Type:	Package
Title:	Discrete Time Simulation of Mosquito-Borne Pathogen Transmission
Version:	0.1.2
Description:	Provides a framework based on S3 dispatch for constructing models of mosquito-borne pathogen transmission which are constructed from submodels of various components (i.e. immature and adult mosquitoes, human populations). A consistent mathematical expression for the distribution of bites on hosts means that different models (stochastic, deterministic, etc.) can be coherently incorporated and updated over a discrete time step.
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.2.1.9000
Imports:	abind, jsonlite
Suggests:	knitr, rmarkdown, testthat (≥ 3.0.0), ggplot2, data.table, callr, httr, readr, withr, plumber
VignetteBuilder:	knitr
URL:	https://dd-harp.github.io/MicroMoB/, https://github.com/dd-harp/MicroMoB
BugReports:	https://github.com/dd-harp/MicroMoB/issues
NeedsCompilation:	yes
Packaged:	2023-01-17 01:29:10 UTC; slwu89
Author:	Sean L. Wu [aut, cre], David L. Smith [aut], Sophie Libkind [ctb]
Maintainer:	Sean L. Wu <slwood89@gmail.com>
Repository:	CRAN
Date/Publication:	2023-01-17 03:40:02 UTC

MicroMoB: Microsimulation for mosquito-borne pathogens

Description

Discrete time simulation of mosquito-borne pathogen transmission

Author(s)

Maintainer: Sean L. Wu slwood89@gmail.com (ORCID)

Authors:

• David L. Smith smitdave@uw.edu (ORCID)

Other contributors:

• Sophie Libkind [contributor]

See Also

Useful links:

• https://dd-harp.github.io/MicroMoB/

• https://github.com/dd-harp/MicroMoB

• Report bugs at https://github.com/dd-harp/MicroMoB/issues

Read global configuration options

Description

Read global configuration options

Usage

api_config_global(path)

Arguments

Check if two numeric values are approximately equal

Description

Check if two numeric values are approximately equal

Usage

approx_equal(a, b, tol = sqrt(.Machine$double.eps))

Arguments

Value

a logical value

Compute human population strata sizes (H)

Description

This method dispatches on the type of model$human.

Usage

compute_H(model)

Arguments

Value

a vector of length n giving the size of each human population stratum

Compute human population strata sizes for MOI model (H)

Description

Compute human population strata sizes for MOI model (H)

Usage

## S3 method for class 'MOI'
compute_H(model)

Arguments

Value

a vector of length n giving the size of each human population stratum

Compute human population strata sizes for SIP model (H)

Description

Compute human population strata sizes for SIP model (H)

Usage

## S3 method for class 'SIP'
compute_H(model)

Arguments

Value

a vector of length n giving the size of each human population stratum

Compute human population strata sizes for SIR model (H)

Description

Compute human population strata sizes for SIR model (H)

Usage

## S3 method for class 'SIR'
compute_H(model)

Arguments

Value

a vector of length n giving the size of each human population stratum

Compute human population strata sizes for SIS model (H)

Description

Compute human population strata sizes for SIS model (H)

Usage

## S3 method for class 'SIS'
compute_H(model)

Arguments

Value

a vector of length n giving the size of each human population stratum

Compute available alternative blood hosts (O)

Description

This method dispatches on the type of model$alternative.

Usage

compute_O(model)

Arguments

Value

a vector of length p giving biting availability of other blood hosts at each patch

Compute available alternative blood hosts for trace model (O)

Description

Compute available alternative blood hosts for trace model (O)

Usage

## S3 method for class 'trace'
compute_O(model)

Arguments

Value

a vector of length p giving biting availability of other blood hosts at each patch

Compute time at risk matrix (\Psi)

Description

The time at risk matrix is \Psi = \Theta \xi This method dispatches on the type of model$human.

Usage

compute_Psi(model)

Arguments

Value

a matrix with n rows and p columns, the time at risk matrix

Compute time at risk matrix for MOI model (\Psi)

Description

Compute time at risk matrix for MOI model (\Psi)

Usage

## S3 method for class 'MOI'
compute_Psi(model)

Arguments

Value

a matrix with n rows and p columns, the time at risk matrix

Compute time at risk matrix for SIP model (\Psi)

Description

Compute time at risk matrix for SIP model (\Psi)

Usage

## S3 method for class 'SIP'
compute_Psi(model)

Arguments

Value

a matrix with n rows and p columns, the time at risk matrix

Compute time at risk matrix for SIR model (\Psi)

Description

Compute time at risk matrix for SIR model (\Psi)

Usage

## S3 method for class 'SIR'
compute_Psi(model)

Arguments

Value

a matrix with n rows and p columns, the time at risk matrix

Compute time at risk matrix for SIS model (\Psi)

Description

Compute time at risk matrix for SIS model (\Psi)

Usage

## S3 method for class 'SIS'
compute_Psi(model)

Arguments

Value

a matrix with n rows and p columns, the time at risk matrix

Compute available visitors (W_{\delta})

Description

This method dispatches on the type of model$visitor.

Usage

compute_Wd(model)

Arguments

Value

a vector of length p giving biting availability of visitors at each patch

Compute available visitors for trace model (W_{\delta})

Description

Compute available visitors for trace model (W_{\delta})

Usage

## S3 method for class 'trace'
compute_Wd(model)

Arguments

Value

a vector of length p giving biting availability of visitors at each patch

Compute density of infective mosquitoes (Z)

Description

This method dispatches on the type of model$mosquito. Z is also known as the "sporozoite rate" in malariology.

Usage

compute_Z(model)

Arguments

Value

a vector of length p giving the density of infected and infectious mosquitoes in each patch

Compute density of infective mosquitoes for BQ model (Z)

Description

This method returns Z.

