stim.generate: Generating a baseline landscape of susceptible individuals

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

Description

Generates a continuous landscape of susceptible individuals given key parameters on the median and range of dengue seroprevalence and the past distribution of recent cases

Usage

stim.generate(pastdat, sero, startweek, sgpop, unipix)

Arguments

pastdat	Data frame of the locations, numbers and timings (in weeks) of recent cases, see ?sgdat
sero	a three element vector giving the minimum, median and maximum observed seroprevalence in the landscape
startweek	the week from which the model simulation will begin
unipix	Universal pixel lookup table, see ?make.unipix

Details

This approach assumes that true seroprevalence will be higher in areas with recent dengue infection. It therefore, takes past data from before the model simulation, uses krigeing (see ?autoKrig) to generate a continuosu surface of predicted past cases, then scales these to match the range and median of the seroprevalence values provided.

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)
plot.state(stim, sgpop, unipix)

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