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EpiModel (version 0.95)

EpiModel-package: Mathematical Modeling of Infectious Disease

Description

ll{ Package: EpiModel Type: Package Version: 0.95 Date: 2013-01-22 License: GPL (>= 2) LazyLoad: yes }

Arguments

Details

The EpiModel package provides functions for building, solving, and plotting mathematical models of infectious disease. The goals of the package are to provide basic tools for modeling in multiple frameworks for pedagogical purposes, and to support users to develop and expand these tools using the package's utility functions for their own research.

EpiModel currently provides functionality for three classes of epidemic models:

  • Deterministic, compartmental epidemic models: these continuous-time models are solved using ordinary differential equations.EpiModelallows for easy specification of sensitivity models to compare multiple runs of the same model with different parameter values.
  • Individual contact models: a novel class of microsimulation models were developed to mirror the deterministic models but add random variation in all components of the transmission dynamics system, from infection to recovery to vital dynamics (births and deaths).
  • Stochastic network models: using the underlying statistical framework of dynamic exponential random graph models (ERGMs) recently developed in theStatnetsuite of software in R, our network models simulate partnership formation and dissolution stochastically according to a user-defined statistical model, as well as disease spread on that network.
Future additions to the package will expand the varieties of models within each of these classes that may be run "out-of-the-box," as well as the helper utility functions that support users' own expansion of network models specifically.

EpiModel supports three infectious disease types to be run across all of the three classes:

  • Susceptible-Infectious (SI) diseases: a two-state disease in which there is life-long infection without recovery. HIV/AIDS is one example, although for this case it is more common to model infection stages as separate compartments (forthcoming in a future release).
  • Susceptible-Infectious-Recovered (SIR) diseases: a three-stage disease in which one has life-long recovery with immunity after infection. Measles is one example, but modern models for the disease also require consideration of vaccination patterns in the population (forthcoming in a future release).
  • Susceptible-Infectious-Susceptible (SIS) diseases: a two-stage disease in which one may transition back and forth from the susceptible to infected states throughout life. Examples include bacterial sexually transmitted diseases like gonorrhea.

The core functions for parameterizing and solving the three model classes are:

  • epiDCMfor deterministic compartmental epidemic models.
  • epiICMfor individual contact epidemic models.
  • epiNet.estfor estimating the statistical models underlying partnership formation and dissolution used in stochastic network epidemic models. This function is a wrapper around theergmandstergmfunctions in theergmandtergmpackages, respectively, with additional diagnostic tables and plots useful for epidemic modeling.
  • epiNet.simNetfor simulating networks given a model fit withepiNet.est. These network simulations are used for network epidemic models in which there is no dependence between the network structure and the disease process (thus, the network structure may be fully simulated ahead of the disease simulation).
  • epiNet.simTransfor stochastic network epidemic models, with a given network model fit or set of network simulations fromepiNet.estorepiNet.simNet, respectively. For models involving dependence of disease trajectories on the network structure (e.g., disease causes death, which dissolves partnerships), it is not necessary to pre-simulate the networks since each disease simulation re-simulates the network at each time step. A help page providing an overview of the internals ofepiNet.simTransthat may be useful for adapting and expanding the software for novel research is available atepiNetModules.

References

http://www.statnet.org