case.backtrack.time: Establishing model initial conditions

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

Description

Generates the starting values for each of the model states given reported cases at time = 0

Usage

case.backtrack.time(casedat, startweek, unipix, stim, betaEnv, mospdeath,
  hometime = 0.2)

Arguments

casedat	Data frame of the locations, numbers and timings (in weeks) of recent cases, see ?sgdat
startweek	Time (in weeks) at which the model simulation is to begin
unipix	Universal pixel lookup table, see ?make.unipix
stim	Starting susceptibiltiy landscape, see ?stim.generate
betaEnv	Starting human <-> mosquito contact rate landscape, see ?betaEnv.generate
mospdeath	single value between 0 and 1, the daily probability of mosquito mortality
hometime	Parameter that controls spatial concentration of movement, defaults to 0.2

Details

Uses supplied relationships to work out how many Infectious, exposed, etc are needed over time to generate the supplied reported detected human cases at t= 0. These are then used for model burin.

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)
mospdeath <- 0.1
startTable <- case.backtrack.time(sgdat, startweek, unipix, stim, betaEnv, mospdeath, hometime = 0.5)

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