Usage

## S3 method for class 'BQ'
compute_Z(model)

Arguments

Value

a vector of length p giving the density of infected and infectious mosquitoes in each blood feeding haunt

Compute density of infective mosquitoes for RM model (Z)

Description

This method returns Z.

Usage

## S3 method for class 'RM'
compute_Z(model)

Arguments

Value

a vector of length p giving the density of infected and infectious mosquitoes in each patch

Compute null density of infective mosquitoes (Z)

Description

Compute null density of infective mosquitoes (Z)

Usage

## S3 method for class 'trace'
compute_Z(model)

Arguments

Value

no return value

Compute bloodmeals taken by mosquitoes on hosts

Description

This should be run prior to any step functions to update components over a time step. It computes various quantities related to disease transmission between species using the generic interfaces (methods) provided by each component. It updates the EIR vector for the human component, and kappa, the net infectiousness of hosts for the mosquito component.

Usage

compute_bloodmeal(model)

Arguments

Value

no return value

Compute bloodmeals taken by mosquitoes on hosts in simple models

Description

The difference between this and compute_bloodmeal is that this function does not include any computations of alternative blood hosts or visitors and is suitable for models which only include mosquitoes and resident human populations.

Usage

compute_bloodmeal_simple(model)

Arguments

Value

no return value

Compute number of newly emerging adults (\lambda)

Description

This method dispatches on the type of model$aqua

Usage

compute_emergents(model)

Arguments

Value

a vector of length p giving the number of newly emerging adult in each patch

Compute number of newly emerging adults from Beverton-Holt dynamics

Description

This function dispatches on the second class attribute of model$aqua for stochastic or deterministic behavior.

Usage

## S3 method for class 'BH'
compute_emergents(model)

Arguments

Value

a vector of length l giving the number of newly emerging adult in each patch

Compute number of newly emerging adults from forcing term

Description

This function dispatches on the second class attribute of model$aqua for stochastic or deterministic behavior.

Usage

## S3 method for class 'trace'
compute_emergents(model)

Arguments

Details

see compute_emergents.trace_deterministic and compute_emergents.trace_stochastic

Value

no return value

Compute number of newly emerging adults from forcing term (deterministic)

Description

Return the column of the lambda matrix for this day.

Usage

## S3 method for class 'trace_deterministic'
compute_emergents(model)

Arguments

Value

a vector of length l giving the number of newly emerging adult in each patch

Compute number of newly emerging adults from forcing term (stochastic)

Description

Draw a Poisson distributed number of emerging adults with mean parameter from the column of the trace matrix for this day.

Usage

## S3 method for class 'trace_stochastic'
compute_emergents(model)

Arguments

Value

a vector of length l giving the number of newly emerging adult in each patch

Compute mosquito feeding rate (f)

Description

This method dispatches on the type of model$mosquito

Usage

compute_f(model, B)

Arguments

Value

a vector of length p giving the per-capita blood feeding rate of mosquitoes in each patch

Compute mosquito feeding rate for BQ model (f)

Description

Blood feeding rates are modeled as a Holling type 2 (rational) function of blood host availability.

f(B) = f_x \frac{s_f B}{1+s_f B}

Here f_x is the maximum blood feeding rate and s_f is a scaling parameter.

Usage

## S3 method for class 'BQ'
compute_f(model, B)

Arguments

Value

a vector of length p giving the per-capita blood feeding rate of mosquitoes in each blood feeding haunt

Compute mosquito feeding rate for RM model (f)

Description

This method simply returns the f parameter of the mosquito object, because the RM model assumes a constant blood feeding rate.

Usage

## S3 method for class 'RM'
compute_f(model, B)

Arguments

Value

a vector of length p giving the per-capita blood feeding rate of mosquitoes in each patch

Compute null mosquito feeding rate (f)

Description

Compute null mosquito feeding rate (f)

Usage

## S3 method for class 'trace'
compute_f(model, B)

Arguments

Value

no return value

Compute number of eggs laid from oviposition for each patch

Description

This method dispatches on the type of model$mosquito

Usage

compute_oviposit(model)

Arguments

Value

a vector of length l giving the total number of eggs laid by adult mosquitoes in each aquatic habitat

Compute number of eggs laid from oviposition for each aquatic habitat for BQ model

Description

This method returns a vector of length l.

Usage

## S3 method for class 'BQ'
compute_oviposit(model)

Arguments

Details

see compute_oviposit.BQ_deterministic and compute_oviposit.BQ_stochastic

Value

a vector of length l giving the total number of eggs laid by adult mosquitoes in each aquatic habitat

Compute number of eggs laid from oviposition for each patch for deterministic RM model

Description

Compute number of eggs laid from oviposition for each patch for deterministic RM model

Usage

## S3 method for class 'BQ_deterministic'
compute_oviposit(model)

Arguments

Value

a vector of length l giving the total number of eggs laid by adult mosquitoes in each aquatic habitat

Compute number of eggs laid from oviposition for each patch for stochastic RM model

Description

Compute number of eggs laid from oviposition for each patch for stochastic RM model

Usage

## S3 method for class 'BQ_stochastic'
compute_oviposit(model)

Arguments

Value

a vector of length l giving the total number of eggs laid by adult mosquitoes in each aquatic habitat

Compute number of eggs laid from oviposition for each patch for RM model

Description

This method returns a vector of length p.

