This set of functions modifies mosquito life history parameters in the presence of adult interventions - indoor residual spraying (IRS) and insecticide treated nets (ITN) This is based on the work of Le Menach et al (2007) and Griffin et al (2010). We vary three parameters in the presence of interventions: Egg laying rate (beta) Adult mortality (muF) Mosquito biting rate (av0)

Description

This set of functions modifies mosquito life history parameters in the presence of adult interventions - indoor residual spraying (IRS) and insecticide treated nets (ITN) This is based on the work of Le Menach et al (2007) and Griffin et al (2010). We vary three parameters in the presence of interventions: Egg laying rate (beta) Adult mortality (muF) Mosquito biting rate (av0)

Usage

add_interventions(params, IRS_cov, LLIN_cov)

Arguments

Value

a vector of the equilibrium number of females in each SEI stage

Base Summary Function

Description

This function does the actual calculations for summarize_stats_CSV. It calculates mean and quantiles, writing output to the appropriate folder.

Usage

base_MQ(
  fList,
  oDir,
  sName,
  nodeNames,
  nNodes,
  genos,
  nGenos,
  times,
  nTimes,
  num_repss,
  mean,
  quantiles,
  oDepth
)

Arguments

Value

None

Base Summary for Males, Unmated Females, and Humans

Description

This function takes a given stage (males, unmated females, or humans) and summarizes them by genotype (infection status for humans), writing output to provided folders.

Usage

base_MUH(fileVec, outList, genos, nGenos, nTimes, nNodes)

Arguments

Details

This function is a base function used in split_aggregate_CSV.

Value

None

Base Aquatic Function for Genotype Summary

Description

This function takes a given aquatic (egg, larval, pupal) stage and sums over the Erlang-distributed stages, returning summary trajectories by genotype.

Usage

base_aquatic_geno(out, spn_P, elp)

Arguments

Details

This function is the base function for summarize_eggs_geno, summarize_larvae_geno, and summarize_pupae_geno.

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

The return object depends on the data provided. If the simulation was only 1 node, then no node designation is returned. If only one repetition was performed, no rep designation is returned. Columns always returned include: time, genotype, and value.

Value

a 3 to 5 column dataframe for plotting with ggplot2

Base Aquatic Function for Erlang-Stage Summary

Description

This function takes a given aquatic (egg, larval, pupal) stage and sums over the genotypes, returning summary trajectories by Erlang-distributed stage.

Usage

base_aquatic_stage(out, spn_P, elp)

Arguments

Details

This function is the base function for summarize_eggs_stage, summarize_larvae_stage, and summarize_pupae_stage.

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

The return object depends on the data provided. If the simulation was only 1 node, then no node designation is returned. If only one repetition was performed, no rep designation is returned. Columns always returned include: time, Erlang-stage, and value.

Value

a 3 to 5 column dataframe for plotting with ggplot2

Base Summary of Erlang Stages for Aquatic Life Stages

Description

This function takes the given aquatic stage and summarizes them by Erlang-distributed dwell times, writing output to provided folders.

Usage

base_erlang(fileVec, outList, genos, nGenos, nErlang, times, nTimes, nNodes)

Arguments

Details

This function is a base function used in split_aggregate_CSV.

Value

None

Base Summary of Erlang Stages for Adult Females

Description

This function takes ALL of the adult female stages and summarized them by Erlang-distributed latent infection, writing output to provided folders.

Usage

base_erlang_F(fileList, outList, nGenos, nErlang, times, nTimes, nNodes)

Arguments

Details

This function is a base function used in split_aggregate_CSV.

Value

None

Base Summary for Eggs, Larvae, Pupae, Susceptible Females, and Infectious Females

Description

This function takes a given stage and summarizes them by genotype, writing output to provided folders.

Usage

base_gen(fileVec, outList, genos, nGenos, nIDX1, times, nTimes, nNodes)

Arguments

Details

This function is a base function used in split_aggregate_CSV.

Value

None

Base Summary for Latent Females

Description

This function takes 'E' stage females and summarizes them by genotype, writing output to provided folders.

Usage

base_gen_FE(fileVec, outList, genos, nGenos, nIDX1, times, nTimes, nNodes)

Arguments

Details

This function is a base function used in split_aggregate_CSV.

Value

None

Base Summary of Infection (SEI) Stages for Adult Females

Description

This function takes ALL of the adult female stages and summarized them by Erlang-distributed latent infection, writing output to provided folders.

Usage

base_sum_F(fileList, outList, genos, nGenos, nErlang, times, nTimes, nNodes)

Arguments

Details

This function is a base function used in split_aggregate_CSV.

Value

None

Base Function for Human Summary

Description

This function takes a given infection ('S','E','I','R') status and returns a summary trajectory

Usage

base_summarize_humans(out, infState)

Arguments

Details

This function is the base function for summarize_humans_epiSIS, summarize_humans_epiSEIR.

The return object depends on the data provided. If the simulation was only 1 node, then no node designation is returned. If only one repetition was performed, no rep designation is returned. Columns always returned include: time, inf, genotype, and value.

Value

a 4 to 6 column dataframe for plotting with ggplot2

Sample Batch Migration Events

Description

Sample batch migration events for simulation given rates of occurance and probability of destination for each patch. Batch migration can be simulated for the aquatic life stages (eggs, larvae, pupae), adult females, and/or adult males. To simulate batch migration, each life stage needs all 3 of its arguments specified. If any arguments are left unspecified (NULL), batch migration for that life stage will not be sampled. The output of this function should be passed to sim_trajectory_R or sim_trajectory_CSV as the argument batch. Calls the internal function batch_migration_stage.

Usage

batch_migration(
  SPN_P,
  tmax,
  ELPrates = NULL,
  ELPmove = NULL,
  ELPprob = NULL,
  Frates = NULL,
  Fmove = NULL,
  Fprob = NULL,
  Mrates = NULL,
  Mmove = NULL,
  Mprob = NULL,
  stage = NULL
)

Arguments

Value

vector of lists describing all batch migration events, segmented by life stage.

Internal function to sample and set up data structure for batch migration

Description

Internal function to sample and set up data structure for batch migration

Usage

batch_migration_stage(SPN_P, rates, move, prob, stage, tmax)

Arguments

Calculate Outbound Movement Rate

Description

Given P, the cumulative probability of moving before dying, and mu, the daily mortality rate, calculate the movement rate gamma to get P. The equation comes from integrating the competing risks and solving for gamma.

Usage

calc_move_rate(mu, P)

Arguments

Value

numeric probability of movement

Examples

  # parameters, see vignette MGDrivE2: One Node Lifecycle Dynamics
  theta <- list(qE = 1/4, nE = 2, qL = 1/3, nL = 3, qP = 1/6, nP = 2,
                muE = 0.05, muL = 0.15, muP = 0.05, muF = 0.09, muM = 0.09,
                beta = 16, nu = 1/(4/24) )

  # lets say a 70% chance to move over the entire lifespan
  rMoveRate <- calc_move_rate(mu = theta$muF, P = 0.70)

Generally, pathogen prevalence is a more accesible metric for users, but the Imperial equilibrium function requires an annual EIR. This function converts a given pathogen prevalence to an EIR

Description

Generally, pathogen prevalence is a more accesible metric for users, but the Imperial equilibrium function requires an annual EIR. This function converts a given pathogen prevalence to an EIR

Usage

convert_prevalence_to_eir(prevalence, age_vector, ft, params)

Arguments

Value

a vector of the equilibrium number of humans in each SIS stage

This function calculates the human and mosquito equilibrium values for the decoupled Imperial model. Currently this only works in one node.

