betaEnv.generate: Generating a baseline landscape of vector-human and...

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

Description

Generates a continuous landscape of human <-> vector contact rates given parameters of mean and variance of contact rate and the degree of spatial correlation with susceptibility

Usage

betaEnv.generate(bmean, bcorrelation, stim)

Arguments

bmean	the mean human <-> vector contact rate across the landscape
bcorrelation	the degree of correlation between human susceptibility and human <-> vector contact rate, must be in interval -1 to 1
stim	a vector of the starting immunity levels in each pixel

Details

Between the three supplied parameters this controls the intensity of transmission, its spatial variability and its spatial structure (in particularlly whether transmission intensity is most intense in areas that have experience recent transmission, or areas that have not experience recent transmission), i.e. does the outbreak happen in a new area or an existign area of high intensity transmission? The implementation is stochastic and an approximation.

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, sgpop, unipix)
par(mfrow = c(2, 1))
plot.state(stim, sgpop, unipix)
betaEnv <- betaEnv.generate(bmean = 8, bcorrelation = -1, stim)
plot.state(betaEnv, sgpop, unipix)

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