Usage

## S3 method for class 'RM'
compute_oviposit(model)

Arguments

Details

see compute_oviposit.RM_deterministic and compute_oviposit.RM_stochastic

Value

a vector of length p giving the total number of eggs laid by adult mosquitoes in each patch

Compute number of eggs laid from oviposition for each patch for deterministic RM model

Description

Compute number of eggs laid from oviposition for each patch for deterministic RM model

Usage

## S3 method for class 'RM_deterministic'
compute_oviposit(model)

Arguments

Value

a vector of length p giving the total number of eggs laid by adult mosquitoes in each patch

Compute number of eggs laid from oviposition for each patch for stochastic RM model

Description

Compute number of eggs laid from oviposition for each patch for stochastic RM model

Usage

## S3 method for class 'RM_stochastic'
compute_oviposit(model)

Arguments

Value

a vector of length l giving the total number of eggs laid by adult mosquitoes in each patch

Compute number of eggs laid from oviposition for each patch for null model

Description

This method dispatches on the type of model$mosquito

Usage

## S3 method for class 'trace'
compute_oviposit(model)

Arguments

Value

a vector of length p giving the total number of eggs laid by adult mosquitoes in each patch

Compute human blood feeding fraction (q)

Description

This method dispatches on the type of model$mosquito

Usage

compute_q(model, W, Wd, B)

Arguments

Value

a vector of length p giving the proportion of bites taken on human hosts in each patch

Compute human blood feeding fraction for BQ model (q)

Description

The human blood feeding fraction is simply the proportion of human hosts.

Usage

## S3 method for class 'BQ'
compute_q(model, W, Wd, B)

Arguments

Value

a vector of length p giving the proportion of bites taken on human hosts in each blood feeding haunt

Compute human blood feeding fraction for RM model (q)

Description

This method simply returns the q parameter of the mosquito object, because the RM model assumes a constant fraction of blood meals are taken on human hosts.

Usage

## S3 method for class 'RM'
compute_q(model, W, Wd, B)

Arguments

Value

a vector of length p giving the proportion of bites taken on human hosts in each patch

Compute null human blood feeding fraction (q)

Description

Compute null human blood feeding fraction (q)

Usage

## S3 method for class 'trace'
compute_q(model, W, Wd, B)

Arguments

Value

no return value

Compute human biting weights (w_{f})

Description

This method dispatches on the type of model$human.

Usage

compute_wf(model)

Arguments

Value

a vector of length n giving the biting weights of human hosts in each stratum

Compute human biting weights for MOI model (w_{f})

Description

Compute human biting weights for MOI model (w_{f})

Usage

## S3 method for class 'MOI'
compute_wf(model)

Arguments

Value

a vector of length n giving the biting weights of human hosts in each stratum

Compute human biting weights for SIP model (w_{f})

Description

Compute human biting weights for SIP model (w_{f})

Usage

## S3 method for class 'SIP'
compute_wf(model)

Arguments

Value

a vector of length n giving the biting weights of human hosts in each stratum

Compute human biting weights for SIR model (w_{f})

Description

Compute human biting weights for SIR model (w_{f})

Usage

## S3 method for class 'SIR'
compute_wf(model)

Arguments

Value

a vector of length n giving the biting weights of human hosts in each stratum

Compute human biting weights for SIS model (w_{f})

Description

Compute human biting weights for SIS model (w_{f})

Usage

## S3 method for class 'SIS'
compute_wf(model)

Arguments

Value

a vector of length n giving the biting weights of human hosts in each stratum

Compute net infectiousness of humans (x)

Description

In a Ross-Macdonald style transmission model, this is computed as

x = c X

This method dispatches on the type of model$human.

Usage

compute_x(model)

Arguments

Value

a vector of length n giving the net infectiousness of human hosts in each stratum

Compute net infectiousness for MOI model (x)

Description

In the simple MOI (queueing) model here (M/M/inf), net infectiousness is considered not to vary with increasing MOI. It is calculated as

c \cdot (1 - \frac{X_{0}}{H})

where X_{0} is the number of uninfected persons (multiplicity of infection of zero).

Usage

## S3 method for class 'MOI'
compute_x(model)

Arguments

Value

a vector of length n giving the net infectiousness of human hosts in each stratum

Compute net infectiousness for SIP model (x)

Description

Compute net infectiousness for SIP model (x)

Usage

## S3 method for class 'SIP'
compute_x(model)

Arguments

Value

a vector of length n giving the net infectiousness of human hosts in each stratum

Compute net infectiousness for SIR model (x)

Description

Compute net infectiousness for SIR model (x)

Usage

## S3 method for class 'SIR'
compute_x(model)

Arguments

Value

a vector of length n giving the net infectiousness of human hosts in each stratum

Compute net infectiousness for SIS model (x)

Description

Compute net infectiousness for SIS model (x)

Usage

## S3 method for class 'SIS'
compute_x(model)

Arguments

Value

a vector of length n giving the net infectiousness of human hosts in each stratum

Compute net infectiousness of visitors (x_{\delta})

Description

This method dispatches on the type of model$visitor.

Usage

compute_xd(model)

Arguments

Value

a vector of length p giving net infectiousness of visitors at each patch

Compute net infectiousness of visitors for trace model (x_{\delta})

Description

Compute net infectiousness of visitors for trace model (x_{\delta})

Usage

## S3 method for class 'trace'
compute_xd(model)

Arguments

Value

a vector of length p giving net infectiousness of visitors at each patch

Distribute items into bins as evenly as possible

Description

Distribute items into bins as evenly as possible

Usage

distribute(n, p)

Arguments

Value

a numeric vector of bin sizes

Division of integers

Description

Division of integers

Usage

divmod(a, b)

Arguments

Value

a list with two elements, quo (quotient) and rem (remainder)

Draw a multinomially distributed random vector

Description

Warning: this function does no argument checking. Ensure the arguments are as follows.

Usage

draw_multinom(n, prob)

Arguments

Value

an integer vector of length equal to the length of prob

Note

This function uses the algorithm presented in: Startek, Michał. "An asymptotically optimal, online algorithm for weighted random sampling with replacement." arXiv preprint arXiv:1611.00532 (2016).

