Title: Agent-Based Model for Taenia_solium Transmission and Control
Version: 0.1.0
Date: 2016-05-15
Author: Brecht Devleesschauwer [aut, cre], Uffe Christian Braae [aut]
Maintainer: Brecht Devleesschauwer <brechtdv@gmail.com>
Description: The cystiSim package provides an agent-based model for Taenia solium transmission and control. cystiSim was developed within the framework of CYSTINET, the European Network on taeniosis/cysticercosis, COST ACTION TD1302.
URL: https://github.com/brechtdv/cystiSim
BugReports: https://github.com/brechtdv/cystiSim/issues
Depends: R (≥ 3.3.0), ggplot2
Imports: magrittr, knitr, graphics, grDevices, stats, utils
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
LazyData: true
NeedsCompilation: no
Packaged: 2016-05-15 13:00:04 UTC; UGent
Repository: CRAN
Date/Publication: 2016-05-15 21:46:33

Agent-based model for Taenia solium transmission and control.

Description

The cystiSim model allows simulating Taenia solium taeniosis/cysticercosis transmission in a virtual population of humans and pigs. It also allows evaluating the possible effects of human mass drug administration, pig mass drug administration, and pig vaccination. cystiSim was developed within the framework of CYSTINET, the European Network on taeniosis/cysticercosis, COST ACTION TD1302 (http://www.cystinet.org/).

Details

Package: cystiSim
Version: 0.1.0
Date: 2016-05-15
Authors: Brecht Devleesschauwer, Uffe Christian Braae
Maintainer: brechtdv@gmail.com
URL: https://github.com/brechtdv/cystiSim
BugReports: https://github.com/brechtdv/cystiSim/issues
Depends: R (>= 3.3.0), ggplot2
Imports: magrittr, knitr
License: GPL (>= 2)
LazyLoad: yes

Available functions in the cystiSim package:

baseline
random_baseline_man Generate a random baseline human population.
random_baseline_pig Generate a random baseline pig population.
model—S3 class 'cystiRun'
fit Fit parameters of a cystiRun model.
initiate Initiate a cystiRun model.
update Update a cystiRun model.
interventions
do_man_mda Do human mass drug administration.
do_pig_mda Do pig mass drug administration.
do_pig_vac Do pig vaccination.
do_pig_mda_vac Do pig mass drug administration AND vaccination.
simulate—S3 class 'cystiSim'
cystiSim Simulate multiple cystiRun models.
report Generate a PDF report for a cystiSim object.
elim Derive when elimination occurred in a cystiSim object.

For more details and examples, please visit the cystiSim Wiki pages on https://github.com/brechtdv/cystiSim/wiki.

Random baseline populations

Description

Generate random baseline human and pig populations.

Usage

random_baseline_man(n, p)
random_baseline_pig(n, p, p.high)

Arguments

n

Number of individuals to simulate.

p

Proportion of individuals infected with a mature parasite.

p.high

Proportion of infected pigs with high infection intensity.

Note

For more details and examples, please visit the cystiSim Wiki pages on https://github.com/brechtdv/cystiSim/wiki.

Author(s)

brechtdv@gmail.com

'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))

## S3 method for class 'cystiRun'
update(object, n = 1200, verbose = TRUE, ...)

## S3 method for class 'cystiRun'
print(x, from = 200, to = NA, ...)

## S3 method for class 'cystiRun'
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'.

Note

For more details and examples, please visit the cystiSim Wiki pages on https://github.com/brechtdv/cystiSim/wiki.

Author(s)

brechtdv@gmail.com

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)

'cystiSim' object

Description

Functions to initiate and explore 'cystiSim' objects. A 'cystiSim' object corresponds to multiple runs of a cystiSim model.

Usage

cystiSim(n = 100, mod, main = NULL)

report(x, ...)

## S3 method for class 'cystiSim'
print(x, ...)

## S3 method for class 'cystiSim'
summary(object, round = 3, ...)

## S3 method for class 'cystiSim'
plot(x, y, annotate = TRUE, ...)

## S3 method for class 'cystiSim'
report(x, name = "cystiSim", ...)

## S3 method for class 'cystiSim'
elim(x, show = c("m", "y"), ...)

Arguments

n

Number of iterations (months).

mod

cystiSim model.

main

cystiSim model label.

x

Object of class 'cystiSim'.

object

Object of class 'cystiSim'.

y

Currently ignored.

round

Number of decimal digits to be printed.

annotate

Should plot be annotated with summary information?

name

Report name.

show

Show time till elimination in terms of months or years?

