import {hero, galleryData} from "./home/js/hero.js"
import {resources} from "./home/js/resources.js"
import {playground} from "./home/js/playground.js"
import {ecosystem} from "./home/js/ecosystem.js"
import {literature} from "./home/js/literature.js"
L = FileAttachment("home/literature.json").json()
gallery = galleryData()

hero(L, gallery)

resources(gallery)

playground()

Get started

A learning path for network models

EpiModel supports several model classes, but its distinctive contribution is stochastic network models built on temporal exponential random graph models (ERGMs). This path is for people who want to build network-based epidemic models with it.

1. 1Beginning
   • Intro to EpiModel vignette
   • The core functions: netest, netdx, and netsim
   • The Journal of Statistical Software methods paper
   • NME course, NME-I: Foundations
2. 2Intermediate
   • NME course, NME-II: Applications
   • EpiModel Gallery: worked examples and model templates, in the order they build on each other
3. 3Advanced
   • Package vignettes on parameters, network objects, and attributes
   • EpiModelHPC and slurmworkflow for running models on a cluster
   • The EpiModelHIV and EpiModelCOVID research packages

ecosystem()

literature(L)

Get help

Ask questions and report bugs on GitHub.

Cite EpiModel

Jenness SM, Goodreau SM, Morris M. EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks. Journal of Statistical Software. 2018; 84(8): 1-47. DOI

Who we are

EpiModel is developed by the EpiModel Research Lab at Emory University with collaborators at the University of Washington. The project page lists our funding.