Description

This function calculates the human and mosquito equilibrium values for the decoupled Imperial model. Currently this only works in one node.

Usage

equilibrium_Imperial_decoupled(age_vector, ft, eir, theta, cube, spn_P)

Arguments

Value

a matrix of the equilibrium number of humans in each Imperial stage by age, and immunity. mosquito equilibrium values, and full theta vector

This function calculates the human equilibrium values for the decoupled Imperial model. Requires the age structure of the population Currently this only works in one node.

Description

This function calculates the human equilibrium values for the decoupled Imperial model. Requires the age structure of the population Currently this only works in one node.

Usage

equilibrium_Imperial_decoupled_human(age_vector, ft, EIR, model_param_list)

Arguments

Value

a matrix of the equilibrium number of humans in each Imperial stage by age, and immunity

Calculate Equilibrium for Mosquito SEI - Human Imperial Model

Description

In decoupled sampling, human states are handled separately from mosquito states. The function equilibrium_Imperial_decoupled_human calculates the distribution of humans at equilibrium required for the Imperial model of malaria transmission. Here we use parameters from that model to calculate the equilibrium states of Susceptible-Exposed-Infectious (SEI) female mosquitoes

Usage

equilibrium_SEI_Imperial(
  params,
  node_list = "b",
  NF = NULL,
  phi = 0.5,
  NH = NULL,
  log_dd = TRUE,
  spn_P,
  pop_ratio_Aq = NULL,
  pop_ratio_F = NULL,
  pop_ratio_M = NULL,
  pop_ratio_H = 1,
  cube
)

Arguments

Details

Imperial model sampling is currently only supported for one-node dynamics: a single node with mosquitoes parameterized by the distribution of human states. These nodes are set using the node_list parameter. Mosquito-only node equilibrium calls equilibrium_lifeycle, which follows one of two models: classic logistic dynamics or the Lotka-Volterra competition model. This is determined by the parameter log_dd, and it changes elements of the return list: K is returned for logistic dynamics, or gamma is returned for Lotka-Volterra dynamics. This is parameterized with the NF parameter to define the adult female numbers. This parameter only needs to be supplied if there are mosquito-only nodes.

For human and mosquito nodes, this function calculates the number of SEI mosquitoes in each state.

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

The initial population genotype ratios are set by supplying the pop_ratio_Aq, pop_ratio_F, and pop_ratio_M values. The default value is NULL, and the function will use the wild-type alleles provided in the cube object. However, one can supply several different objects to set the initial genotype ratios. All genotypes provided must exist in the cube (this is checked by the function). If a single, named vector is provided, then all patches will be initialized with the same ratios. If a matrix is provided, with the number of columns (and column names) giving the initial genotypes, and a row for each patch, each patch can be set to a different initial ratio. The three parameters do not need to match each other.

The params argument supplies all of the ecological and epidemiological parameters necessary to calculate equilibrium values. This is used to set the initial population distribution and during the simulation to maintain equilibrium. This params must include the following named parameters, noted as being the same as lifecycle parameters, or new for the epidemiological equilibrium

• (Lifecycle parameters)

  • qE: inverse of mean duration of egg stage

  • nE: shape parameter of Erlang-distributed egg stage

  • qL: inverse of mean duration of larval stage

  • nL: shape parameter of Erlang-distributed larval stage

  • qP: inverse of mean duration of pupal stage

  • nP: shape parameter of Erlang-distributed pupal stage

  • muE: egg mortality

  • muL: density-independent larvae mortality

  • muP: pupae mortality

  • muF: adult female mortality, supplied from Imperial equilibrium function

  • muM: adult male mortality, supplied from Imperial equilibrium function

  • beta: egg-laying rate, daily

  • nu: mating rate of unmated females

• (Epidemiological parameters)

  • NH: number of humans, can be a vector

  • FOIv: force of infection on mosquitoes, supplied from Imperial equilibrium function

  • Iv_eq: per-capita proportion of infectious mosquitoes The return list contains all of the parameters necessary later in the simulations.

Value

a vector of the equilibrium number of females in each SEI stage

Calculate Equilibrium for Mosquito SEI - Human SEIR Model

Description

Given prevalence of disease in humans (modeled as an SEIR: Susceptible-Latent-Infected-Recovered process with birth and death) and entomological parameters of transmission, this function calculates the quasi-stationary distribution of adult female mosquitoes across SEI (Susceptible-Exposed-Infectious) stages, allowing for Erlang distributed E stage.

Usage

equilibrium_SEI_SEIR(
  params,
  node_list = "b",
  NF = NULL,
  phi = 0.5,
  NH = NULL,
  log_dd = TRUE,
  spn_P,
  pop_ratio_Aq = NULL,
  pop_ratio_F = NULL,
  pop_ratio_M = NULL,
  pop_ratio_H = c(1, 0, 0, 0),
  cube
)

Arguments

Details

This function handles 3 types of nodes: Human only, mosquito only, and nodes with both. These nodes are set using the node_list parameter. Mosquito-only node equilibrium calls equilibrium_lifeycle, which follows one of two models: classic logistic dynamics or the Lotka-Volterra competition model. This is determined by the parameter log_dd, and it changes elements of the return list: K is returned for logistic dynamics, or gamma is returned for Lotka-Volterra dynamics. This is parameterized with the NF parameter to define the adult female numbers. This parameter only needs to be supplied if there are mosquito-only nodes.

Human-only nodes don't require any equilibrium calculations. These nodes use the NH and pop_ratio_H to set adult human populations and infection rates in nodes. These two parameters only need to be supplied if there are human-only nodes. pop_ratio_H needs to be a matrix with the number of rows equal to the number of human-only patches, and 4 columns. The columns are assumed to be fractions of the population in "S", "E", "I", or "R" states, and every row must sum to 1.

For human and mosquito nodes, this function calls make_Q_SEI to construct the infinitesimal generator matrix which is used to solve for the quasi-stationary (stochastic) or equilibrium (deterministic) distribution of mosquitoes over stages. Parameters are provided by params.

For information on the method used to solve this distribution, see section "3.1.3 Nonsingularity of the Subintensity Matrix" of:

• Bladt, Mogens, and Bo Friis Nielsen. Matrix-exponential distributions in applied probability. Vol. 81. New York: Springer, 2017.

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

The initial population genotype ratios are set by supplying the pop_ratio_Aq, pop_ratio_F, and pop_ratio_M values. The default value is NULL, and the function will use the wild-type alleles provided in the cube object. However, one can supply several different objects to set the initial genotype ratios. All genotypes provided must exist in the cube (this is checked by the function). If a single, named vector is provided, then all patches will be initialized with the same ratios. If a matrix is provided, with the number of columns (and column names) giving the initial genotypes, and a row for each patch, each patch can be set to a different initial ratio. The three parameters do not need to match each other.