Get carrying capacity for Beverton-Holt aquatic mosquito model

Description

Get carrying capacity for Beverton-Holt aquatic mosquito model

Usage

get_K_aqua_BH(model, times, places)

Arguments

Value

a matrix

Get parameters for trace driven alternative blood hosts

Description

The JSON config file should have two entries:

• O: vector or matrix (see time_patch_varying_parameter for valid dimensions)

For interpretation of the entries, please read setup_alternative_trace.

Usage

get_config_alternative_trace(path)

Arguments

Value

a named list

Examples

# to see an example of proper JSON input, run the following
library(jsonlite)
par <- list(
 "O" = rep(1, 5)
)
toJSON(par, pretty = TRUE)

Get parameters for aquatic (immature) model with Beverton-Holt dynamics

Description

The JSON config file should have two entries:

• stochastic: a boolean value

• molt: a scalar, vector, or matrix (row major)

• surv: a scalar, vector, or matrix (row major)

• K: a scalar, vector, or matrix (row major)

• L: a vector

Please see time_patch_varying_parameter for allowed dimensions of entries molt, surv, and K. L should be of length equal to the number of patches. For interpretation of the entries, please read setup_aqua_BH.

Usage

get_config_aqua_BH(path)

Arguments

Value

a named list

Examples

# to see an example of proper JSON input, run the following
library(jsonlite)
p <- 5 # number of patches
t <- 10 # number of days to simulate
par <- list(
 "stochastic" = FALSE,
 "molt" = 0.3,
 "surv" = rep(0.5, 365),
 "K" = matrix(rpois(n = t * p, lambda = 100), nrow = p, ncol = t),
 "L" = rep(10, p)
)
toJSON(par, pretty = TRUE)

Get parameters for aquatic (immature) model with forced emergence

Description

The JSON config file should have two entries:

• stochastic: a boolean value

• lambda: a scalar, vector, or matrix (row major). It will be passed to time_patch_varying_parameter, see that function's documentation for appropriate dimensions.

For interpretation of the entries, please read setup_aqua_trace.

Usage

get_config_aqua_trace(path)

Arguments

Value

a named list

Examples

# to see an example of proper JSON input, run the following
library(jsonlite)
t <- 10 # number of days to simulate
par <- list(
 "stochastic" = FALSE,
 "lambda" = rpois(n = t, lambda = 10)
)
toJSON(par, pretty = TRUE)

Get parameters for MOI human model

Description

The JSON config file should have 9 entries:

• stochastic: a boolean value

• theta: matrix (row major)

• wf: vector

• H: vector

• MOI: matrix (row major)

• b: scalar

• c: scalar

• r: scalar

• sigma: scalar

For interpretation of the entries, please read setup_humans_MOI.

Usage

get_config_humans_MOI(path)

Arguments

Value

a named list

Examples

# to see an example of proper JSON input, run the following
library(jsonlite)
n <- 6 # number of human population strata
p <- 5 # number of patches
theta <- matrix(rexp(n*p), nrow = n, ncol = p)
theta <- theta / rowSums(theta)
H <- rep(10, n)
MOI <- matrix(0, nrow = 10, ncol = n)
MOI[1, ] <- H
par <- list(
 "stochastic" = FALSE,
 "theta" = theta,
 "wf" = rep(1, n),
 "H" = H,
 "MOI" = MOI,
 "b" = 0.55,
 "c" = 0.15,
 "r" = 1/200,
 "sigma" = 1
)
toJSON(par, pretty = TRUE)

Get parameters for SIR human model

Description

The JSON config file should have 8 entries:

• stochastic: a boolean value

• theta: matrix (row major)

• wf: vector

• H: vector

• SIR: matrix (row major)

• b: scalar

• c: scalar

• gamma: scalar

For interpretation of the entries, please read setup_humans_SIR.

Usage

get_config_humans_SIR(path)

Arguments

Value

a named list

Examples

# to see an example of proper JSON input, run the following
library(jsonlite)
n <- 6 # number of human population strata
p <- 5 # number of patches
theta <- matrix(rexp(n*p), nrow = n, ncol = p)
theta <- theta / rowSums(theta)
H <- rep(10, n)
SIR <- matrix(0, nrow = n, ncol = 3)
SIR[, 1] <- H
par <- list(
 "stochastic" = FALSE,
 "theta" = theta,
 "wf" = rep(1, n),
 "H" = H,
 "SIR" = SIR,
 "b" = 0.55,
 "c" = 0.15,
 "gamma" = 1/7
)
toJSON(par, pretty = TRUE)

Get parameters for SIS human model

Description

The JSON config file should have 8 entries:

• stochastic: a boolean value

• theta: matrix (row major)

• wf: vector

• H: vector

• X: vector

• b: scalar

• c: scalar

• r: scalar

For interpretation of the entries, please read setup_humans_SIS.

Usage

get_config_humans_SIS(path)

Arguments

Value

a named list

Examples

# to see an example of proper JSON input, run the following
library(jsonlite)
n <- 6 # number of human population strata
p <- 5 # number of patches
theta <- matrix(rexp(n*p), nrow = n, ncol = p)
theta <- theta / rowSums(theta)
H <- rep(10, n)
X <- rep(3, n)
par <- list(
 "stochastic" = FALSE,
 "theta" = theta,
 "wf" = rep(1, n),
 "H" = H,
 "X" = X,
 "b" = 0.55,
 "c" = 0.15,
 "r" = 1/200
)
toJSON(par, pretty = TRUE)

Get parameters for generalized Ross-Macdonald mosquito model

Description

The JSON config file should have 8 entries:

• stochastic: a boolean value

• f: scalar

• q: scalar

• eip: scalar or vector; see time_varying_parameter for valid formats

• p: scalar or vector; see time_varying_parameter for valid formats

• psi: matrix

• nu: scalar

• M: vector

• Y: vector

• Z: vector

For interpretation of the entries, please read setup_mosquito_RM.

