epiNetModules: Modules for Stochastic Network Models

Description

Stochastic network models of infectious disease epidemics simulated through the epiNet class models require a complex series of statistical modeling, random simulations, and bookkeeping. After the network model is estimated, disease is simulated upon networks using the epiNet.simTrans function. Within this function are a series of steps to initialize the simulation, and then randomly set new infections, recoveries, and vital dynamics on the network.

Addressing novel research questions will likely require modifying or expanding the existing functionality of epiNet.simTrans code. Our software design goal has been to "modularize" the internal processes within epiNet.simTrans in order to present the procedures of these network models clearly for easy editing and supplementation.

A set of related procedures are known as modules. In this help page, we present a brief overview of the module functions in the order in which they are used within epiNet.simTrans to help guide users on the "big picture" for their own code expansion. Note that these functions are not shown on the main help page index since they are not called directly by the end-user. Review the help pages for these functions for further details.

Arguments

Initialization Functions

These functions set up the nodal attributes like disease status on the network at the starting time step of disease simualtion, $t_1$. For multiple-simulation function calls, these are reset at the beginning of each individual simulation.

init.status: sets which nodes are initially infected, either through thei.numori.idsparameters.
init.inf.time: sets the time of infection for those nodes which were infected ininit.status.
init.pids: establishes persistent ID numbers for bipartite network simulations with vital dynamics.

Infection and Recovery Functions

The main disease simulation occurs at each time step given the current state of the network at that step. Infection of nodes is simulated as a function of attributes of the nodes and the dyad (e.g., the mode number or infection time of the infected node in a partnership). Recovery of nodes is likewise simulated as a function of nodal attributes of those infected nodes. These functions also conduct analysis on the network to save summary disease measures such as disease incidence.

infection: simulates disease transmission given an edgelist of serodisdant partnerships by calculating the relevant transmission and act rates for each nodal pair, and then updated the nodal attributes and summary statistics.
discord.edgelist: determines from the network object which edges are active, and then the subset of active edges which are comprised of a serodiscordant (one node susceptible and one node infected) nodal pairs.
recovery: simulates recovery from infection either to a lifelong immune state (for SIR models) or back to the susceptible state (for SIS models), as a function of the recovery rate specified in therec.rateparameter.

Vital Dynamics Functions

Vital dynamics such as birth and death processes are simulated at each time step to update entries into and exits from the network. These are used in dependent network models.

deaths.sus: randomly simulates death for susceptible status nodes given the death rate specified in theds.rateparameter. This involves deactivating susceptible nodes.
deaths.inf: randomly simulates death for infected status nodes given the death rate specified in thedi.rateparameter. This involves deactivating infected nodes.
deaths.rec: randomly simulates death for recovered status nodes given the death rate specified in thedr.rateparameter. This involves deactivating recovered nodes.
births: randomly simulates new births into the network given the current population size and the birth rate specified in theb.rateparameter. This involves adding new nodes into the network.