The params argument supplies all of the ecological and epidemiological parameters necessary to calculate equilibrium values. This is used to set the initial population distribution and during the simulation to maintain equilibrium. This params must include the following named parameters, noted as being the same as lifecycle parameters, or new for the epidemiological equilibrium

• (Lifecycle parameters)

  • qE: inverse of mean duration of egg stage

  • nE: shape parameter of Erlang-distributed egg stage

  • qL: inverse of mean duration of larval stage

  • nL: shape parameter of Erlang-distributed larval stage

  • qP: inverse of mean duration of pupal stage

  • nP: shape parameter of Erlang-distributed pupal stage

  • muE: egg mortality

  • muL: density-independent larvae mortality

  • muP: pupae mortality

  • muF: adult female mortality

  • muM: adult male mortality

  • beta: egg-laying rate, daily

  • nu: mating rate of unmated females

• (Epidemiological parameters)

  • NH: number of humans, can be a vector

  • X: SEIR prevalence in humans, can be a vector of length 4 for 1 node, or a matrix for many nodes

  • NFX: number of female mosquitoes, only required if any prevalence (X) is zero

  • b: mosquito to human transmission efficiency, can be a vector

  • c: human to mosquito transmission efficiency, can be a vector

  • r: rate of recovery in humans (1/duration of infectiousness)

  • muH: death rate of humans (1/avg lifespan)

  • f: rate of blood feeding

  • Q: human blood index

  • qEIP: related to scale parameter of Gamma distributed EIP (1/qEIP is mean length of EIP)

  • nEIP: shape parameter of Gamma distributed EIP

  • delta: inverse duration of the latent stage (E)

The return list contains all of the parameters necessary later in the simulations.

For equilibrium without epidemiological parameters, see equilibrium_lifeycle. For equilibrium without latent humans (SIS dynamics), see equilibrium_SEI_SIS.

Value

a vector of the equilibrium number of females in each SEI stage

Calculate Equilibrium for Mosquito SEI - Human SIS Model

Description

Given prevalence of disease in humans (modeled as an SIS: Susceptible-Infected-Susceptible process with birth and death) and entomological parameters of transmission, this function calculates the quasi-stationary distribution of adult female mosquitoes across SEI (Susceptible-Exposed-Infectious) stages, allowing for Erlang distributed E stage.

Usage

equilibrium_SEI_SIS(
  params,
  node_list = "b",
  NF = NULL,
  phi = 0.5,
  NH = NULL,
  log_dd = TRUE,
  spn_P,
  pop_ratio_Aq = NULL,
  pop_ratio_F = NULL,
  pop_ratio_M = NULL,
  pop_ratio_H = 1,
  cube
)

Arguments

Details

This function handles 3 types of nodes: Human only, mosquito only, and nodes with both. These nodes are set using the node_list parameter. Mosquito-only node equilibrium calls equilibrium_lifeycle, which follows one of two models: classic logistic dynamics or the Lotka-Volterra competition model. This is determined by the parameter log_dd, and it changes elements of the return list: K is returned for logistic dynamics, or gamma is returned for Lotka-Volterra dynamics. This is parameterized with the NF parameter to define the adult female numbers. This parameter only needs to be supplied if there are mosquito-only nodes.

Human-only nodes don't require any equilibrium calculations. These nodes use the NH and pop_ratio_H to set adult human populations and infection rates in nodes. These two parameters only need to be supplied if there are human-only nodes.

For human and mosquito nodes, this function calls make_Q_SEI to construct the infinitesimal generator matrix which is used to solve for the quasi-stationary (stochastic) or equilibrium (deterministic) distribution of mosquitoes over stages. Parameters are provided by params.

For information on the method used to solve this distribution, see section "3.1.3 Nonsingularity of the Subintensity Matrix" of:

• Bladt, Mogens, and Bo Friis Nielsen. Matrix-exponential distributions in applied probability. Vol. 81. New York: Springer, 2017.

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

The initial population genotype ratios are set by supplying the pop_ratio_Aq, pop_ratio_F, and pop_ratio_M values. The default value is NULL, and the function will use the wild-type alleles provided in the cube object. However, one can supply several different objects to set the initial genotype ratios. All genotypes provided must exist in the cube (this is checked by the function). If a single, named vector is provided, then all patches will be initialized with the same ratios. If a matrix is provided, with the number of columns (and column names) giving the initial genotypes, and a row for each patch, each patch can be set to a different initial ratio. The three parameters do not need to match each other.

The params argument supplies all of the ecological and epidemiological parameters necessary to calculate equilibrium values. This is used to set the initial population distribution and during the simulation to maintain equilibrium. This params must include the following named parameters, noted as being the same as lifecycle parameters, or new for the epidemiological equilibrium

• (Lifecycle parameters)

  • qE: inverse of mean duration of egg stage

  • nE: shape parameter of Erlang-distributed egg stage

  • qL: inverse of mean duration of larval stage

  • nL: shape parameter of Erlang-distributed larval stage

  • qP: inverse of mean duration of pupal stage

  • nP: shape parameter of Erlang-distributed pupal stage

  • muE: egg mortality

  • muL: density-independent larvae mortality

  • muP: pupae mortality

  • muF: adult female mortality

  • muM: adult male mortality

  • beta: egg-laying rate, daily

  • nu: mating rate of unmated females

• (Epidemiological parameters)

  • NH: number of humans, can be a vector

  • X: prevalence in humans, can be a vector

  • NFX: number of female mosquitoes, only required if any prevalence (X) is zero

  • b: mosquito to human transmission efficiency, can be a vector

  • c: human to mosquito transmission efficiency, can be a vector

  • r: rate of recovery in humans (1/duration of infectiousness)

  • muH: death rate of humans (1/avg lifespan)

  • f: rate of blood feeding

  • Q: human blood index

  • qEIP: related to scale parameter of Gamma distributed EIP (1/qEIP is mean length of EIP)

  • nEIP: shape parameter of Gamma distributed EIP

The return list contains all of the parameters necessary later in the simulations.

For equilibrium without epidemiological parameters, see equilibrium_lifeycle. For equilibrium with latent humans (SEIR dynamics), see equilibrium_SEI_SEIR.

For examples of using this function, see: vignette("lifecycle-node", package = "MGDrivE2")

Value

a vector of the equilibrium number of females in each SEI stage

This function calculates the equilibrium values for the decoupled SIS human states. Currently this only works in one node.

Description

This function calculates the equilibrium values for the decoupled SIS human states. Currently this only works in one node.

Usage

equilibrium_SEI_decoupled_human(params)

Arguments

Value

a vector of the equilibrium number of humans in each SIS stage

Calculate Equilibrium for Decoupled Mosquito SEI model. Human states will be handled separately.

Description

Given prevalence of disease in humans (modeled as an SIS: Susceptible-Infected-Susceptible process with birth and death) and entomological parameters of transmission, this function calculates the quasi-stationary distribution of adult female mosquitoes across SEI (Susceptible-Exposed-Infectious) stages, allowing for Erlang distributed E stage.