Usage

get_config_mosquito_RM(path)

Arguments

Value

a named list

Examples

# to see an example of proper JSON input, run the following
library(jsonlite)
t <- 10 # days to simulate
p <- 5 # number of patches
EIP <-  rep(5, t)
p_surv <- 0.95
psi <- matrix(rexp(p^2), nrow = p, ncol = p)
psi <- psi / rowSums(psi)
par <- list(
 "stochastic" = FALSE,
 "f" = 0.3,
 "q" = 0.9,
 "eip" = EIP,
 "p" = p_surv,
 "psi" = psi,
 "nu" = 20,
 "M" = rep(100, p),
 "Y" = rep(20, p),
 "Z" = rep(5, p)
)
toJSON(par, pretty = TRUE)

Get parameters for null mosquito model

Description

The JSON config file should have 1 entry:

• oviposit: vector

For interpretation of the entries, please read setup_mosquito_trace.

Usage

get_config_mosquito_trace(path)

Arguments

Value

a named list

Examples

# to see an example of proper JSON input, run the following
library(jsonlite)
par <- list(
 "oviposit" = rep(1, 5)
)
toJSON(par, pretty = TRUE)

Get parameters for trace driven visitors

Description

The JSON config file should have two entries:

• Wd: vector or matrix (see time_patch_varying_parameter for valid dimensions)

• xd: vector or matrix (see time_patch_varying_parameter for valid dimensions)

For interpretation of the entries, please read setup_visitor_trace.

Usage

get_config_visitor_trace(path)

Arguments

Value

a named list

Examples

# to see an example of proper JSON input, run the following
library(jsonlite)
par <- list(
 "Wd" = rep(1, 5),
 "xd" = rep(0.01, 365)
)
toJSON(par, pretty = TRUE)

Get extrinsic incubation period for Ross-Macdonald mosquito model

Description

Get extrinsic incubation period for Ross-Macdonald mosquito model

Usage

get_eip_mosquito_RM(model, times)

Arguments

Value

no return value

Get feeding rate for Ross-Macdonald mosquito model

Description

Get feeding rate for Ross-Macdonald mosquito model

Usage

get_f_mosquito_RM(model)

Arguments

Value

a vector

Get kappa for Ross-Macdonald mosquito model

Description

Get kappa for Ross-Macdonald mosquito model

Usage

get_kappa_mosquito_RM(model)

Arguments

Value

a vector

Get daily emergence for Beverton-Holt aquatic mosquito model

Description

Get daily emergence for Beverton-Holt aquatic mosquito model

Usage

get_lambda_aqua_trace(model, times, places)

Arguments

Value

a matrix

Get daily maturation probability for Beverton-Holt aquatic mosquito model

Description

Get daily maturation probability for Beverton-Holt aquatic mosquito model

Usage

get_molt_aqua_BH(model, times, places)

Arguments

Value

a matrix

Get number of eggs laid per oviposition for Ross-Macdonald mosquito model

Description

Get number of eggs laid per oviposition for Ross-Macdonald mosquito model

Usage

get_nu_mosquito_RM(model)

Arguments

Value

a vector

Get daily survival probability for Ross-Macdonald mosquito model

Description

Get daily survival probability for Ross-Macdonald mosquito model

Usage

get_p_mosquito_RM(model, times, places)

Arguments

Value

a matrix

Get mosquito dispersal matrix for Ross-Macdonald mosquito model

Description

Get mosquito dispersal matrix for Ross-Macdonald mosquito model

Usage

get_psi_mosquito_RM(model)

Arguments

Value

a matrix

Get human blood feeding fraction for Ross-Macdonald mosquito model

Description

Get human blood feeding fraction for Ross-Macdonald mosquito model

Usage

get_q_mosquito_RM(model)

Arguments

Value

a vector

Get daily survival probability for Beverton-Holt aquatic mosquito model

Description

Get daily survival probability for Beverton-Holt aquatic mosquito model

Usage

get_surv_aqua_BH(model, times, places)

Arguments

Value

a matrix

Get maximum time of simulation from model object

Description

Get maximum time of simulation from model object

Usage

get_tmax(model)

Arguments

Get current time of simulation from model object

Description

Get current time of simulation from model object

Usage

get_tnow(model)

Arguments

Does a numeric object consist of only zeros and ones?

Description

Does a numeric object consist of only zeros and ones?

Usage

is_binary(x)

Arguments

Value

a logical value

Make a model object

Description

The model object is a hashed environment. By default it contains a single list, model$global storing global state.

Usage

make_MicroMoB(tmax, p, l = p)

Arguments

Value

an object of class environment

Observe PfPR in human strata

Description

This method dispatches on the type of model$human.

Usage

observe_pfpr(model, parameters)

Arguments

Value

an array of counts, with actual condition as first dimension and tested condition as the second dimension, and the third dimension is the human strata

Observe PfPR in human strata for SIP model

Description

Observe PfPR in human strata for SIP model

Usage

## S3 method for class 'SIP'
observe_pfpr(model, parameters)

Arguments

Value

an array of counts, with actual condition as first dimension and tested condition as the second dimension, and the third dimension is the human strata

Observe PfPR in human strata for SIS model

Description

Observe PfPR in human strata for SIS model

Usage

## S3 method for class 'SIS'
observe_pfpr(model, parameters)

Arguments

Value

an array of counts, with actual condition as first dimension and tested condition as the second dimension, and the third dimension is the human strata

Get output for aquatic (immature) mosquito populations

Description

This method dispatches on the type of model$aqua. It returns the current state of the aquatic component.

