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  • Last update: 5 years ago
  • Total downloads: 147
  • Operating system: WinXP, Win2003, Win2000, Win Vista, Windows 7
  • Publisher: tjhladish
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windows default iconEpiFire Publisher's description

EpiFire is a C++ applications programming interface

EpiFire is a C++ applications programming interface (API) that does two things:

* Model the spread of an infectious disease in a population
* Generate and manipulate networks of nodes and edges

While the network code can be used independently from the epidemiological code and vice versa—they are conceptually and functionally distinct—from the beginning, the libraries were developed to be compatible with each other. What EpiFire excels at is simulating the stochastic spread of disease on contact networks.
EpiFire’s finite, stochastic epidemic simulators:

* Continuous time
* Very fast—simulations of 1 million individuals in < 1 second on a modern desktop computer
* Closely match traditional compartmental (diff eq) models
* Infectious period and time between transmission events are exponential random variables


* No real time. Who gets infected is predicted, but not when or in what order.
* Network-based
* Still quite fast (500,000 individuals in < 1 second); usually generating the network takes longer than running the simulation
* Important parameters include the network’s degree distribution and transmissibility (probability of transmission)


* Discrete time, and specific chains of transmission are predicted
* Network-based
* Parameters include number of time steps in infectious period, the per-time step transmissibility, and the degree distribution
* Slower: with an infectious period of 10 and a mean degree around 4, simulations with 50,000 people take about 1 second

The Network, Node, and Edge classes provide several dozen methods for constructing, manipulating, and describing networks. The focus here is on randomly generated networks according to some degree distribution. We provide an implementation of the Erdos-Renyi algorithm (which generates Poisson random networks), as well as the more generic configuration model, which an be used to generate random networks with Poisson, exponential, scale-free, or arbitrary degree distributions.

System Requirements:

No special requirements.
Program Release Status: Minor Update
Program Install Support: Install and Uninstall

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