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)
Course files for "Ocean/Atmosphere Time Series Analysis"

time-series This package contains all necessary files for the course Ocean/Atmosphere Time Series Analysis, an introduction to data and time series an

Jonathan Lilly 107 Nov 29, 2022
Code base of KU AIRS: SPARK Autonomous Vehicle Team

KU AIRS: SPARK Autonomous Vehicle Project Check this link for the blog post describing this project and the video of SPARK in simulation and on parkou

Mehmet Enes Erciyes 1 Nov 23, 2021
A Python implementation of FastDTW

fastdtw Python implementation of FastDTW [1], which is an approximate Dynamic Time Warping (DTW) algorithm that provides optimal or near-optimal align

tanitter 651 Jan 04, 2023
Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking and Jupyter notebook analysis.

sklearn-evaluation Machine learning model evaluation made easy: plots, tables, HTML reports, experiment tracking, and Jupyter notebook analysis. Suppo

Eduardo Blancas 354 Dec 31, 2022
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

1.3k Dec 28, 2022
TorchDrug is a PyTorch-based machine learning toolbox designed for drug discovery

A powerful and flexible machine learning platform for drug discovery

MilaGraph 1.1k Jan 08, 2023
Automated Machine Learning Pipeline for tabular data. Designed for predictive maintenance applications, failure identification, failure prediction, condition monitoring, etc.

Automated Machine Learning Pipeline for tabular data. Designed for predictive maintenance applications, failure identification, failure prediction, condition monitoring, etc.

Amplo 10 May 15, 2022
In this Repo a simple Sklearn Model will be trained and pushed to MLFlow

SKlearn_to_MLFLow In this Repo a simple Sklearn Model will be trained and pushed to MLFlow Install This Repo is based on poetry python3 -m venv .venv

1 Dec 13, 2021
Esse é o meu primeiro repo tratando de fim a fim, uma pipeline de dados abertos do governo brasileiro relacionado a compras de contrato e cronogramas anuais com spark, em pyspark e SQL!

Olá! Esse é o meu primeiro repo tratando de fim a fim, uma pipeline de dados abertos do governo brasileiro relacionado a compras de contrato e cronogr

Henrique de Paula 10 Apr 04, 2022
GAM timeseries modeling with auto-changepoint detection. Inspired by Facebook Prophet and implemented in PyMC3

pm-prophet Pymc3-based universal time series prediction and decomposition library (inspired by Facebook Prophet). However, while Faceook prophet is a

Luca Giacomel 314 Dec 25, 2022
A Pythonic framework for threat modeling

pytm: A Pythonic framework for threat modeling Introduction Traditional threat modeling too often comes late to the party, or sometimes not at all. In

Izar Tarandach 644 Dec 20, 2022
Crunchdao - Python API for the Crunchdao machine learning tournament

Python API for the Crunchdao machine learning tournament Interact with the Crunc

3 Jan 19, 2022
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

Ross Taylor 2k Jan 02, 2023
Banpei is a Python package of the anomaly detection.

Banpei Banpei is a Python package of the anomaly detection. Anomaly detection is a technique used to identify unusual patterns that do not conform to

Hirofumi Tsuruta 282 Jan 03, 2023
We have a dataset of user performances. The project is to develop a machine learning model that will predict the salaries of baseball players.

Salary-Prediction-with-Machine-Learning 1. Business Problem Can a machine learning project be implemented to estimate the salaries of baseball players

Ayşe Nur Türkaslan 9 Oct 14, 2022
A python library for easy manipulation and forecasting of time series.

Time Series Made Easy in Python darts is a python library for easy manipulation and forecasting of time series. It contains a variety of models, from

Unit8 5.2k Jan 04, 2023
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques

Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learn

Vowpal Wabbit 8.1k Dec 30, 2022
A Python library for choreographing your machine learning research.

A Python library for choreographing your machine learning research.

AI2 270 Jan 06, 2023
Greykite: A flexible, intuitive and fast forecasting library

The Greykite library provides flexible, intuitive and fast forecasts through its flagship algorithm, Silverkite.

LinkedIn 1.7k Jan 04, 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