ReModel: Read, Run, Reuse Open Simulation Models

About
Join us
Sessions

ReModel sessions

Categories

All (6)

DES (6)

Nonelective Flow DES Model: Running

DES

The Non-Elective Flow Simulation is an open-source discrete-event simulation developed by Helena Robinson at Countess of Chester Hospital NHS Foundation Trust. It represents patients moving from the emergency department into SDEC or an inpatient bed and explores how bed capacity, admitted length of stay and SDEC activity affect queues and waiting times. The model is written in Python using SimPy and includes a Streamlit interface.

Nonelective Flow DES Model: Reusing

DES

The Non-Elective Flow Simulation is an open-source discrete-event simulation developed by Helena Robinson at Countess of Chester Hospital NHS Foundation Trust. It represents patients moving from the emergency department into SDEC or an inpatient bed and explores how bed capacity, admitted length of stay and SDEC activity affect queues and waiting times. The model is written in Python using SimPy and includes a Streamlit interface.

Meeting the demand of 111 for primary care services: Running

DES

MOOOD is an open-source discrete-event simulation developed by Richard Pilbery and colleagues. It follows callers after an NHS 111 primary-care disposition and represents subsequent contact with primary care, NHS 111, ambulance services and emergency departments. The Python and SimPy model compares the existing system with timely primary-care contact and reports activity across services and avoidable ED attendance.

Meeting the demand of 111 for primary care services: Reusing

DES

MOOOD is an open-source discrete-event simulation developed by Richard Pilbery and colleagues. It follows callers after an NHS 111 primary-care disposition and represents subsequent contact with primary care, NHS 111, ambulance services and emergency departments. The Python and SimPy model compares the existing system with timely primary-care contact and reports activity across services and avoidable ED attendance.

The Value of Triage during Periods of Intense COVID-19 Demand: Simulation Modeling Study: Running

DES

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.

The Value of Triage during Periods of Intense COVID-19 Demand: Simulation Modeling Study: Reusing

DES

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.

No matching items