Summer: compartmental disease modelling in Python

Overview

Summer: compartmental disease modelling in Python

Automated Tests

Summer is a Python-based framework for the creation and execution of compartmental (or "state-based") epidemiological models of infectious disease transmission.

It provides a range of structures for easily implementing compartmental models, including structure for some of the most common features added to basic compartmental frameworks, including:

  • A variety of inter-compartmental flows (infections, transitions, births, deaths, imports)
  • Force of infection multipliers (frequency, density)
  • Post-processing of compartment sizes into derived outputs
  • Stratification of compartments, including:
    • Adjustments to flow rates based on strata
    • Adjustments to infectiousness based on strata
    • Heterogeneous mixing between strata
    • Multiple disease strains

Some helpful links to learn more:

Installation and Quickstart

This project is tested with Python 3.6. Install the summerepi package from PyPI

pip install summerepi

Then you can use the library to build and run models. See here for some code examples.

Development

Poetry is used for packaging and dependency management.

Initial project setup is documented here and should work for Windows or Ubuntu, maybe for MacOS.

Some common things to do as a developer working on this codebase:

# Activate summer conda environment prior to doing other stuff (see setup docs)
conda activate summer

# Install latest requirements
poetry install

# Publish to PyPI - use your PyPI credentials
poetry publish --build

# Add a new package
poetry add

# Run tests
pytest -vv

# Format Python code
black .
isort . --profile black

Releases

Releases are numbered using Semantic Versioning

  • 1.0.0/1:
    • Initial release
  • 1.1.0:
    • Add stochastic integrator
  • 2.0.2:
    • Rename fractional flow to transition flow
    • Remove sojourn flow
    • Add vectorized backend and other performance improvements
  • 2.0.3:
    • Set default IVP solver to use a maximum step size of 1 timestep
  • 2.0.4:
    • Add runtime derived values
  • 2.0.5:
    • Remove legacy Summer implementation
  • 2.1.0:
    • Add AdjustmentSystems
    • Improve vectorization of flows
    • Add computed_values inputs to flow and adjustment parameters
  • 2.1.1:
    • Fix for invalid/unused package imports (cachetools)
  • 2.2.0
    • Add validation and compartment caching optimizations
  • 2.2.1
    • Derived output index caching
    • Optimized fast-tracks for infectious multipliers
  • 2.2.2
    • JIT infectiousness calculations
    • Various micro-optimizations
  • 2.2.3
    • Bugfix release (clamp outputs to 0.0)
  • 2.2.4
    • Datetime awareness, DataFrame outputs

Release process

To do a release:

  • Commit any code changes and push them to GitHub
  • Choose a new release number accoridng to Semantic Versioning
  • Add a release note above
  • Edit the version key in pyproject.toml to reflect the release number
  • Publish the package to PyPI using Poetry, you will need a PyPI login and access to the project
  • Commit the release changes and push them to GitHub (Use a commit message like "Release 1.1.0")
  • Update requirements.txt in Autumn to use the new version of Summer
poetry build
poetry publish

Documentation

Sphinx is used to automatically build reference documentation for this library. The documentation is automatically built and deployed to summerepi.com whenever code is pushed to master.

To run or edit the code examples in the documentation, start a jupyter notebook server as follows:

jupyter notebook --config docs/jupyter_notebook_config.py
# Go to http://localhost:8888/tree/docs/examples in your web browser.

You can clean outputs from all the example notbooks with

./docs/scripts/clean.sh

To build and deploy

./docs/scripts/build.sh
./docs/scripts/deploy.sh

To work on docs locally

./docs/scripts/watch.sh
You might also like...
Metric learning algorithms in Python

metric-learn: Metric Learning in Python metric-learn contains efficient Python implementations of several popular supervised and weakly-supervised met

[HELP REQUESTED] Generalized Additive Models in Python
[HELP REQUESTED] Generalized Additive Models in Python

pyGAM Generalized Additive Models in Python. Documentation Official pyGAM Documentation: Read the Docs Building interpretable models with Generalized

Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

Karate Club is an unsupervised machine learning extension library for NetworkX. Please look at the Documentation, relevant Paper, Promo Video, and Ext

Open source time series library for Python

PyFlux PyFlux is an open source time series library for Python. The library has a good array of modern time series models, as well as a flexible array

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Master status: Development status: Package information: TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assista

MLBox is a powerful Automated Machine Learning python library.
MLBox is a powerful Automated Machine Learning python library.

MLBox is a powerful Automated Machine Learning python library. It provides the following features: Fast reading and distributed data preprocessing/cle

Python package for stacking (machine learning technique)
Python package for stacking (machine learning technique)

vecstack Python package for stacking (stacked generalization) featuring lightweight functional API and fully compatible scikit-learn API Convenient wa

A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning

imbalanced-learn imbalanced-learn is a python package offering a number of re-sampling techniques commonly used in datasets showing strong between-cla

Python-based implementations of algorithms for learning on imbalanced data.