...

Other arguments to be passed to generics.

Value

cystiSim returns an object of S3 class 'cystiSim'.

Note

For more details and examples, please visit the cystiSim Wiki pages on https://github.com/brechtdv/cystiSim/wiki.

Author(s)

brechtdv@gmail.com

See Also

cystiRun

Examples

## Not run: 

## 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

## set seed for reproducibility
set.seed(264)

## need to define coverage and efficacy of all interventions
cov_man_mda <- 0.80
cov_pig_mda <- 0.90
cov_pig_vac <- NULL
eff_man_mda <- 0.70
eff_pig_mda <- 0.90
eff_pig_vac <- NULL

## run the simulations
sim <-
cystiSim(
  n = 10,
  main = "example",
  mod = {
initiate(man_mbeya, pig_mbeya, ph2m, pl2m, m2p, e2p) %>%
  update(240) %>%
  do_man_mda(coverage = cov_man_mda, efficacy = eff_man_mda) %>%
  do_pig_mda(coverage = cov_pig_mda, efficacy = eff_pig_mda) %>%
  update(120)
  }
)

## summarize results
summary(sim)

## plot simulations (mean and uncertainty interval)
plot(sim)

## create PDF report and plot
report(sim)

## End(Not run)

Do human mass drug administration(MDA)

Description

Intervention function that mimics the possible effects of human mass drug administration.

Usage

do_man_mda(x, coverage, efficacy, min.age = 0, max.age = Inf)

Arguments

x

cystiRun object.

coverage

Presumed coverage of drug administration within eligible population, expressed as a decimal value.

efficacy

Presumed efficacy of tapeworm treatment, expressed as a decimal value.

min.age

Minimum age for MDA, in months. Defaults to 0, i.e., no lower age limit.

max.age

Maximum age for MDA, in months. Defaults to Inf, i.e., no upper age limit.

Note

For more details and examples, please visit the cystiSim Wiki pages on https://github.com/brechtdv/cystiSim/wiki.

Author(s)

brechtdv@gmail.com

See Also

Other interventions: do_pig_mda, do_pig_vac, do_pig_mda_vac

Do pig mass drug administration (MDA)

Description

Intervention function that mimics the possible effects of pig mass drug administration.

Usage

do_pig_mda(x, coverage, efficacy, immunity = 3, min.age = 1, max.age = Inf)

Arguments

x

cystiRun object.

coverage

Presumed coverage of drug treatment within eligible population, expressed as a decimal value.

efficacy

Presumed efficacy of drug treatment, expressed as a decimal value.

immunity

Presumed duration of immunity following drug treatment of an infected pig. Defaults to 3 months.

min.age

Minimum age for drug treatment, in months. Defaults to 1 month.

max.age

Maximum age for drug treatment, in months. Defaults to Inf, i.e., no upper age limit.

Note

For more details and examples, please visit the cystiSim Wiki pages on https://github.com/brechtdv/cystiSim/wiki.

Author(s)

brechtdv@gmail.com

See Also

Other interventions: do_man_mda, do_pig_vac, do_pig_mda_vac

Do pig mass drug administration (MDA) and vaccination

Description

Intervention function that mimics the possible effects of combined pig mass drug administration and vaccination.

Usage

do_pig_mda_vac(x, coverage, efficacy.mda, efficacy.vac,
               immunity.mda = 3, immunity.vac = Inf, interval = 4,
               min.age = 1, max.age = Inf)

Arguments

x

cystiRun object.

coverage

Presumed coverage within eligible population, expressed as a decimal value.

efficacy.mda

Presumed efficacy of drug treatment, expressed as a decimal value.

efficacy.vac

Presumed efficacy of vaccine, expressed as a decimal value.

immunity.mda

Presumed duration of immunity following drug treatment of an infected pig. Defaults to 3 months.

immunity.vac

Presumed duration of immunity following successful vaccination. Defaults to Inf.

interval

Maximum interval between two consecutive shots for immunity. Defaults to 4 months.

min.age

Minimum age for intervention, in months. Defaults to 1 month.

max.age

Maximum age for intervention, in months. Defaults to Inf, i.e., no upper age limit.

Note

For more details and examples, please visit the cystiSim Wiki pages on https://github.com/brechtdv/cystiSim/wiki.

Author(s)

brechtdv@gmail.com

See Also

Other interventions: do_man_mda, do_pig_mda, do_pig_vac

Do pig vaccination

Description

Intervention function that mimics the possible effects of pig vaccination.

