Stochastic network models of infectious disease in EpiModel require
statistical modeling of networks, simulation of those networks forward
through time, and simulation of epidemic dynamics on top of those evolving
networks. The netsim function handles both the network and
epidemic simulation tasks. Within this function are a series of modules that
initialize the simulation and then simulate new infections, recoveries, and
demographics on the network. Modules also handle the resimulation of the
network and some bookkeeping calculations for disease prevalence.
Writing original network models that expand upon our "base" model set will
require modifying the existing modules or adding new modules to the workflow
in netsim. The existing modules may be used as a template for
replacement or new modules.
This help page provides an orientation to these module functions, in the
order in which they are used within netsim, to help guide users
in writing their own functions. These module functions are not shown
on the help index since they are not called directly by the end-user. To
understand these functions in more detail, review the separate help pages
listed below.
This function sets up nodal attributes, like disease status, on the network at the starting time step of disease simulation, \(t_1\). For multiple-simulation function calls, these are reset at the beginning of each individual simulation.
initialize.net: sets up the main netsim_dat data
structure used in the simulation, initializes which nodes are infected
(via the initial conditions passed in init.net), and
simulates a first time step of the networks given the network model
fit from netest.
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 edges. Recovery of nodes is likewise simulated as a function of nodal attributes of those infected nodes. These functions also calculate summary flow measures such as disease incidence.
infection.net: simulates disease transmission given an
edgelist of discordant partnerships by calculating the relevant
transmission and act rates for each edge, and then updating the nodal
attributes and summary statistics.
recovery.net: 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 parameters
specified in param.net.
Demographics such as arrival and departure processes are simulated at each time step to update entries into and exits from the network. These are used in epidemic models with network feedback, in which the network is resimulated at each time step to account for the nodal changes affecting the edges.
departures.net: randomly simulates departure for nodes
given their disease status (susceptible, infected, recovered), and
their group-specific departure rates specified in
param.net. Departures involve deactivating nodes.
arrivals.net: randomly simulates new arrivals into the
network given the current population size and the arrival rate
specified in the a.rate parameters. This involves adding new
nodes into the network.
In dependent network models, the network object is resimulated at each time step to account for changes in the size of the network (changed through entries and exits), and the disease status of the nodes.
resim_nets: resimulates the network object one time step
forward given the set of formation and dissolution coefficients
estimated in netest.
Network simulations require bookkeeping at each time step to calculate the summary epidemiological statistics used in the model output analysis.
prevalence.net: calculates the number in each disease
state (susceptible, infected, recovered) at each time step for those
active nodes in the network. If the epi.by control is used, it
calculates these statistics by a set of specified nodal attributes.
verbose.net: summarizes the current state of the
simulation and prints this to the console.
If epidemic type is supplied within control.net,
EpiModel defaults each of the base epidemic and demographic modules described
above (arrivals.FUN, departures.FUN, infection.FUN, recovery.FUN) to the
correct .net function based on variables passed to param.net
(e.g. num.g2, denoting population size of group two, would select the
two-group variants of the aforementioned modules). Two-group modules are
denoted by a .2g affix (e.g., recovery.2g.net)