Usage

equilibrium_SEI_decoupled_mosy(
  params,
  node_list = "b",
  NF = NULL,
  phi = 0.5,
  NH = NULL,
  log_dd = TRUE,
  spn_P,
  pop_ratio_Aq = NULL,
  pop_ratio_F = NULL,
  pop_ratio_M = NULL,
  pop_ratio_H = 1,
  cube
)

Arguments

Details

This function handles 3 types of nodes: Human only, mosquito only, and nodes with both. These nodes are set using the node_list parameter. Mosquito-only node equilibrium calls equilibrium_lifeycle, which follows one of two models: classic logistic dynamics or the Lotka-Volterra competition model. This is determined by the parameter log_dd, and it changes elements of the return list: K is returned for logistic dynamics, or gamma is returned for Lotka-Volterra dynamics. This is parameterized with the NF parameter to define the adult female numbers. This parameter only needs to be supplied if there are mosquito-only nodes.

Human-only nodes don't require any equilibrium calculations. These nodes use the NH and pop_ratio_H to set adult human populations and infection rates in nodes. These two parameters only need to be supplied if there are human-only nodes.

For human and mosquito nodes, this function calls make_Q_SEI to construct the infinitesimal generator matrix which is used to solve for the quasi-stationary (stochastic) or equilibrium (deterministic) distribution of mosquitoes over stages. Parameters are provided by params.

For information on the method used to solve this distribution, see section "3.1.3 Nonsingularity of the Subintensity Matrix" of:

• Bladt, Mogens, and Bo Friis Nielsen. Matrix-exponential distributions in applied probability. Vol. 81. New York: Springer, 2017.

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

The initial population genotype ratios are set by supplying the pop_ratio_Aq, pop_ratio_F, and pop_ratio_M values. The default value is NULL, and the function will use the wild-type alleles provided in the cube object. However, one can supply several different objects to set the initial genotype ratios. All genotypes provided must exist in the cube (this is checked by the function). If a single, named vector is provided, then all patches will be initialized with the same ratios. If a matrix is provided, with the number of columns (and column names) giving the initial genotypes, and a row for each patch, each patch can be set to a different initial ratio. The three parameters do not need to match each other.

The params argument supplies all of the ecological and epidemiological parameters necessary to calculate equilibrium values. This is used to set the initial population distribution and during the simulation to maintain equilibrium. This params must include the following named parameters, noted as being the same as lifecycle parameters, or new for the epidemiological equilibrium

• (Lifecycle parameters)

  • qE: inverse of mean duration of egg stage

  • nE: shape parameter of Erlang-distributed egg stage

  • qL: inverse of mean duration of larval stage

  • nL: shape parameter of Erlang-distributed larval stage

  • qP: inverse of mean duration of pupal stage

  • nP: shape parameter of Erlang-distributed pupal stage

  • muE: egg mortality

  • muL: density-independent larvae mortality

  • muP: pupae mortality

  • muF: adult female mortality

  • muM: adult male mortality

  • beta: egg-laying rate, daily

  • nu: mating rate of unmated females

• (Epidemiological parameters)

  • NH: number of humans, can be a vector

  • X: prevalence in humans, can be a vector

  • NFX: number of female mosquitoes, only required if any prevalence (X) is zero

  • b: mosquito to human transmission efficiency, can be a vector

  • c: human to mosquito transmission efficiency, can be a vector

  • r: rate of recovery in humans (1/duration of infectiousness)

  • muH: death rate of humans (1/avg lifespan)

  • f: rate of blood feeding

  • Q: human blood index

  • qEIP: related to scale parameter of Gamma distributed EIP (1/qEIP is mean length of EIP)

  • nEIP: shape parameter of Gamma distributed EIP

The return list contains all of the parameters necessary later in the simulations.

For equilibrium without epidemiological parameters, see equilibrium_lifeycle. For equilibrium with latent humans (SEIR dynamics), see equilibrium_SEI_SEIR.

For examples of using this function, see: vignette("lifecycle-node", package = "MGDrivE2")

Value

a vector of the equilibrium number of females in each SEI stage

Calculate Equilibrium for Lifecycle Model (Logistic or Lotka-Volterra)

Description

This function calculates deterministic equilibria for the mosquito lifecycle model.

Usage

equilibrium_lifeycle(
  params,
  NF,
  phi = 0.5,
  log_dd = TRUE,
  spn_P,
  pop_ratio_Aq = NULL,
  pop_ratio_F = NULL,
  pop_ratio_M = NULL,
  cube
)

Arguments

Details

Equilibrium can be calculated using one of two models: classic logistic dynamics or following the Lotka-Volterra competition model. This is determined by the parameter log_dd, and it changes elements of the return list: K is returned for logistic dynamics, or gamma is returned for Lotka-Volterra dynamics.

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

The initial population genotype ratios are set by supplying the pop_ratio_Aq, pop_ratio_F, and pop_ratio_M values. The default value is NULL, and the function will use the wild-type alleles provided in the cube object. However, one can supply several different objects to set the initial genotype ratios. All genotypes provided must exist in the cube (this is checked by the function). If a single, named vector is provided, then all patches will be initialized with the same ratios. If a matrix is provided, with the number of columns (and column names) giving the initial genotypes, and a row for each patch, each patch can be set to a different initial ratio. The three parameters do not need to match each other.

The params argument supplies all of the ecological parameters necessary to calculate equilibrium values. This is used to set the initial population distribution and during the simulation to maintain equilibrium. params must include the following named parameters:

• qE: inverse of mean duration of egg stage

• nE: shape parameter of Erlang-distributed egg stage

• qL: inverse of mean duration of larval stage

• nL: shape parameter of Erlang-distributed larval stage

• qP: inverse of mean duration of pupal stage

• nP: shape parameter of Erlang-distributed pupal stage

• muE: egg mortality

• muL: density-independent larvae mortality

• muP: pupae mortality

• muF: adult female mortality

• muM: adult male mortality

• beta: egg-laying rate, daily

• nu: mating rate of unmated females

The return list contains all of the params parameters, along with the density-dependent parameter, either K or gamma. These are the parameters necessary later in the simulations. This was done for compatibility with equilibrium_SEI_SIS, which requires several extra parameters not required further in the simulations.

For equilibrium with epidemiological parameters, see equilibrium_SEI_SIS. For equilibrium with latent humans (SEIR dynamics), see equilibrium_SEI_SEIR.

Value

a list with 3 elements: init a matrix of equilibrium values for every life-cycle stage, params a list of parameters for the simulation, M0 a vector of initial conditions

Calculate Erlang shape parameter

Description

Calculate Erlang shape parameter

Usage

get_shape(cv, q)

Arguments

Value

integer value representing the coefficient of variation in Erlang-distributed life stages.

