hum.inf.prob: Calculate probability of human infection

In obrady/SpatialDengue: Fits and simulates a spatial model of the vector-borne disease dengue

Description

Calculates the pixel-specific probability of human infection taking into account their time allocation in different pixels with or without infectious mosquitoes

Usage

hum.inf.prob(mos_I, betaEnv, unipix, mm_list2, mm_list_cum)

Arguments

mos_I	vector, Infectious mosquitoes in each pixel
betaEnv	Starting human <-> mosquito contact rate landscape, see ?betaEnv.generate
unipix	Universal pixel lookup table, see ?make.unipix
mm_list2	List of human movement matrices adjusted by the hometime parameter, see ?move.matrix.gene and ?DEN.spatial
mm_list_cum	Matrix of patch effective population size (including both resident population and those who move there during the day)

Details

Calculates proabbility of infection of mosquitoes resident in each patch (mosquitoes don't move around) when infectious humans are moving around. Infectiousness of humans is also dependent on their time since infection, peaking when symptomatic. Returns a vector with length equal to the number of patches giving the proabbility of infection of each susceptible mosquito

Examples

data(sgdat)
data(sgpop)
sgpop <- pop.process(sgpop, agg = 10)
unipix <- make.unipix(sgpop)
sgdat <- data.frame(sgdat, patchID = apply(cbind(sgdat[, 3:2]), 1, pix.id.find, unipix))
sero <- c(0.577, 0.659, 0.814)
startweek <- 40
stim <- stim.generate(sgdat, sero, startweek, unipix)
betaEnv <- betaEnv.generate(bmean = 8, blogvariance = log(2), bcorrelation = -1, stim, unipix)
mos_I <- runif(nrow(unipix), min = 0, max = 20)
mlist <- move.matrix.gene(unipix, hometime = 0.2)
HIP <- hum.inf.prob(mos_I, betaEnv, unipix, mlist[[1]], mlist[[2]])
plot.state(HIP, sgpop, unipix)

obrady/SpatialDengue documentation built on Nov. 27, 2020, 12:13 p.m.