Model overview
This open R simulation by Wood and colleagues examines hypothetical intensive-care triage strategies when COVID-19 demand might exceed capacity. It compares first-come, first-served admission, triage when demand is expressed and reverse triage. Inputs include demand, length of stay, capacity and assumptions about patient outcomes; outputs include lives and life-years lost. The model has been independently reproduced through the STARS project.
Session objectives and outline
This session will focus on obtaining the original code and running a defined part of the analysis. Participants will:
- clone the original GitHub repository and identify the main R scripts, inputs and outputs;
- establish an environment in which a defined part of the analysis will run;
- reproduce an existing scenario, table or figure;
- compare their run with the paper and independent reproduction; and
- explore the existing demand, capacity and triage scenarios with appropriate caution.
The aim is for participants to leave with a local copy of the repository, a working environment and a successful run of a defined part of the analysis.
Actions for participants before the session
- Bring a laptop with R and Git installed.
- Clone or download the original model repository.
- Review the STARS reproduction website.
- Read the paper abstract or the shorter HDR UK case study.
Older analytical environments can require specific package versions. Tested setup instructions will be added here before the session.
Additional resources
- Original GitHub repository — R model, inputs, analysis scripts and outputs.
- Peer-reviewed paper — Wood et al. (2021), Medical Decision Making.
- PubMed record — abstract and bibliographic details.
- HDR UK case study — accessible account of the question and findings.