est.transdist.temporal: Change in mean transmission distance over time

In IDSpatialStats: Estimate Global Clustering in Infectious Disease

Description

Estimates the change in mean transmission distance over the duration of the epidemic by running est.trandsdist on all cases occuring up to each time point.

Usage

est.transdist.temporal(
  epi.data,
  gen.t.mean,
  gen.t.sd,
  t1,
  max.sep,
  max.dist,
  n.transtree.reps = 10,
  mean.equals.sd = FALSE,
  theta.weights = NULL,
  parallel = FALSE,
  n.cores = NULL
)

Arguments

epi.data	a three-column matrix giving the coordinates (x and y) and time of infection (t for all cases in an epidemic (columns must be in x, y, t order)
gen.t.mean	mean generation time of the infecting pathogen
gen.t.sd	standard deviation of generation time of the infecting pathogen
t1	time step to begin estimation of transmission distance
max.sep	maximum number of time steps allowed between two cases (passed to the get.transdist.theta function)
max.dist	maximum spatial distance between two cases considered in calculation
n.transtree.reps	number of time to simulate transmission trees when estimating the weights of theta (passed to the est.transdist.theta.weights function, default = 10). Higher values of this parameter cause significant increases in computation time.
mean.equals.sd	logical term indicating if the mean and standard deviation of the transmission kernel are expected to be equal (default = FALSE)
theta.weights	use external matrix of theta weights. If NULL (default) the matrix of theta weights is automatically estimated by calling the est.transdist.theta.weights function
parallel	run time steps in parallel (default = FALSE)
n.cores	number of cores to use when parallel = TRUE (default = NULL, which uses half the available cores)

Value

a numeric matrix containing the point estimate for mean transmission distance for each unique time step of the epidemic and the sample size $n$ used to make the estimate NAs are returned for time steps which contain fewer than three cases

Author(s)

John Giles, Justin Lessler, and Henrik Salje

References

Salje H, Cummings DAT and Lessler J (2016). “Estimating infectious disease transmission distances using the overall distribution of cases.” Epidemics, 17, pp. 10–18. ISSN 1755-4365, doi: 10.1016/j.epidem.2016.10.001.

See Also

Other transdist: est.transdist.bootstrap.ci(), est.transdist.temporal.bootstrap.ci(), est.transdist.theta.weights(), est.transdist(), get.transdist.theta()

Examples

     
set.seed(123)

# Exponentially distributed transmission kernel with mean and standard deviation = 100
dist.func <- alist(n=1, a=1/100, rexp(n, a)) 

# Simulate epidemic
a <- sim.epidemic(R=2,
                  gen.t.mean=7,
                  gen.t.sd=2,
                  tot.generations=7,
                  min.cases=30,
                  trans.kern.func=dist.func)

a <- a[sample(1:nrow(a), 50),] # subsample a to 50 observations

# Estimate mean transmission kernel over time
b <- est.transdist.temporal(epi.data=a,
                            gen.t.mean=7,
                            gen.t.sd=2,
                            t1=0,
                            max.sep=1e10,
                            max.dist=1e10,
                            n.transtree.reps=5,
                            mean.equals.sd=TRUE,
                            n.cores=2)
b

plot(b[,2], pch=19, col='grey', ylim=c(min(b[,2], na.rm=TRUE), max(b[,2], na.rm=TRUE)), 
     xlab='Time step', ylab='Estimated mean of transmission kernel')
abline(h=100, col='red', lty=2)
axis(3, b[,2])

low <- loess(b[,2] ~ as.vector(1:length(b[,2])))
low <- predict(low, newdata=data.frame(as.vector(1:length(b[,2]))))
lines(low, lwd=3, col='blue')

IDSpatialStats documentation built on Aug. 9, 2021, 9:08 a.m.