ODE describing the age-structured Imperial model used in decoupled sampling, which will pass in values of I_V and return the human states for usein the mosquito portion of the model

Description

ODE describing the age-structured Imperial model used in decoupled sampling, which will pass in values of I_V and return the human states for usein the mosquito portion of the model

Usage

human_Imperial_ODE(t, state, parameters)

Arguments

Value

matrix of disease states after integration

Model Parameter List Creation

Description

model_param_list_create creates list of model parameters to be used within equilibrium_init_create

Usage

imperial_model_param_list_create(
  eta = 1/(21 * 365),
  rho = 0.85,
  a0 = 2920,
  sigma2 = 1.67,
  max_age = 100 * 365,
  rA = 1/195,
  rT = 0.2,
  rD = 0.2,
  rU = 1/110.299,
  rP = 1/15,
  dE = 12,
  delayGam = 12.5,
  cD = 0.0676909,
  cT = 0.322 * cD,
  cU = 0.006203,
  gamma1 = 1.82425,
  d1 = 0.160527,
  dID = 3650,
  ID0 = 1.577533,
  kD = 0.476614,
  uD = 9.44512,
  aD = 8001.99,
  fD0 = 0.007055,
  gammaD = 4.8183,
  alphaA = 0.75735,
  alphaU = 0.185624,
  b0 = 0.590076,
  b1 = 0.5,
  dB = 3650,
  IB0 = 43.8787,
  kB = 2.15506,
  uB = 7.19919,
  theta0 = 0.0749886,
  theta1 = 0.0001191,
  iv0 = 1.09629,
  kv = 2.00048,
  av = 2493.41,
  gammaV = 2.91282,
  fvS = 0.141195,
  pctMort = 0.215,
  phi0 = 0.791666,
  phi1 = 0.000737,
  dCA = 10950,
  IC0 = 18.02366,
  kC = 2.36949,
  uCA = 6.06349,
  PM = 0.774368,
  dCM = 67.6952,
  dVM = 76.8365,
  dVA = 30 * 365,
  PVM = 0.195768,
  uVA = 11.4321,
  tau1 = 0.69,
  tau2 = 2.31,
  muF = 0.132,
  nEIP = 3,
  qEIP = 1/10,
  Q0 = 0.92,
  DY = 365,
  thetaB = 0.89,
  thetaI = 0.97,
  r_llin = 0.56,
  s_llin = 0.03,
  r_irs = 0.6,
  s_irs = 0,
  qE = 1/3,
  nE = 2,
  qL = 1/7,
  nL = 3,
  qP = 1/1,
  nP = 2,
  muE = 0.05,
  muL = 0.15,
  muP = 0.05,
  muM = 0.132,
  eps = 58.9,
  nu = 1/(4/24),
  NH = 1000,
  ...
)

Arguments

Value

A named vector of all baseline parameters required by the Imperial malaria model.

This function creates all of the necessary parameters for the Imperial model. Parameters furnished by MGDrivE will be removed from this function. Adapted from: https://github.com/mrc-ide/deterministic-malaria-model/blob/master/R/model_parameters.R

A newer version of the model also includes parameters for severe disease. See: https://github.com/mrc-ide/malariasimulation for details.

Examples

imperial_model_param_list_create(NH=1500)
imperial_model_param_list_create(qE=1/4)

Rate Matrix (Q) for Adult Mosquito SEI Dynamics

Description

Construct the infinitesimal generator matrix for (individual) adult female infection dynamics. Adult females follow SEI (Susceptible-Exposed-Infectious) style dynamics with a Gamma distributed EIP, with a mean duration 1/q and variance 1/nq^2 (following shape-scale parameterization, EIP ~ Gamma(n,1/nq)). This function only constructs the rate matrix for either a single mosquito or cohort that all emerged at the same time (the rate matrix for a population with emergence is infinite in dimension).

Usage

make_Q_Imperial(q, n, mu, FOIv)

Arguments

Value

rate matrix for a single (emergence) cohort of SEI mosquito

Rate Matrix (Q) for Adult Mosquito SEI Dynamics

Description

Construct the infinitesimal generator matrix for (individual) adult female infection dynamics. Adult females follow SEI (Susceptible-Exposed-Infectious) style dynamics with a Gamma distributed EIP, with a mean duration 1/q and variance 1/nq^2 (following shape-scale parameterization, EIP ~ Gamma(n,1/nq)). This function only constructs the rate matrix for either a single mosquito or cohort that all emerged at the same time (the rate matrix for a population with emergence is infinite in dimension).

Usage

make_Q_SEI(q, n, mu, c, a, x)

Arguments

Value

rate matrix for a single (emergence) cohort of SEI mosquito

Convert Stochastic Matrix to Rate Matrix

Description

Given a stochastic matrix, return the rate matrix (infinitesimal generator) that would generate it when exponentiated over the interval of unit time.

Usage

movement_prob2rate(tau)

Arguments

Details

Warning: if the matrix provided has diagonal-only rows (i.e., the location is independent), the rate matrix will return 0 in that row, as there is no movement rate that can generate that scenario.

Value

a list with two elements: gamma negative diagonal of the rate matrix, mat matrix of row normalized off-diagonal elements

Examples

  # generate random matrix for example
  #  This represents a 3-node landscape, with random movement between nodes
  moveMat <- matrix(data = runif(n = 9), nrow = 3, ncol = 3)
  moveMat <- moveMat/rowSums(moveMat)

  moveRate <- movement_prob2rate(tau = moveMat)

Mosquito Death Rates, Comoros Islands

Description

This is a matrix containing estimated mosquito death rates from the Comoros Islands, between Mozambique and Madagascar. It provides hourly death rates over the course of one year.

Usage

data(mu_ts)

Format

matrix with 3 named columns and 8760 rows:

Grande_Comore

Hourly death rates for main island

Moheli

Hourly death rates for second island

Anjouan

Hourly death rates for smallest island

Simulate Trajectory From a SPN Model

Description

This function provides a unified interface to the various simulation algorithms for SPN, returning output sampled at a lattice of time points to the user, and handling various exogenous events that may occur during the simulation (such as release of adult mosquitoes).

Usage

sim_trajectory_CSV(
  x0,
  tmax,
  dt = 1,
  dt_stoch = 0.1,
  folders = "./",
  stage = c("M", "F"),
  S,
  hazards,
  Sout = NULL,
  sampler = "tau",
  method = "lsoda",
  events = NULL,
  batch = NULL,
  verbose = TRUE,
  ...
)

Arguments

Details

dt_stoch is used by the Poisson Time-Step (step_PTS) and Chemical Langevin (step_CLE) methods to approximate the hazards. A smaller dt_stoch provides a better approximation, but will take longer to run.

The stoichiometry matrix (S) is generated in spn_S.

The list of hazards (hazards) come from spn_hazards.

Several samplers are provided. The default is a Poisson Time-Step (step_PTS) method. Other options are Gillespie's Direct Method (step_DM) and a Chemical Langevin sampler (step_CLE). Additionally, for convenience, an ODE "sampler" (step_ODE) is provided for compatibility with other samplers. This function uses methods from deSolve.

If using the ode sampler, several methods are provided in the deSolve package, see ode. For inhomogeneous systems, consider using the "rk4" method to avoid excessive integration times.

Additionally, events objects must follow the format required by deSolve. This was done for consistency, see events for more information.

This function writes all output to .csv files. Each simulation is written to a folder element - the number of repetitions is the number of folders provided. What life-stages get recorded is specified by the stage parameter. All life-stages can be stored, or any subset thereof. Females are split by infection status, i.e. by "S", "E", or "I".

This function tracks state variables specified by argument stage by default; an optional argument Sout can be provided to track number of event firings each time step (for discrete stochastic simulations), or cumulative intensity (for continuous stochastic simulations), or the rate function of particular events for ODE simulation. The matrix must have number of columns equal to number of events in the system (the number of hazard functions), and a row for each tracking variable. If Sout is provided, it outputs an additional csv, "Tracking.csv". The function track_hinf is provided, which builds a matrix to track human infection events.

To return simulations to R for further processing, see sim_trajectory_R.