Usage

do_pig_vac(x, coverage, efficacy, immunity = Inf, interval = 4,
           min.age = 1, max.age = Inf)

Arguments

x

cystiRun object.

coverage

Presumed coverage of vaccination within eligible population, expressed as a decimal value.

efficacy

Presumed efficacy of vaccine, expressed as a decimal value.

immunity

Presumed duration of immunity following successful vaccination. Defaults to Inf.

interval

Maximum interval between two consecutive shots for immunity. Defaults to 4 months.

min.age

Minimum age for vaccination, in months. Defaults to 1 month.

max.age

Maximum age for vaccination, in months. Defaults to Inf, i.e., no upper age limit.

Note

For more details and examples, please visit the cystiSim Wiki pages on https://github.com/brechtdv/cystiSim/wiki.

Author(s)

brechtdv@gmail.com

See Also

Other interventions: do_man_mda, do_pig_mda, do_pig_mda_vac

Fit cystiSim parameters

Description

This function simulates random cystiSim parameters, and return those scenarios that result in a baseline prevalence close to the specified target.

Usage

fit(n.sim, n.update, target, limit,
    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))

Arguments

n.sim

Desired number of retained scenarios.

n.update

Number of updates of the baseline model.

target

Named list of target prevalences for ht, pc and/or pi

limit

Maximum tolerated deviance.

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.

Details

The abbreviations used:

Note

For more details and examples, please visit the cystiSim Wiki pages on https://github.com/brechtdv/cystiSim/wiki.

Author(s)

brechtdv@gmail.com

Mbeya human population

Description

Default baseline dataframe.

Usage

data("man_mbeya")

Format

A data frame with 6000 observations on the following 7 variables.

age

a numeric vector

sex

a factor with levels female male

taenia

a numeric vector

taenia_immature

a numeric vector

time_since_infection

a numeric vector

environment

a numeric vector

time_since_contamination

a numeric vector

Note

For more details and examples, please visit the cystiSim Wiki pages on https://github.com/brechtdv/cystiSim/wiki.

Mbozi human population

Description

Default baseline dataframe.

Usage

data("man_mbozi")

Format

A data frame with 6000 observations on the following 7 variables.

age

a numeric vector

sex

a factor with levels female male

taenia

a numeric vector

taenia_immature

a numeric vector

time_since_infection

a numeric vector

environment

a numeric vector

time_since_contamination

a numeric vector

Note

For more details and examples, please visit the cystiSim Wiki pages on https://github.com/brechtdv/cystiSim/wiki.

Mbeya pig population

Description

Default baseline dataframe.

Usage

data("pig_mbeya")

Format

A data frame with 498 observations on the following 8 variables.

age

a numeric vector

cysti

a numeric vector

cysti_immature

a numeric vector

time_since_infection

a numeric vector

intensity

a factor with levels 0 H L

immunity

a numeric vector

time_since_vaccination

a logical vector

slaughtered

a numeric vector

Note

For more details and examples, please visit the cystiSim Wiki pages on https://github.com/brechtdv/cystiSim/wiki.

Mbozi pig population

Description

Default baseline dataframe.

Usage

data("pig_mbozi")

Format

A data frame with 498 observations on the following 8 variables.

age

a numeric vector

cysti

a numeric vector

cysti_immature

a numeric vector

time_since_infection

a numeric vector

intensity

a factor with levels 0 H L

immunity

a numeric vector

time_since_vaccination

a logical vector

slaughtered

a numeric vector

Note

For more details and examples, please visit the cystiSim Wiki pages on https://github.com/brechtdv/cystiSim/wiki.

Pig slaughter functions

Description

These functions simulate age-dependent slaughter of pigs. The default function is slaughter_nbinom.

Usage

slaughter_binom(age, min, max, p)
slaughter_nbinom(age, min, max, size, mu)

Arguments

age

Age of the pigs.

min

Minimum age at slaughter, i.e., Pr(slaughter<min)=0.

max

Age at which all pigs are definitely slaughtered, i.e., Pr(slaughter>=max)=1.

p

Binomial probability of slaughter.

size

Size of Negative Binomial distribution of age-specific slaughter probability.

mu

Mean of Negative Binomial distribution of age-specific slaughter probability.

Note

For more details and examples, please visit the cystiSim Wiki pages on https://github.com/brechtdv/cystiSim/wiki.

Author(s)

brechtdv@gmail.com