episimmer

Episimmer is an Epidemic Simulation Framework for Decision Support. It is a highly flexible system that can be easily configured to help take decisions during an epidemic in closed communities like university campuses and gated communities.

https://github.com/healthbadge/episimmer

Science Score: 36.0%

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    Low similarity (12.6%) to scientific vocabulary

Keywords

decision-support-system epidemic-simulations epidemiology intervention policy recommender simulation usermodel vulnerability-detection
Last synced: 6 months ago · JSON representation

Repository

Episimmer is an Epidemic Simulation Framework for Decision Support. It is a highly flexible system that can be easily configured to help take decisions during an epidemic in closed communities like university campuses and gated communities.

Basic Info
  • Host: GitHub
  • Owner: healthbadge
  • License: other
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage:
  • Size: 49.5 MB
Statistics
  • Stars: 16
  • Watchers: 3
  • Forks: 3
  • Open Issues: 5
  • Releases: 4
Topics
decision-support-system epidemic-simulations epidemiology intervention policy recommender simulation usermodel vulnerability-detection
Created over 5 years ago · Last pushed 6 months ago
Metadata Files
Readme License Citation

README.md

Test Status Documentation Status Code Coverage License PyPI

Episimmer : Epidemic Simulation Platform

Powered by HealthBadge

Episimmer is an Epidemic Simulation Platform that aims to provide Decision and Recommendation Support to help answer your questions related to policies and restrictions during an epidemic. Using simulation techniques widely applied to other fields, we can help schools and colleges discover and hone the opportunities and optimizations they could make to their COVID-19 strategy.

From the most simple decisions (Which days to be online or offline) to more complex strategies (What restrictions should I put on library use?, How many times should I test?, Whom do I test?) Episimmer is the tool for the job.

The Environment

Episimmer is an agent-based epidemic simulator which allows you to model any kind of disease spreading environment. With the help of simple text files, you can have your agents and the interaction network setup in no time. Here are some example of the environments you can create.

SIR_2_locations.gifSIR_2_locations.gif|random_SIR.gifrandom_SIR.gif :-------------------------:|:-------------------------: Completely connected agents at two locations | Random Graph G(100, 0.1)

Star_graph.gifStar_graph.gif|cellular_automaton.gifcellular_automaton.gif|multi_cycle.gifmulti_cycle.gif :-------------------------:|:-------------------------:|:-------------------------: Star Graph | Cellular Automaton | Multi-cycle graph

The edges represent connections between the agents and the node colours represent the changing agent disease state.

But these are static networks, which aren't truly representing real-world phenomena. Episimmer allows you to have cycling dynamic networks which more closely align with real-world social networks than static networks.

Dynamic Network

Dynamic Network

Dynamic Network

The Disease Model

Episimmer also allows easy creation of compartmental disease models. You can be as creative as you like, for example,

complex_model1.pngcomplex_model1.png|complex_model2.pngcomplex_model2.png :-------------------------:|:-------------------------: Complex Model 1 (Taken from here) | Complex Model 2 (Taken from here)


Or you could even model a fancy Zombie Apocalypse disease model like this

Zombie Apocalypse

Zombie Apocalypse

Zombie Apocalypse

Intervention Policies

There are many kinds of policies that can be implemented in Episimmer by the user and each policy is built to be flexible. Multiple policies defined by the user can be run in a simulation seamlessly. We have also created templates for the user to test different kinds of policies.

Currently, the policies that can be implemented are:

  1. Lockdown Policy
  2. Testing Policy
  3. Vaccination Policy
  4. Contact Tracing Policy

Vulnerability Detection

Vulnerability Detection refers to finding vulnerabilities in the system that highly affect or are highly affected by disease spread. A major part of decision support is detecting vulnerabilities in the ecosystem and taking appropriate actions to control the disease spread.

Two kinds of Vulnerability Detection exist in Episimmer -

  1. Agent-based Vulnerability Detection
  2. Event-based Vulnerability Detection

For more details, check out the official documentation.

Installation

Prerequisites

Episimmer requires python 3.7+.

Install using pip

If you are using Linux or macOS you can install episimmer from PyPI with pip: pip install episimmer

Install from source

Or you can install from source

  1. First clone this repository: git clone https://github.com/healthbadge/episimmer.git
  2. Then, to install the package, run the following command inside the episimmer directory: pip install -e .

  3. If you do not have pip you can instead use: python setup.py install

If you do not have root access, you should add the --user option to the above lines.

Running Examples

To run examples -

If you installed episimmer through PyPI, run:

episimmer <Path_to_Example>

Otherwise, in the repository, run: python episimmer/main.py <Path_to_Example>

Command line Arguments

positional arguments: example_path : Pass the path to the data folder

optional arguments: -np or --noplot : Restrict plotting the time plot after simulation. Default = False -vul or --vuldetect : Run Vulnerability Detection on the data folder based on VD_config.txt. Default = False -a or --animate : Creates a gif animation of the time plot. Default = False -s or --stats : Choose to store statistics. Default = False -viz or --vizdyn : Creates a gif of the simulation environment progressing through the days. Default = False

Tutorials

Check out Episimmer's official documentation for a complete tutorial on the simulator. You may also go through these colab notebooks for a more hands-on tutorial on Episimmer:

  1. Tutorial 1 - Episimmer Basics
  2. Tutorial 2 - The Environment
  3. Tutorial 3 - Disease Modelling
  4. Tutorial 4 - Intervention Policies
  5. Tutorial 5 - Vulnerability Detection

UI

Our current UI can be found at here. Note that it has minimal functionality as compared to running the codebase directly. Yet it competes with the current state of the art systems with multiple novel features.

GitHub Events

Total
  • Watch event: 1
  • Push event: 7
  • Fork event: 1
Last Year
  • Watch event: 1
  • Push event: 7
  • Fork event: 1

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 856
  • Total Committers: 14
  • Avg Commits per committer: 61.143
  • Development Distribution Score (DDS): 0.532
Top Committers
Name Email Commits
suryadheeshjith S****h@g****m 401
Inavamsi Enaganti i****5@g****m 262
inavamsi i****m 40
Surya Dheeshjith 4****h@u****m 39
Anurita Bose 6****e@u****m 38
Apar Ahuja a****a@A****l 25
Uday i****8@g****m 13
Apar Ahuja 6****a@u****m 8
ruthushankar 6****r@u****m 7
ruthushankar r****r@g****m 7
Foy Savas f****y@s****s 5
tusharshetty61 t****1@g****m 5
pre-commit-ci[bot] 6****]@u****m 3
anuritabose a****s@g****m 3
Committer Domains (Top 20 + Academic)
sav.as: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 23
  • Total pull requests: 77
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 1 day
  • Total issue authors: 2
  • Total pull request authors: 8
  • Average comments per issue: 0.17
  • Average comments per pull request: 0.21
  • Merged pull requests: 66
  • Bot issues: 0
  • Bot pull requests: 2
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • inavamsi (21)
  • suryadheeshjith (2)
Pull Request Authors
  • suryadheeshjith (49)
  • inavamsi (10)
  • ItIsUday (5)
  • AparAhuja (4)
  • ruthushankar (3)
  • anuritabose (2)
  • pre-commit-ci[bot] (2)
  • tusharshetty61 (2)
Top Labels
Issue Labels
good first issue (2) bug (1)
Pull Request Labels
documentation (5) enhancement (4)

Dependencies

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.github/workflows/test.yml actions
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