ND DIAL: Imbalanced Algorithms Minimalist Python-based implementations of algorithms for imbalanced learning. Includes deep and representational learn

Comments
  • Vectorized backend and support code

    Vectorized backend and support code

    This is the fast vectorized backend we've been discussing lately. It runs our covid model ~3x faster than the reference.

    Wanting to get this merged sooner rather than later to avoid code drift. Matt has looked at this already, feedback from James appreciated

    opened by dshipman 0
Releases(v1.0.1)
Decision Weights in Prospect Theory

Decision Weights in Prospect Theory It's clear that humans are irrational, but how irrational are they? After some research into behavourial economics

Cameron Davidson-Pilon 32 Nov 08, 2021
This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch

This is an implementation of the proximal policy optimization algorithm for the C++ API of Pytorch. It uses a simple TestEnvironment to test the algorithm

Martin Huber 59 Dec 09, 2022
A library of sklearn compatible categorical variable encoders

Categorical Encoding Methods A set of scikit-learn-style transformers for encoding categorical variables into numeric by means of different techniques

2.1k Jan 07, 2023
A simple guide to MLOps through ZenML and its various integrations.

ZenBytes Join our Slack Community and become part of the ZenML family Give the main ZenML repo a GitHub star to show your love ZenBytes is a series of

ZenML 127 Dec 27, 2022
Scikit-learn compatible wrapper of the Random Bits Forest program written by (Wang et al., 2016)

sklearn-compatible Random Bits Forest Scikit-learn compatible wrapper of the Random Bits Forest program written by Wang et al., 2016, available as a b

Tamas Madl 8 Jul 24, 2021
A project based example of Data pipelines, ML workflow management, API endpoints and Monitoring.

MLOps template with examples for Data pipelines, ML workflow management, API development and Monitoring.

Utsav 33 Dec 03, 2022
Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and t

164 Jan 04, 2023
A machine learning toolkit dedicated to time-series data

tslearn The machine learning toolkit for time series analysis in Python Section Description Installation Installing the dependencies and tslearn Getti

2.3k Dec 29, 2022
2D fluid simulation implementation of Jos Stam paper on real-time fuild dynamics, including some suggested extensions.

Fluid Simulation Usage Download this repo and store it in your computer. Open a terminal and go to the root directory of this folder. Make sure you ha

Mariana Ávalos Arce 5 Dec 02, 2022
Skforecast is a python library that eases using scikit-learn regressors as multi-step forecasters

Skforecast is a python library that eases using scikit-learn regressors as multi-step forecasters. It also works with any regressor compatible with the scikit-learn API (pipelines, CatBoost, LightGBM

Joaquín Amat Rodrigo 297 Jan 09, 2023
A data preprocessing and feature engineering script for a machine learning pipeline is prepared.

FEATURE ENGINEERING Business Problem: A data preprocessing and feature engineering script for a machine learning pipeline needs to be prepared. It is

Pinar Oner 7 Dec 18, 2021
Bayesian Modeling and Computation in Python

Bayesian Modeling and Computation in Python Open access and Code This repository contains the open access version of the text and the code examples in

Bayesian Modeling and Computation in Python 339 Jan 02, 2023
Dual Adaptive Sampling for Machine Learning Interatomic potential.

DAS Dual Adaptive Sampling for Machine Learning Interatomic potential. How to cite If you use this code in your research, please cite this using: Hong

6 Jul 06, 2022
onelearn: Online learning in Python

onelearn: Online learning in Python Documentation | Reproduce experiments | onelearn stands for ONE-shot LEARNning. It is a small python package for o

15 Nov 06, 2022
Python library for multilinear algebra and tensor factorizations

scikit-tensor is a Python module for multilinear algebra and tensor factorizations

Maximilian Nickel 394 Dec 09, 2022
A simple machine learning python sign language detection project.

SST Coursework 2022 About the app A python application that utilises the tensorflow object detection algorithm to achieve automatic detection of ameri

Xavier Koh 2 Jun 30, 2022
Python 3.6+ toolbox for submitting jobs to Slurm

Submit it! What is submitit? Submitit is a lightweight tool for submitting Python functions for computation within a Slurm cluster. It basically wraps

Facebook Incubator 768 Jan 03, 2023
The easy way to combine mlflow, hydra and optuna into one machine learning pipeline.

mlflow_hydra_optuna_the_easy_way The easy way to combine mlflow, hydra and optuna into one machine learning pipeline. Objective TODO Usage 1. build do

shibuiwilliam 9 Sep 09, 2022
MLBox is a powerful Automated Machine Learning python library.

MLBox is a powerful Automated Machine Learning python library. It provides the following features: Fast reading and distributed data preprocessing/cle

Axel 1.4k Jan 06, 2023
30 Days Of Machine Learning Using Pytorch

Objective of the repository is to learn and build machine learning models using Pytorch. 30DaysofML Using Pytorch

Mayur 119 Nov 24, 2022