epydemic: Epidemic simulations on networks in Python

Vision: A common platform for epidemic simulation

epydemic aims to provide a common framework for the scalable and efficient simulation of epidemic processes.

What are epidemics?

Many common processes can be treated as epidemic, where some condition spreads between the nodes of a network. The most familiar example is a transmissible disease such as flu, where un-infected people become infected through contact with infceted people. Mathematically the population of people form a network (or graph), where the nodes represent people and the edges represent possible contacts between them. An epidemic begins when one or more person is initially infected and begins to spread the disease to his neighbours with some probability. Depending on factors such as the way the network is connected, the probability of a contact giving rise to an infection, the rate at which people recover from the illness and so forth, the disease may come to infect none, some, or all of the people.

It turns out that a lot of interesting processes work mathematically like epidemics. As well as diseases, these include the spread of computer viruses, the spread of rumours on social media (or in the real world), the spread of genetic mutations, and even how soils drain.

Simulating epidemic processes is therefore something a lot of people want to do frequently. However, the actual process of setting up and simulating an epidemic is complicated, and this is especially true when we want to do lots of repetitions of experiments to explore how different parameters affect the way an epidemic behaves.

What is epydemic?

epydemic is a simulation framework for epidemic processes. It aims to provide the common simulation approaches used in the scientific literature, together with a small set of “common epidemics” that can form the basis for experimentation. epydemic is built on top of epyc, an experiment management package that handles running different simulations, either on a single machine or in the cloud.

Current features

  • Compatible with both Python 2.7 and Python 3.7 and later
  • Supports both discrete-time synchronous dynamics and continuous-time stochastic dynamics (Gillespie simulation)
  • Uses networkx for representing disease networks, allowing random networks to be generated easily and real networks to be impported from outside sources
  • Support for a generic compartmented model of disease, allowing more complex diseases to be described
  • A single model description drives all dynamics – no re-writing
  • Susceptible-Infected-Removed (SIR) and Susceptible-Infected-Susceptible (SIS) models built-in, with either stochastic or fixed recovery times
  • Integrated with epyc’s labs and experiments, including execution in parallel on compute clusters