EoN (Epidemics on Networks): a fast, flexible Python package for simulation, analytic approximation, and analysis of epidemics on networks
arXiv:2001.02436 · doi:10.21105/joss.01731
Abstract
We provide a description of the Epidemics on Networks (EoN) python package designed for studying disease spread in static networks. The package consists of over methods available for users to perform stochastic simulation of a range of different processes including SIS and SIR disease, and generic simple or comlex contagions.
This paper is available in a shorter form without examples at https://joss.theoj.org/papers/10.21105/joss.01731.pdf
References in corpus (8)
- Cooperative Game Theory Approaches for Network Partitioning
- Random graphs with clustering
- Cascade Dynamics of Multiplex Propagation
- Edge-Based Compartmental Modeling for Infectious Disease Spread Part I: An Overview
- Optimized Gillespie algorithms for the simulation of Markovian epidemic processes on large and heterogeneous networks
- Phase Transitions in Cooperative Coinfections: Simulation Results for Networks and Lattices
- Co-circulation of infectious diseases on networks
- Mutually cooperative epidemics on power-law networks
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