Usage

output_aqua(model)

Arguments

Value

a data.frame

Get output for aquatic (immature) mosquito populations with Beverton-Holt dynamics

Description

Return a data.frame.

Usage

## S3 method for class 'BH'
output_aqua(model)

Arguments

Value

a data.frame with columns L (immature) and A (emerging pupae)

Get output for aquatic (immature) mosquito populations with forced emergence

Description

This function returns an empty data.frame as trace models do not have endogenous dynamics.

Usage

## S3 method for class 'trace'
output_aqua(model)

Arguments

Value

a data.frame

Get output for mosquito populations

Description

This method dispatches on the type of model$mosquito. It returns the current state of the adult mosquito component.

Usage

output_mosquitoes(model)

Arguments

Value

a data.frame

Get output for Ross-Macdonald mosquito populations

Description

Return a data.frame.

Usage

## S3 method for class 'RM'
output_mosquitoes(model)

Arguments

Value

a data.frame with columns M (all adult mosquitoes), Y (infected mosquitoes), and Z (infectious mosquitoes), and rows correspond to places.

Get output for null mosquito populations

Description

This function returns an empty data.frame as trace models do not have endogenous dynamics.

Usage

## S3 method for class 'trace'
output_mosquitoes(model)

Arguments

Value

a data.frame

Sample a stochastic matrix

Description

x is a matrix with arbitrary number of rows but whose columns are equal to the number of bins that the stochastic matrix prob parameterizes a distribution over. Each row of x gives a distribution of counts over bins and is resampled according to prob. It is conceptually similar to "stochastically" distributing the matrix as x %*% prob, which gives the expectation.

Usage

sample_stochastic_matrix(x, prob)

Arguments

Value

a matrix whose dimensions equal the original x

Sample a stochastic vector

Description

Given a vector of counts in cells, x and a stochastic matrix prob, each row of which describes a probability distribution of how that cell should be distributed among bins, sample destination bins for each cell count, and return a vector giving the number of counts in bins. It is conceptually similar to "stochastically" distributing the vector as x %*% prob, which gives the expectation.

Usage

sample_stochastic_vector(x, prob)

Arguments

Value

a vector of length equal to the number of columns of prob

Set carrying capacity for Beverton-Holt aquatic mosquito model

Description

Change the carrying capacity parameter K for some times and places. The parameter K is stored internally as a matrix so that times and places are used to modify a submatrix, therefore the new value K should either be a scalar value to update the entire submatrix or a matrix of places rows and times columns.

Usage

set_K_aqua_BH(model, K, times, places)

Arguments

Value

no return value

Set extrinsic incubation period for Ross-Macdonald mosquito model

Description

Change the extrinsic incubation period parameter eip for some set of times. The new values eip should either be a scalar or a vector of length equal to the length of times.

Usage

set_eip_mosquito_RM(model, eip, times)

Arguments

Value

no return value

Set feeding rate for Ross-Macdonald mosquito model

Description

Change the feeding rate parameter f.

Usage

set_f_mosquito_RM(model, f)

Arguments

Value

no return value

Set kappa for Ross-Macdonald mosquito model

Description

Change kappa.

Usage

set_kappa_mosquito_RM(model, kappa)

Arguments

Value

no return value

Set daily emergence for trace (forced) aquatic mosquito model

Description

Change the daily emergence parameter lambda for some times and places. The parameter lambda is stored internally as a matrix so that times and places are used to modify a submatrix, therefore the new value lambda should either be a scalar value to update the entire submatrix or a matrix of places rows and times columns.

Usage

set_lambda_aqua_trace(model, lambda, times, places)

Arguments

Value

no return value

Set daily maturation probability for Beverton-Holt aquatic mosquito model

Description

Change the daily maturation probability parameter molt for some times and places. The parameter molt is stored internally as a matrix so that times and places are used to modify a submatrix, therefore the new value molt should either be a scalar value to update the entire submatrix or a matrix of places rows and times columns.

Usage

set_molt_aqua_BH(model, molt, times, places)

Arguments

Value

no return value

Set number of eggs laid per oviposition for Ross-Macdonald mosquito model

Description

Change the number of eggs laid per oviposition parameter nu.

Usage

set_nu_mosquito_RM(model, nu)

Arguments

Value

no return value

Set daily survival probability for Ross-Macdonald mosquito model

Description

Change the daily survival probability parameter p for some times and places. The parameter p is stored internally as a matrix so that times and places are used to modify a submatrix, therefore the new value p should either be a scalar value to update the entire submatrix or a matrix of places rows and times columns.

Usage

set_p_mosquito_RM(model, p, times, places)

Arguments

Value

no return value

Set mosquito dispersal matrix for Ross-Macdonald mosquito model

Description

Change the mosquito dispersal matrix parameter psi.

Usage

set_psi_mosquito_RM(model, psi)

Arguments

Value

no return value

Set human blood feeding fraction for Ross-Macdonald mosquito model

Description

Change the human blood feeding fraction parameter q.

Usage

set_q_mosquito_RM(model, q)

Arguments

Value

no return value

Set daily survival probability for Beverton-Holt aquatic mosquito model

Description

Change the daily survival probability parameter surv for some times and places. The parameter surv is stored internally as a matrix so that times and places are used to modify a submatrix, therefore the new value surv should either be a scalar value to update the entire submatrix or a matrix of places rows and times columns.

Usage

set_surv_aqua_BH(model, surv, times, places)

Arguments

Value

no return value

Setup trace driven alternative blood hosts

Description

This model complies with the visitors component interface. It adds a named list model$alternative.