Value

NULL - prints output to .csv files

Simulate Trajectory From a SPN Model

Description

This function provides a unified interface to the various simulation algorithms for SPN, returning output sampled at a lattice of time points to the user, and handling various exogenous events that may occur during the simulation (such as release of adult mosquitoes). This function is used in decoupled sampling, where the mosquito and human states are separated. This is used primarily when using the Imperial model of malaria transmission.

Usage

sim_trajectory_CSV_decoupled(
  x0,
  h0,
  inf_labels,
  tmax,
  dt = 1,
  dt_stoch = 0.1,
  folders = "./",
  S,
  hazards,
  SPN_P,
  theta,
  Sout = NULL,
  sampler = "tau",
  method = "lsoda",
  events = NULL,
  batch = NULL,
  verbose = TRUE,
  human_ode = "Imperial",
  cube = cube,
  ...
)

Arguments

Details

dt_stoch is used by the Poisson Time-Step (step_PTS) and Chemical Langevin (step_CLE) methods to approximate the hazards. A smaller dt_stoch provides a better approximation, but will take longer to run.

The stoichiometry matrix (S) is generated in spn_S.

The list of hazards (hazards) come from spn_hazards.

Several samplers are provided. The default is a Poisson Time-Step (step_PTS) method. Other options are Gillespie's Direct Method (step_DM) and a Chemical Langevin sampler (step_CLE). Additionally, for convenience, an ODE "sampler" (step_ODE) is provided for compatibility with other samplers. This function uses methods from deSolve.

If using the ode sampler, several methods are provided in the deSolve package, see ode. For inhomogeneous systems, consider using the "rk4" method to avoid excessive integration times.

Additionally, events objects must follow the format required by deSolve. This was done for consistency, see events for more information.

This function writes all output to .csv files. Each simulation is written to a folder element - the number of repetitions is the number of folders provided. For now, only adult mosquito states, human states, clinical incidence, and pathogen prevalence are written to CSVs.

This function tracks state variables specified by argument stage by default; an optional argument Sout can be provided to track number of event firings each time step (for discrete stochastic simulations), or cumulative intensity (for continuous stochastic simulations), or the rate function of particular events for ODE simulation. The matrix must have number of columns equal to number of events in the system (the number of hazard functions), and a row for each tracking variable. If Sout is provided, it outputs an additional csv, "Tracking.csv". The function track_hinf is provided, which builds a matrix to track human infection events.

To return simulations to R for further processing, see sim_trajectory_R.

Value

NULL - prints output to .csv files

Simulate Trajectory From a SPN Model

Description

This function provides a unified interface to the various simulation algorithms for SPN, returning output sampled at a lattice of time points to the user, and handling various exogenous events that may occur during the simulation (such as release of adult mosquitoes).

Usage

sim_trajectory_R(
  x0,
  tmax,
  dt = 1,
  dt_stoch = 0.1,
  num_reps = 1,
  S,
  hazards,
  Sout = NULL,
  sampler = "tau",
  method = "lsoda",
  events = NULL,
  batch = NULL,
  verbose = TRUE,
  ...
)

Arguments

Details

dt_stoch is used by the Poisson Time-Step (step_PTS) and Chemical Langevin (step_CLE) methods to approximate the hazards. A smaller dt_stoch provides a better approximation, but will take longer to run.

The stoichiometry matrix (S) is generated in spn_S.

The list of hazards (hazards) come from spn_hazards.

Several samplers are provided. The default is a Poisson Time-Step (step_PTS) method. Other options are Gillespie's Direct Method (step_DM) and a Chemical Langevin sampler (step_CLE). Additionally, for convenience, an ODE "sampler" (step_ODE) is provided for compatibility with other samplers. This function uses methods from deSolve.

If using the ode sampler, several methods are provided in the deSolve package, see ode. For inhomogeneous systems, consider using the "rk4" method to avoid excessive integration times.

Additionally, events objects must follow the format required by deSolve. This was done for consistency, see events for more information.

This function tracks state variables by default; an optional argument Sout can be provided to track number of event firings each time step (for discrete stochastic simulations), or cumulative intensity (for continuous stochastic simulations), or the rate function of particular events for ODE simulation. The matrix must have number of columns equal to number of events in the system (the number of hazard functions), and a row for each tracking variable. The function track_hinf is provided, which builds a matrix to track human infection events.

To save output as .csv files, see sim_trajectory_CSV.

Value

a list with 2 elements: "state" is the array of returned state values, and "events" will return events tracked with Sout if provided, otherwise is NULL

Simulate Trajectory From a SPN Model

Description

This function provides a unified interface to the various simulation algorithms for SPN, returning output sampled at a lattice of time points to the user, and handling various exogenous events that may occur during the simulation (such as release of adult mosquitoes).

Usage

sim_trajectory_R_decoupled(
  x0,
  h0,
  tmax,
  inf_labels,
  dt = 1,
  dt_stoch = 0.1,
  num_reps = 1,
  S,
  hazards,
  SPN_P,
  theta,
  Sout = NULL,
  sampler = "tau",
  method = "lsoda",
  events = NULL,
  batch = NULL,
  verbose = TRUE,
  human_ode = "Imperial",
  cube = cube,
  ...
)

Arguments

Details

dt_stoch is used by the Poisson Time-Step (step_PTS) and Chemical Langevin (step_CLE) methods to approximate the hazards. A smaller dt_stoch provides a better approximation, but will take longer to run.

The stoichiometry matrix (S) is generated in spn_S.

The list of hazards (hazards) come from spn_hazards.

Several samplers are provided. The default is a Poisson Time-Step (step_PTS) method. Other options are Gillespie's Direct Method (step_DM) and a Chemical Langevin sampler (step_CLE). Additionally, for convenience, an ODE "sampler" (step_ODE) is provided for compatibility with other samplers. This function uses methods from deSolve.

If using the ode sampler, several methods are provided in the deSolve package, see ode. For inhomogeneous systems, consider using the "rk4" method to avoid excessive integration times.

Additionally, events objects must follow the format required by deSolve. This was done for consistency, see events for more information.

This function tracks state variables by default; an optional argument Sout can be provided to track number of event firings each time step (for discrete stochastic simulations), or cumulative intensity (for continuous stochastic simulations), or the rate function of particular events for ODE simulation. The matrix must have number of columns equal to number of events in the system (the number of hazard functions), and a row for each tracking variable. The function track_hinf is provided, which builds a matrix to track human infection events.

To save output as .csv files, see sim_trajectory_CSV.

Value

a list with 2 elements: "state" is the array of returned state values, and "events" will return events tracked with Sout if provided, otherwise is NULL

Simulate Trajectory From one SPN Model

Description

This is an internal function to sim_trajectory_CSV. It does the actual sampling once all of the functions have been checked and setup.

Usage

sim_trajectory_base_CSV(
  x0,
  times,
  stepFun,
  folders,
  stage,
  events0 = NULL,
  batch = NULL,
  Sout = NULL,
  verbose = TRUE
)

Arguments

Value

no return, prints .csv files into provided folders

Simulate Trajectory From one SPN Model

Description

This is an internal function to sim_trajectory_CSV. It does the actual sampling once all of the functions have been checked and setup.

