# EpiModel-package: Mathematical Modeling of Infectious Disease Dynamics In EpiModel: Mathematical Modeling of Infectious Disease Dynamics

## Description

 Package: EpiModel Type: Package Version: 2.2.0 Date: 2021-11-09 License: GPL-3 LazyLoad: yes

## Details

The EpiModel software package provides tools for building, solving, and visualizing mathematical models of infectious disease dynamics. These tools allow users to simulate epidemic models in multiple frameworks for both pedagogical purposes ("base models") and novel research purposes ("extension models").

## Model Classes and Infectious Disease Types

EpiModel provides functionality for three classes of epidemic models:

• Deterministic Compartmental Models: these continuous-time models are solved using ordinary differential equations. EpiModel allows for easy specification of sensitivity analyses to compare multiple scenarios f the same model across different parameter values.

• Stochastic Individual Contact Models: a novel class of individual-based, microsimulation models that were developed to add random variation in all components of the transmission system, from infection to recovery to vital dynamics (arrivals and departures).

• Stochastic Network Models: with the underlying statistical framework of temporal exponential random graph models (ERGMs) recently developed in the Statnet suite of software in R, network models over epidemics simulate edge (e.g., partnership) formation and dissolution stochastically according to a specified statistical model, with disease spread across that network.

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

• Susceptible-Infectious (SI): a two-state disease in which there is life-long infection without recovery. HIV/AIDS is one example, although for this case it is common to model infection stages as separate compartments.

• Susceptible-Infectious-Recovered (SIR): 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.

• Susceptible-Infectious-Susceptible (SIS): 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.

These basic disease types may be extended in any arbitrarily complex way to simulate specific diseases for research questions.

## Model Parameterization and Simulation

EpiModel uses three model setup functions for each model class to input the necessary parameters, initial conditions, and control settings:

• `param.dcm`, `param.icm`, and `param.net` are used to input epidemic parameters for each of the three model classes. Parameters include the rate of contacts or acts between actors, the probability of transmission per contact, and recovery and demographic rates for models that include those transitions.

• `init.dcm`, `init.icm`, and `init.net` are used to input the initial conditions for each class. The main conditions are limited to the numbers or, if applicable, the specific agents in the population who are infected or recovered at the simulation outset.

• `control.dcm`, `control.icm`, and `control.net` are used to specify the remaining control settings for each simulation. The core controls for base model types include the disease type, number of time steps, and number of simulations. Controls are also used to input new model functions (for DCMs) and new model modules (for ICMs and network models) to allow the user to simulate fully original epidemic models in EpiModel. See the documentation for the specific control functions help pages.

With the models parameterized, the functions for simulating epidemic models are:

• `dcm` for deterministic compartmental models.

• `icm` for individual contact models.

• Network models are simulated in a three-step process:

1. `netest` estimates the statistical model for the network structure itself (i.e., how partnerships form and dissolve over time given the parameterization of those processes). This function is a wrapper around the `ergm` and `stergm` functions in the `ergm` and `tergm` packages. The current statistical framework for model simulation is called "egocentric inference": target statistics summarizing these formation and dissolution processes collected from an egocentric sample of the population.

2. `netdx` runs diagnostics on the dynamic model fit by simulating the base network over time to ensure the model fits the targets for formation and dissolution.

3. `netsim` simulates the stochastic network epidemic models, with a given network model fit in `netest`. Here the function requires this model fit object along with the parameters, initial conditions, and control settings as defined above.

## References

The EpiModel website is at http://www.epimodel.org/, and the source code is at https://github.com/EpiModel/EpiModel. Bug reports and feature requests are welcome.

Our primary methods paper on EpiModel is published in the Journal of Statistical Software. If you use EpiModel for any research or teaching purposes, please cite this reference:

Jenness SM, Goodreau SM, and Morris M. EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks. Journal of Statistical Software. 2018; 84(8): 1-47. doi: 10.18637/jss.v084.i08.

We have also developed two extension packages for modeling specific disease dynamics. For HIV and bacterial sexually transmitted infections, we have developed `EpiModelHIV`, which is available on Github at https://github.com/EpiModel/EpiModelHIV. For COVID-19, we have developed `EpiModelCOVID`, which is available at https://github.com/EpiModel/EpiModelCOVID.

EpiModel documentation built on Nov. 10, 2021, 1:09 a.m.