C2pop: Adapt a Contact Matrix to Population Fractions

In hhh4contacts: Age-Structured Spatio-Temporal Models for Infectious Disease Counts

Adapt a Contact Matrix to Population Fractions

Description

Experimental function, which tries to adjust a given contact matrix such that the stationary distribution of its row-normalized version (i.e., the transition matrix) becomes approximately equal to a prespecified probability vector.

Usage

C2pop(C, target, eps = 0.001, iter.max = 100)

Arguments

C	a square numeric (contact) matrix.
target	the stationary probability vector to approximate.
eps	the tolerated mean absolute difference between the target probabilities and the stationary distribution of the adapted, normalized contact matrix.
iter.max	maximum number of iterations (guard against infinite loop).

Value

the adapted, normalized contact matrix.

Author(s)

Leonhard Held (original) and Sebastian Meyer (this implementation)

See Also

adaptP for an alternative method.

Examples

GROUPING <- c(1, 2, 2, 4, 4, 2)
C <- contactmatrix(grouping = GROUPING)
popBErbyg <- aggregateCountsArray(pop2011, dim = 2, grouping = GROUPING)
popfracs <- prop.table(colSums(popBErbyg))
## adapt 'C' to the given population fractions
Cpop <- C2pop(C, popfracs)
## compare the stationary distributions
compstat <- cbind(before = stationary(C/rowSums(C)), popBE = popfracs,
                  after = stationary(Cpop))
matplot(compstat, type="b", lty=1, ylim=c(0, max(compstat)),
        xlab="age group", ylab="population fraction")
## compare the normalized contact matrices
print(plotC(C/rowSums(C), main="original", at=seq(0,0.6,length.out=17)),
      split=c(1,1,2,1), more=TRUE)
print(plotC(Cpop, main="adapted", at=seq(0,0.6,length.out=17)),
      split=c(2,1,2,1), more=FALSE)

hhh4contacts documentation built on Oct. 8, 2024, 1:08 a.m.