Usage

sim_trajectory_base_CSV_decoupled(
  x0,
  h0,
  SPN_P,
  theta,
  times,
  stepFun,
  events0 = NULL,
  batch = NULL,
  Sout = NULL,
  verbose = TRUE,
  human_ode = "Imperial",
  cube = NULL,
  folders = folders
)

Arguments

Value

no return, prints .csv files into provided folders

Simulate Trajectory From one SPN Model

Description

This is an internal function to sim_trajectory_R. It does the actual sampling once all of the functions have been checked and setup.

Usage

sim_trajectory_base_R(
  x0,
  times,
  num_reps,
  stepFun,
  events = NULL,
  batch = NULL,
  Sout = NULL,
  verbose = TRUE
)

Arguments

Value

matrix of sampled values

Simulate Trajectory From one SPN Model using Imperial Malaria model

Description

This is an internal function to sim_trajectory_R. It does the actual sampling once all of the functions have been checked and setup.

Usage

sim_trajectory_base_R_decoupled_Imperial(
  x0,
  h0,
  SPN_P,
  theta,
  times,
  num_reps,
  stepFun,
  events = NULL,
  batch = NULL,
  Sout = NULL,
  verbose = TRUE,
  cube = NULL
)

Arguments

Value

matrix of sampled values

Simulate Trajectory From one SPN Model using Human SIS model

Description

This is an internal function to sim_trajectory_R. It does the actual sampling once all of the functions have been checked and setup.

Usage

sim_trajectory_base_R_decoupled_SIS(
  x0,
  h0,
  SPN_P,
  theta,
  times,
  num_reps,
  stepFun,
  events = NULL,
  batch = NULL,
  Sout = NULL,
  verbose = TRUE,
  cube = NULL
)

Arguments

Value

matrix of sampled values

Solve for Constant Aquatic Mortality

Description

In MGDrivE, the model was typically solved at equilibrium assuming the density-independent mortality was constant over aquatic stages (eggs, larvae, pupae), given a daily growth rate, r_{M}. Given that growth rate, it solved for that mortality \mu_{Aqua} by relating it with R_{M}, the per-generation growth rate of the population, calculable from r_{M} and the mean duration of life stages. This function uses uniroot to solve for mu_{Aqua}.

Usage

solve_muAqua(params, rm)

Arguments

Details

This function needs the following parameters in params:

• muF: adult female mortality

• beta: rate of egg laying

• phi: sex ratio at emergence

• qE: inverse of mean duration of egg stage

• nE: shape parameter of Erlang-distributed egg stage

• qL: inverse of mean duration of larval stage

• nL: shape parameter of Erlang-distributed larval stage

• qP: inverse of mean duration of pupal stage

• nP: shape parameter of Erlang-distributed pupal stage

Value

location of the root, as provided from uniroot

Examples

theta <- list(qE = 1/4, nE = 2, qL = 1/5, nL = 3, qP = 1/6, nP = 2, muF = 1/12,
             beta = 32, phi = 0.5);
muAqatic <- solve_muAqua(params = theta, rm = 1.096)

Split CSV output by Patch and Aggregate by Mate or Dwell-Stage

Description

This function reads in the output files from sim_trajectory_CSV and splits them into smaller files. The files are output by patch, with the appropriate patch numbers for mosquitoes or humans, and specific stages are aggregated by a given metric.

Usage

split_aggregate_CSV(
  read_dir,
  write_dir = read_dir,
  stage = c("E", "L", "P", "M", "U", "FS", "FE", "FI", "H"),
  spn_P,
  tmax,
  dt,
  erlang = FALSE,
  sum_fem = FALSE,
  rem_file = FALSE,
  verbose = TRUE
)

Arguments

Details

Given the read_dir, this function assumes the follow file structure:

• read_dir

  • repetition 1

    • M.csv

    • FS.csv

    • ...
      

  • repetition 2

    • M.csv

    • FS.csv

    • ...
      

  • repetition 3

  • ...
    

This function expects the write_dir to be empty, and it sets up the same file structure as the read_dir. For a 2-node simulation, the output will be organized similar to:

• write_dir

  • repetition 1

    • M_0001.csv

    • M_0002.csv

    • FS_0001.csv

    • FS_0001.csv

    • ...
      

  • repetition 2

    • M_0001.csv

    • M_0002.csv

    • FS_0001.csv

    • FS_0001.csv

    • ...
      

  • repetition 3

  • ...
    

stage defines which life-stages the function will analyze. These stages must be any combination of: "E", "L", "P", "M", "U", "FS", "FE", "FI", "H". These must come from the set of stages provided to sim_trajectory_CSV via the stage argument. It can be less than what was printed by the simulation, but any extra stages provided, but not printed, will throw a warning and then be ignored.

erlang defines how aquatic (eggs, larvae, and pupae) stages and adult females (only mated females) are aggregated. By default, erlang is FALSE, and all of these stages are summarized by genotype only, combining any Erlang-distributed dwell stages (for eggs, larvae, and pupae) or latent infection (for adult females) stages. If erlang is TRUE, summaries are returned by dwell stage or infection status, combining any genotype information.
Female summaries always combine over mate-genotype, so only female genotypes are returned.

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

tmax, dt define the last sampling time, and each sampling time in-between.

For more details about using this function to process CSV output see: vignette("data-analysis", package = "MGDrivE2")

Value

Writes output to files in write_dir

Split CSV output for decoupled sampling with Imperial malaria model

Description

This function reads in the output files from sim_trajectory_CSV and splits them into smaller files. The files are output by patch, with the appropriate patch numbers for mosquitoes or humans, and specific stages are aggregated by a given metric.

Usage

split_aggregate_CSV_decoupled(
  read_dir,
  write_dir = read_dir,
  spn_P,
  tmax,
  dt,
  human_states,
  sum_fem = FALSE,
  rem_file = FALSE,
  verbose = TRUE,
  erlang = FALSE
)

Arguments

Details

Given the read_dir, this function assumes the follow file structure:

• read_dir

  • repetition 1

    • M.csv

    • FS.csv

    • ...
      

  • repetition 2

    • M.csv

    • FS.csv

    • ...
      

  • repetition 3

  • ...
    

This function expects the write_dir to be empty, and it sets up the same file structure as the read_dir. For a 2-node simulation, the output will be organized similar to:

• write_dir

  • repetition 1

    • M_0001.csv

    • M_0002.csv

    • FS_0001.csv

    • FS_0001.csv

    • ...
      

  • repetition 2

    • M_0001.csv

    • M_0002.csv

    • FS_0001.csv

    • FS_0001.csv

    • ...
      

  • repetition 3

  • ...
    

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

tmax, dt define the last sampling time, and each sampling time in-between.

For more details about using this function to process CSV output see: vignette("data-analysis", package = "MGDrivE2")

Value

Writes output to files in write_dir

Make Places (P) For a Network (SEI Mosquitoes - SEIR Humans)

Description

This function makes the set of places (P) for a SPN model of a metapopulation network for simulation of coupled SEI-SEIR dynamics. It is the network version of spn_P_epiSEIR_node.

Usage

spn_P_epiSEIR_network(node_list, params, cube)

Arguments

Details

The params argument supplies all of the ecological parameters necessary to calculate equilibrium values. This function requires the nE, nL, nP, and nEIP parameters to be specified. For more details, see equilibrium_SEI_SEIR

For examples of using this function, see: vignette("seir-dynamics", package = "MGDrivE2")

Value

a list with two elements: ix contains labeled indices of the places by life stage and node, u is the character vector of places (P)

Make Places (P) For a Node (SEI Mosquitoes - SEIR Humans)

Description

This function makes the set of places (P) for a SPN. It is used alone if our model is a single-node metapopulation for mosquito SEI and human SEIR dynamics; otherwise it is used as part of other functions to make SPN models with larger state spaces (metapopulation models, spn_P_epiSEIR_network).

