Welcome to Epidemics on Networks's documentation! ================================================= **EoN** (Epidemics on Networks) is a Python module that provides tools to study the spread of SIS and SIR diseases in networks. **Support EoN**: - The best way to support EoN is to `cite EoN's publication `_ - The next best option `is to let me know you're using it`_. - Both of these will help my case when applying for grants & promotions and help me justify the time I spend on it. **MIT License**: See :download:`license.txt<../license.txt>` for full details. Highlights ---------- **EoN** is based on the book `Mathematics of Epidemics on Networks: from Exact to Approximate Models`_ **EoN** is built on top of NetworkX_. Its repository_ is on github. EoN's tools fall into two broad categories: - **Stochastic simulation of SIS and SIR disease** - Event-based simulation - much faster than traditional Gillespie simulation - allows weighted graphs - allows non-Markovian dynamics - Gillespie algorithms for Markovian dynamics - Through some careful optimization the unweighted SIS/SIR versions are comparable to the event-based simulation. - The weighted version is slower, but still reasonably fast. - There are methods for generic simple contagions and generic complex contagions. - discrete-time (synchronous update) models - tools for visualizing and animating simulated epidemics. - **Numerical solvers for ODE models** - pair approximation models - effective degree models - edge-based compartmental models Table of Contents ----------------- .. toctree:: :maxdepth: 2 Getting Started Examples EoN Changes .. _repository: https://github.com/springer-math/Mathematics-of-Epidemics-on-Networks .. _Mathematics of epidemics on networks\: from exact to approximate models: http://www.springer.com/us/book/9783319508047 .. _NetworkX: https://networkx.github.io .. _is to let me know you're using it: https://github.com/springer-math/Mathematics-of-Epidemics-on-Networks/issues/31