est.transdist {IDSpatialStats}

Estimate transmission distance

Description

this function estimates the mean transmission distance of an epidemic when given the locations and times of symptomatic cases and the mean and standard deviation of the generation time of the infecting pathogen

Usage

est.transdist(
  epi.data,
  gen.t.mean,
  gen.t.sd,
  t1,
  max.sep,
  max.dist,
  n.transtree.reps = 100,
  theta.weights = 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). Warning: higher values of this parameter cause significant increases in computation time.

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

Value

a list containing the estimated mean distance of the transmission kernel (mu.est) and its standard deviation (sigma.est). Bounded estimates (bound.mu.est and bound.sigma.est) are also given for when the assumption of equal mean and standard deviation is violated.

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 est.wt: est.wt.matrix(), est.wt.matrix.weights()

Other transdist: est.transdist.bootstrap.ci(), est.transdist.temporal(), est.transdist.temporal.bootstrap.ci(), est.transdist.theta.weights(), 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=1.5,
                  gen.t.mean=7,
                  gen.t.sd=2,
                  min.cases=50,
                  tot.generations=12,
                  trans.kern.func=dist.func)

# Estimate mean and standara deviation of transmission kernel
b <- est.transdist(epi.data=a,
                   gen.t.mean=7,
                   gen.t.sd=2,
                   t1=0,
                   max.sep=1e10,
                   max.dist=1e10,
                   n.transtree.reps=10)
b