data of the incubation time or serial interval of a COVID-19 infection. With this rule the default parameters for the web application and the parameter ranges for the sensitivity analysis can be identified.
   • {property}: incubation_time or serial_interval
2. run_simulation_: In the simulations, for each use case the respective model parameters are varied over specific ranges to calculate the outcomes in one or more scenarios. The simulations of the first three use cases depend on the previous step; for the other two use cases the parameters and their ranges are directly integrated in the scripts.
   • {use_case}: infection_period or infection_spread or illness_period or infectious_period or group_quarantine
3. calculate_metrics_: The outcomes for different model parameters are compared to the outcomes with default parameters in each use case by calculating different metrics.
   • {use_case}: infection_period or infection_spread or illness_period or infectious_period or group_quarantine
4. plot_metrics_: For visualization, the values from the previous step are plotted for each scenario and metric in each use case separately. Only specific combinations of wildcards are allowed:
   • {use_case}: infection_period
     • {scenario}: _
     • {metric}: iou or w1
   • {use_case}: infection_spread
     • {scenario}: _
     • {metric}: deltapred
   • {use_case}: illness_period
     • {scenario}: generation1 or generation2 or generation3
     • {metric}: iou or w1
   • {use_case}: infectious_period
     • {scenario}: _
     • {metric}: iou or w1
   • {use_case}: group_quarantine
     • {scenario}: childcare or school or sensitivity
     • {metric}: deltaprob

Authors and Contributors

Authors

Rieke Alpers

Contributors

(Contributing guidelines will follow)