cystiSim (version 0.1.0)

cystiRun: 'cystiRun' object

Description

Functions to initiate, update and explore 'cystiRun' objects. A 'cystiRun' object corresponds to a single run of a cystiSim model.

Usage

initiate(man, pig, ph2m, pl2m, m2p, e2p, age.coef = c(0, 0), slaughter = slaughter_nbinom, slaughter.args = list(min = 6, max = 36, size = 0.70, mu = 80))
"update"(object, n = 1200, verbose = TRUE, ...)
"print"(x, from = 200, to = NA, ...)
"plot"(x, y = NULL, show = c("PC", "PR", "HT", "EN"), start = 0, from = 1, to = NA, ...) 
prevalence(z)

Arguments

man

Human population dataframe.

pig

Pig population dataframe.

ph2m

Pig (Heavy infection) to Man transmission probability.

pl2m

Pig (Light infection) to Man transmission probability.

m2p

Man to Pig transmission probability.

e2p

Environment to Pig transmission probability.

age.coef

Optional intercept and slope for the association between age and taeniosis.

slaughter

Function that defines the slaughter probability of pigs.

slaughter.args

Arguments to be passed to the slaughter function.

object

Object of class 'cystiRun'.

n

Number of iterations (months).

verbose

If TRUE, a progress bar is shown.

x

Object of class 'cystiRun'.

y

Currently ignored.

z

Vector of infection indicators.

start

Origin of plot.

from

First iteration to be used in output.

to

Last iteration to be used in output. The default value NA corresponds to the last available iteration.

show

Which output should be plotted?

...

Arguments to be passed on to generic function.

Value

initiate and update return an object of S3 class 'cystiRun'.

See Also

Intervention functions: do_man_mda, do_pig_mda, do_pig_vac, do_pig_mda_vac

Examples

## we will use the built-in Mbeya dataset
prevalence(pig_mbeya$cysti)
prevalence(man_mbeya$taenia)

## define transmission probabilities
ph2m <- 0.000174918
pl2m <- 0.000149501
m2p  <- 6.85E-05
e2p  <- 0.00022611

## first initiate the 'cystiRun' object
mod <- initiate(man_mbeya, pig_mbeya, ph2m, pl2m, m2p, e2p)

## update the model 240 cycles (=20 years)
## this is a burn-in period, needed to obtain steady state
mod <- update(mod, 240)

## apply human mass drug administration
mod <- do_man_mda(mod, efficacy = 0.70, coverage = 0.80)

## apply pig mass drug administration
mod <- do_pig_mda(mod, efficacy = 0.90, coverage = 0.90)

## update the model 120 more cycles (=10 years)
mod <- update(mod, 120)

## plot the cycles
plot(mod, from = 200, start = 40)