Usage

setup_alternative_trace(model, O = NULL)

Arguments

Value

no return value

Setup aquatic (immature) mosquito model with Beverton-Holt dynamics

Description

A single compartment for all aquatic stages is modeled which suffers density dependent mortality like the Beverton-Holt model.

Usage

setup_aqua_BH(model, stochastic, molt, surv, K, L)

Arguments

Details

All parameters can be passed either as a vector of length equal to l, a matrix with l rows and tmax columns, or a matrix with l rows and 365 columns.

Value

no return value

Setup aquatic (immature) mosquito model with trace (forced) emergence

Description

Emergence is passed as a (possibly time varying) parameter which is decoupled from the adult mosquito dynamics. This module assumes l and p are equivalent, as emergence rates are given for p.

Usage

setup_aqua_trace(model, lambda, stochastic)

Arguments

Value

no return value

Setup humans with MOI (multiplicity of infection) pathogen model

Description

This is a queueing model (M/M/inf) of superinfection in humans.

Usage

setup_humans_MOI(
  model,
  stochastic,
  theta,
  wf = NULL,
  H,
  MOI,
  b = 0.55,
  c = 0.15,
  r = 1/200,
  sigma = 1
)

Arguments

Value

no return value

Note

The step_humans method for the MOI model will grow the MOI matrix (add rows) if an individual's MOI exceeds the size of the matrix; therefore it's a good idea to pad the input matrix with extra empty rows to avoid reallocating memory during the simulation as much as possible.

Setup humans with SIP pathogen model

Description

A simple SIP (Susceptible-Infected-Protected) model

Usage

setup_humans_SIP(
  model,
  stochastic,
  theta,
  wf = NULL,
  SIP,
  b = 0.55,
  c = 0.15,
  r = 1/200,
  rho = 0.07,
  eta = 1/32
)

Arguments

Value

no return value

Setup humans with SIR infection model

Description

A simple SIR (Susceptible-Infected-Recovered) model

Usage

setup_humans_SIR(
  model,
  stochastic,
  theta,
  wf = NULL,
  H,
  SIR,
  b = 0.55,
  c = 0.15,
  gamma = 1/5
)

Arguments

Value

no return value

Setup humans with SIS pathogen model

Description

A simple SIS (Susceptible-Infected-Susceptible) model

Usage

setup_humans_SIS(
  model,
  stochastic,
  theta,
  wf = NULL,
  H,
  X,
  b = 0.55,
  c = 0.15,
  r = 1/200
)

Arguments

Value

no return value

Setup blood feeding & oviposition (BQ) behavioral state mosquito model

Description

This is a behavioral state model which allows for time varying EIP and survival probability. Mosquitoes transition between blood feeding (B) and oviposition (Q) depending on the success (or not) of those biological activities. It complies with the mosquito component interface, and may be simulated deterministically or stochastically.

Usage

setup_mosquito_BQ(
  model,
  stochastic,
  eip,
  pB,
  pQ,
  psiQ,
  Psi_bb,
  Psi_bq,
  Psi_qb,
  Psi_qq,
  nu = 25,
  M,
  Y
)

Arguments

Value

no return value

Setup generalized Ross-Macdonald mosquito model

Description

This is a generalized RM model which allows for time varying EIP and survival probability. It complies with the mosquito component interface, and may be simulated deterministically or stochastically.

Usage

setup_mosquito_RM(
  model,
  stochastic,
  f = 0.3,
  q = 0.9,
  eip,
  p,
  psi,
  nu = 25,
  M,
  Y,
  Z,
  N = NULL
)

Arguments

Value

no return value

Setup null mosquito model

Description

This is a null model of mosquito dynamics that is only for testing/verifying aquatic models. It implements a single method compute_oviposit.trace and all other methods throw an error.

Usage

setup_mosquito_trace(model, oviposit)

Arguments

Value

no return value

Setup trace driven visitors

Description

This model complies with the visitors component interface. It adds a named list model$visitor.

Usage

setup_visitor_trace(model, Wd = NULL, xd = NULL)

Arguments

Value

no return value

Update aquatic (immature) mosquito populations

Description

This method dispatches on the type of model$aqua

Usage

step_aqua(model)

Arguments

Value

no return value

Update aquatic (immature) mosquito populations for Beverton-Holt dynamics

Description

This function dispatches on the second class attribute of model$aqua for stochastic or deterministic behavior.

Usage

## S3 method for class 'BH'
step_aqua(model)

Arguments

Value

no return value

Update aquatic (immature) mosquito populations for deterministic Beverton-Holt dynamics

Description

Run a deterministic state update.

Usage

## S3 method for class 'BH_deterministic'
step_aqua(model)

Arguments

Value

no return value

Update aquatic (immature) mosquito populations for stochastic Beverton-Holt dynamics

Description

Run a stochastic state update.

Usage

## S3 method for class 'BH_stochastic'
step_aqua(model)

Arguments

Value

no return value

Update aquatic (immature) mosquito populations for forced emergence

Description

This function does nothing as trace models do not have endogenous dynamics.

Usage

## S3 method for class 'trace'
step_aqua(model)

Arguments

Value

no return value

Update human population

Description

This method dispatches on the type of model$human.

