library(ramp.xds)
library(ramp.library)
library(ramp.work) Vector Control Effect Sizes
We start with the same data set we used in History of Exposure, but now we consider how malaria has been affected by two rounds of IRS (in green).
Duration of Effects
Each round of IRS has an effect associated with it that is expected to wane over time. We use information to estimate the temporal dimensions of the effect:
p1 <- makepar_F_sharkfin(850, 180, 1/7, 1/40)
round1 <- make_function(p1)
p2 <- makepar_F_sharkfin(1215, 180, 1/7, 1/40)
round2 <- make_function(p2)
plot(tt, round1(tt)+ round2(tt), type = "l", ylab= "Coverage", xlab= "Time")
Model
Infection Dynamics
Each model asks us to make some assumptions about the parameters affecting malaria infection and immunity, including drug taking. The SIP model is explained in the documentation:
Xo = list(xi=2/365, rho=0.1)Seasonality
We need to make some kind of assumption about the form for seasonal exposure to the EIR. We’ll adjust the phase in a bit, but for now, we’re going to specify the shape. This is explained in the documentation for make_function.sin in ramp.xds (offsite).
p0 <- makepar_F_sin(floor=.1)
F_s0 = make_function(p0)
plot(tt, F_s0(tt), type = "l", ylim = c(c(0, 2)), ylab = "Seasonal Effect", xlab = "Time")
Adult Mosquitoes
Lo = list(Lambda = 36, F_season = F_s0)mod <- xds_setup(Xname = "SIP", MYZname = "SEI", Lopts = Lo)mod <- xds_solve(mod, 7*365)
mod <- last_to_inits(mod)
mod <- xds_solve(mod, Tmax=6*365, dt=30)xds_plot_aEIR(mod)
xds_plot_PR(mod)
XH <- get_XH(mod)
mean(XH$true_pr)[1] 0.2810122Estimation of Effect Size
To estimate the effect size, we must need a model with mosquito ecology and infection dynamics and a model for the