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stochastic_systems

Practical material for modelling stochastic health systems

https://github.com/health-data-science-or/stochastic_systems

Science Score: 67.0%

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    Low similarity (8.1%) to scientific vocabulary
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Repository

Practical material for modelling stochastic health systems

Basic Info
  • Host: GitHub
  • Owner: health-data-science-OR
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: master
  • Size: 14.7 MB
Statistics
  • Stars: 10
  • Watchers: 2
  • Forks: 2
  • Open Issues: 1
  • Releases: 5
Created about 6 years ago · Last pushed over 1 year ago
Metadata Files
Readme Changelog License Citation

README.md

Binder DOI License: MIT Python 3.7+

HPDM097 - Making a difference with health data:

Stochastic Healthcare Systems

Practical material for modelling stochastic health systems

Dependencies

Please use the provided conda environment

``` conda env create -f binder/environment.yml

conda activate hds_stoch ```

Syllabus

Computer simulation exercises

  1. Introduction to SimPy Colab
  • optional: Advanced methods for results collection Colab

  1. Modelling more complex health systems Colab

  2. Input modelling exercises

    3.1 Introduction to autofit [Colab](https://colab.research.google.com/github/health-data-science-OR/stochasticsystems/blob/master/labs/simulation/lab3/simlab3autofit_intro.ipynb)

    3.2 A&E data wrangling and input modelling Colab

  3. Modelling time dependent arrivals Colab

  4. Case study: modelling health systems with a scheduling function. Colab

  5. Simulation output analysis Colab

Solutions to exercises:

  1. Introduction to SimPy Colab

  2. Modelling more complex health systems Colab

  3. Input modelling exercises

    3.2 A&E data wrangling and input modelling Colab

  4. Modelling time dependent arrivals Colab

  5. Case study: modelling health systems with a scheduling function. Colab

  6. Simulation output analysis Colab

Owner

  • Name: Health Data Science and Operations Research
  • Login: health-data-science-OR
  • Kind: organization

Citation (CITATION.cff) 

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: modelling stochastic health care systems using simulation in Python
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Thomas
    family-names: Monks
    affiliation: University of Exeter
    orcid: 'https://orcid.org/0000-0003-2631-4481'
repository-code: 'https://github.com/health-data-science-OR/stochastic_systems/'
keywords:
  - discrete-event simulation
  - stochastic systems
  - queuing 
  - health service delivery
  - python
  - open science
license: MIT

GitHub Events

Total
  • Watch event: 1
  • Push event: 5
Last Year
  • Watch event: 1
  • Push event: 5

Dependencies

binder/environment.yml pypi