Usage

step_humans(model)

Arguments

Value

no return value

Update MOI human model

Description

Update MOI human model

Usage

## S3 method for class 'MOI'
step_humans(model)

Arguments

Value

no return value

Update MOI human model (deterministic)

Description

Update MOI human model (deterministic)

Usage

## S3 method for class 'MOI_deterministic'
step_humans(model)

Arguments

Value

no return value

Update MOI human model (stochastic)

Description

Update MOI human model (stochastic)

Usage

## S3 method for class 'MOI_stochastic'
step_humans(model)

Arguments

Value

no return value

Update SIP human model

Description

Update SIP human model

Usage

## S3 method for class 'SIP'
step_humans(model)

Arguments

Value

no return value

Update SIP human model (deterministic)

Description

Update SIP human model (deterministic)

Usage

## S3 method for class 'SIP_deterministic'
step_humans(model)

Arguments

Value

no return value

Update SIP human model (stochastic)

Description

Update SIP human model (stochastic)

Usage

## S3 method for class 'SIP_stochastic'
step_humans(model)

Arguments

Value

no return value

Update SIR human model

Description

Update SIR human model

Usage

## S3 method for class 'SIR'
step_humans(model)

Arguments

Value

no return value

Update SIR human model (deterministic)

Description

Update SIR human model (deterministic)

Usage

## S3 method for class 'SIR_deterministic'
step_humans(model)

Arguments

Value

no return value

Update SIR human model (stochastic)

Description

Update SIR human model (stochastic)

Usage

## S3 method for class 'SIR_stochastic'
step_humans(model)

Arguments

Value

no return value

Update SIS human model

Description

Update SIS human model

Usage

## S3 method for class 'SIS'
step_humans(model)

Arguments

Value

no return value

Update SIS human model (deterministic)

Description

Update SIS human model (deterministic)

Usage

## S3 method for class 'SIS_deterministic'
step_humans(model)

Arguments

Value

no return value

Update SIS human model (stochastic)

Description

Update SIS human model (stochastic)

Usage

## S3 method for class 'SIS_stochastic'
step_humans(model)

Arguments

Value

no return value

Update mosquito population

Description

This method dispatches on the type of model$mosquito

Usage

step_mosquitoes(model)

Arguments

Value

no return value

Update blood feeding & oviposition (BQ) behavioral state mosquitoes

Description

This function dispatches on the second argument of model$mosquito for stochastic or deterministic behavior.

Usage

## S3 method for class 'BQ'
step_mosquitoes(model)

Arguments

Details

see step_mosquitoes.BQ_deterministic and step_mosquitoes.BQ_stochastic

Value

no return value

Update blood feeding & oviposition (BQ) behavioral state mosquitoes (deterministic)

Description

Update blood feeding & oviposition (BQ) behavioral state mosquitoes (deterministic)

Usage

## S3 method for class 'BQ_deterministic'
step_mosquitoes(model)

Arguments

Value

no return value

Update blood feeding & oviposition (BQ) behavioral state mosquitoes (stochastic)

Description

Update blood feeding & oviposition (BQ) behavioral state mosquitoes (stochastic)

Usage

## S3 method for class 'BQ_stochastic'
step_mosquitoes(model)

Arguments

Value

no return value

Update Ross-Macdonald mosquitoes

Description

This function dispatches on the second argument of model$mosquito for stochastic or deterministic behavior.

Usage

## S3 method for class 'RM'
step_mosquitoes(model)

Arguments

Details

see step_mosquitoes.RM_deterministic and step_mosquitoes.RM_stochastic

Value

no return value

Update Ross-Macdonald mosquitoes (deterministic)

Description

Update Ross-Macdonald mosquitoes (deterministic)

Usage

## S3 method for class 'RM_deterministic'
step_mosquitoes(model)

Arguments

Value

no return value

Update Ross-Macdonald mosquitoes (stochastic)

Description

Update Ross-Macdonald mosquitoes (stochastic)

Usage

## S3 method for class 'RM_stochastic'
step_mosquitoes(model)

Arguments

Value

no return value

Update null mosquito population

Description

Update null mosquito population

Usage

## S3 method for class 'trace'
step_mosquitoes(model)

Arguments

Value

no return value

Helper function for lumped population strata (counts)

Description

If input is given as a matrix of population counts per strata (columns) and patch (rows), this function calculates the residency matrix and population size for the overall stratification of both residency and strata.

Usage

strata_to_residency_counts(H_counts)

Arguments

Value

a list with three elements:

• J: the residency matrix mapping elements in H to patches

• H: the overall population distribution over strata and patches

Examples

# taken from package tests
J <- matrix(
   c(0.3, 0.5, 0.2,
   0.1, 0.6, 0.3), nrow = 3, ncol = 2, byrow = FALSE
)
H <- c(50, 60)
H_overall <- J %*% diag(H)
residency <- strata_to_residency_proportion(H_strata = H, J_strata = J)

Helper function for lumped population strata (proportional assignment)

Description

If input is given as a vector of population sizes per-strata, lumped over patches, and a separate matrix whose columns describe how each strata is distributed over patches, this function calculates the residency matrix and population size for the overall stratification of both residency and strata.

Usage

strata_to_residency_proportion(H_strata, J_strata)

Arguments

Value

a list with three elements:

• assignment_indices: provides a mapping from patch (rows) and strata (columns) into the "unrolled" vector H

• J: the residency matrix mapping elements in H to patches

• H: the overall population distribution over strata and patches

Examples

# taken from package tests
J <- matrix(
   c(0.3, 0.5, 0.2,
   0.1, 0.6, 0.3), nrow = 3, ncol = 2, byrow = FALSE
)
H <- c(50, 60)
# get the overall assignment of strata (cols) across patches (rows)
H_overall <- J %*% diag(H)
residency <- strata_to_residency_proportion(H_strata = H, J_strata = J)

Input parameters that may vary by time and patch

Description

Input parameters that may vary by time and patch

Usage

time_patch_varying_parameter(param, p, tmax)

Arguments

Value

a matrix with p rows and tmax columns

Input parameters that may vary by time

Description

Input parameters that may vary by time

Usage

time_varying_parameter(param, tmax)

Arguments

Value

a vector with tmax elements