Parameters That Vary With the Duration of Infection

Marks transmission probabilities, act rates, or recovery rates as varying with the time since infection, for the inf.prob, inf.prob.g2, act.rate, rec.rate, and rec.rate.g2 arguments of param.net(). The values are given either by stage, with the length of each stage in time steps, or one per time step since infection. The built-in netsim() modules apply the value at each infected node's current duration of infection.

Usage

by_infection_duration(x, durations = NULL)

Arguments

x

A numeric vector of values. With durations, one value per stage of infection, optionally named for the stages (such as c(acute = 0.25, chronic = 0.02)). Without durations, one value per time step since infection: the first element applies in the first time step of an infection, the second in the second, and so on, and the last element carries forward for the rest of the infection.

durations

Optional numeric vector of the same length as x, giving the length of each stage in time steps. Every stage but the last must last a whole number of time steps, one or more. The last stage lasts until the infection ends, so its duration must be Inf; for a value that changes after a finite last stage, add a stage with the value that follows.

Value

An object of class by_infection_duration: the numeric vector of values x, with the stage durations in its durations attribute. Without durations, every stage lasts one time step but the last.

Details

A duration of infection is the number of time steps since the infected node was infected, counted from 1: a node infected at time step 10 is in its first step of infection at time steps 10 and 11, its second at time step 12, and so on. For transmission, the duration is that of the infected partner; for recovery, that of the recovering node.

The two forms describe the same thing. by_infection_duration(c(acute = 0.25, chronic = 0.02), durations = c(10, Inf)) gives 0.25 in the first ten time steps of an infection and 0.02 from the eleventh on, which can represent an acute stage of high infectiousness; by_infection_duration(c(rep(0.25, 10), 0.02)) is the same parameter given per time step. The stage form suits parameters reported by stage of infection; the per-step form suits a profile computed elsewhere, such as an infectiousness curve.

This is variation over the course of each infection, not over calendar time: to change a parameter at a given time step of the simulation, use scenarios or parameter updaters (see vignette("model-parameters", package = "EpiModel")).

In multi-layer models, an entry of a multilayer() parameter may itself be a by_infection_duration object: inf.prob = multilayer(by_infection_duration(c(0.5, 0.1)), 0.05). Arithmetic on the object, such as x * 2, scales the values and keeps the stages.

With the built-in modules, a plain vector passed to these arguments stops netsim() with an error, since a plain vector carries no record of what its positions mean; custom modules keep their own reading of plain vectors, such as values by group or by layer. Parameter tables cannot represent these objects yet, so param.net_to_table() stops on them.

See also

param.net(), multilayer().

Examples

# An acute stage of ten time steps with a higher transmission probability
inf.prob <- by_infection_duration(c(acute = 0.25, chronic = 0.02),
                                  durations = c(10, Inf))
inf.prob
#> Values by stage of infection (time steps since infection):
#>    Stage Steps Value
#>    acute  1-10  0.25
#>  chronic   11+  0.02

# Recovery impossible in the first 20 time steps of an infection, then
# certain
by_infection_duration(c(0, 1), durations = c(20, Inf))
#> Values by stage of infection (time steps since infection):
#>  Stage Steps Value
#>      1  1-20     0
#>      2   21+     1

# One value per time step, here an infectiousness profile
by_infection_duration(round(dgamma(1:15, shape = 3, rate = 0.5), 3))
#> Values by duration of infection (time steps since infection), the last carried forward:
#>  [1] 0.038 0.092 0.126 0.135 0.128 0.112 0.092 0.073 0.056 0.042 0.031 0.022
#> [13] 0.016 0.011 0.008

param <- param.net(inf.prob = inf.prob, act.rate = 1)
param
#> Model Parameters
#> ---------------------------
#> inf.prob = 
#> by_infection_duration(c(acute = 0.25, chronic = 0.02), durations = c(10, Inf))
#> act.rate = 1

# \donttest{
nw <- network_initialize(n = 100)
est <- netest(nw, formation = ~edges, target.stats = 50,
              coef.diss = dissolution_coefs(~offset(edges), 10),
              verbose = FALSE)
#> Starting simulated annealing (SAN)
#> Iteration 1 of at most 4
#> Finished simulated annealing
#> Starting maximum pseudolikelihood estimation (MPLE):
#> Obtaining the responsible dyads.
#> Evaluating the predictor and response matrix.
#> Maximizing the pseudolikelihood.
#> Finished MPLE.
sim <- netsim(est, param, init.net(i.num = 10),
              control.net(type = "SI", nsteps = 25, nsims = 1,
                          verbose = FALSE))
tm <- get_transmat(sim)
table(tm$infDur, tm$transProb)
#>     
#>      0.02 0.25
#>   1     0    6
#>   2     0    5
#>   3     0    4
#>   4     0    4
#>   5     0    4
#>   6     0    5
#>   7     0    5
#>   8     0    4
#>   9     0    2
#>   10    0    1
#>   12    1    0
#>   15    1    0
#>   33    1    0
# }