Usage

spn_P_epiSEIR_node(params, cube)

Arguments

Details

The params argument supplies all of the ecological parameters necessary to calculate equilibrium values. This function requires the nE, nL, nP, and nEIP parameters to be specified. For more details, see equilibrium_SEI_SEIR

For examples of using this function, see: vignette("seir-dynamics", package = "MGDrivE2")

Value

a list with two elements: ix contains labeled indices of the places by life stage, u is the character vector of places (P)

Make Places (P) For a Network (SEI Mosquitoes - SIS Humans)

Description

This function makes the set of places (P) for a SPN model of a metapopulation network for simulation of coupled SEI-SIS dynamics. It is the network version of spn_P_epiSIS_node.

Usage

spn_P_epiSIS_network(node_list, params, cube)

Arguments

Details

The params argument supplies all of the ecological parameters necessary to calculate equilibrium values. This function requires the nE, nL, nP, and nEIP parameters to be specified. For more details, see equilibrium_SEI_SIS

For examples of using this function, see: vignette("epi-network", package = "MGDrivE2")

Value

a list with two elements: ix contains labeled indices of the places by life stage and node_id, u is the character vector of places (P)

Make Places (P) For a Node (SEI Mosquitoes - SIS Humans)

Description

This function makes the set of places (P) for a SPN. It is used alone if our model is a single-node metapopulation for mosquito SEI and human SIS dynamics; otherwise it is used as part of other functions to make SPN models with larger state spaces (metapopulation models, see spn_P_epiSIS_network).

Usage

spn_P_epiSIS_node(params, cube)

Arguments

Details

The params argument supplies all of the ecological parameters necessary to calculate equilibrium values. This function requires the nE, nL, nP, and nEIP parameters to be specified. For more details, see equilibrium_SEI_SIS

For examples of using this function, see: vignette("epi-node", package = "MGDrivE2")

Value

a list with two elements: ix contains labeled indices of the places by life stage, u is the character vector of places (P)

Make Places (P) For a Node (SEI Mosquitoes). Note in the v2 epi module, we only use the SPN framework for the mosquito component of the model. The human compoenent will be handled separately in the sampler, and will be formulated as an ODE. This function makes the set of places (P) for a SPN. It is used alone if our model is a single-node metapopulation for mosquito SEI and dynamics; This is used by both SIS and Imperial transmission models.

Description

The params argument supplies all of the ecological parameters necessary to calculate equilibrium values. This function requires the nE, nL, nP, and nEIP parameters to be specified. For more details, see equilibrium_SEI_SIS

Usage

spn_P_epi_decoupled_node(params, cube)

Arguments

Details

For examples of using this function, see: vignette("epi-node", package = "MGDrivE2")

Value

a list with two elements: ix contains labeled indices of the places by life stage, u is the character vector of places (P)

Make Places (P) For a Network (Mosquitoes only)

Description

This function makes the set of places (P) for a SPN model of a metapopulation network. It is the network version of spn_P_lifecycle_node.

Usage

spn_P_lifecycle_network(num_nodes, params, cube)

Arguments

Details

The params argument supplies all of the ecological parameters necessary to calculate equilibrium values. This function requires the nE, nL, and nP parameters to be specified. For more details, see equilibrium_lifeycle

For examples of using this function, see: vignette("lifecycle-network", package = "MGDrivE2")

Value

a list with two elements: ix contains labeled indices of the places by life stage and node_id, u is the character vector of places (P)

Make Places (P) For a Node (Mosquitoes only)

Description

This function makes the set of places (P) for a SPN. It is used alone if our model is a single-node metapopulation for mosquito dynamics only; otherwise it is used as part of other functions to make SPN models with larger state spaces (metapopulation models, see spn_P_lifecycle_network).

Usage

spn_P_lifecycle_node(params, cube)

Arguments

Details

The params argument supplies all of the ecological parameters necessary to calculate equilibrium values. This function requires the nE, nL, and nP parameters to be specified. For more details, see equilibrium_lifeycle

For examples of using this function, see: vignette("lifecycle-node", package = "MGDrivE2")

Value

a list with two elements: ix contains labeled indices of the places by life stage, u is the character vector of places (P)

Make Post Matrix For a Petri Net

Description

Generate the Post (|v| by |u|) matrix for the SPN. This gives the edges from T to P (output arcs) in the bipartite network.

Usage

spn_Post(spn_P, spn_T)

Arguments

Details

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

The set of transitions (spn_T) is generated from one of the following: spn_T_lifecycle_node, spn_T_lifecycle_network, spn_T_epiSIS_node, spn_T_epiSIS_network, spn_T_epiSEIR_node, spn_T_epiSEIR_network.

Value

a matrix of type dgCMatrix-class

Make Pre Matrix For a Petri Net

Description

Generate the Pre (|v| by |u|) matrix for the SPN. This gives the edges from P to T (input arcs) in the bipartite network.

Usage

spn_Pre(spn_P, spn_T)

Arguments

Details

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

The set of transitions (spn_T) is generated from one of the following: spn_T_lifecycle_node, spn_T_lifecycle_network, spn_T_epiSIS_node, spn_T_epiSIS_network, spn_T_epiSEIR_node, spn_T_epiSEIR_network.

Value

a matrix of type dgCMatrix-class

Make stoichiometry Matrix For a Petri Net

Description

Generate the stoichiometry (|u| by |v|) matrix for the SPN. Each column gives the net effect of that transition firing upon the state space of the model. Internally, this creates a Pre (spn_Pre) and Post (spn_Post) matrix, and then calculates the final stoichiometry.

Usage

spn_S(spn_P, spn_T)

Arguments

Details

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

The set of transitions (spn_T) is generated from one of the following: spn_T_lifecycle_node, spn_T_lifecycle_network, spn_T_epiSIS_node, spn_T_epiSIS_network, spn_T_epiSEIR_node, spn_T_epiSEIR_network.

Value

stoichiometry matrix representing the net effect of a transition in the SPN state model.

Make Transitions (T) For a Network (SEI Mosquitoes - SEIR Humans)

Description

This function makes the set of transitions (T) for a SPN model of a metapopulation network for simulation of coupled SEI-SEIR dynamics. It is the network version of spn_T_epiSEIR_node.

Usage

spn_T_epiSEIR_network(node_list, spn_P, params, cube, h_move, m_move)

Arguments

Details

This function takes the places produced from spn_P_epiSEIR_network and builds all possible transitions between subsets of those places.

The params argument supplies all of the ecological parameters necessary to calculate equilibrium values. This function requires the nE, nL, nP, and nEIP parameters to be specified. For more details, see equilibrium_SEI_SEIR

While this function produces all structural information related to transitions, hazards are produced by a separate function, spn_hazards.

For larger networks, this function may take some time to return, please be patient; the Petri Net modeling formalism trades additional computation time at model initialization for faster sampling of trajectories within a simulation.

Please note, the movement matrices (h_move and m_move) are NOT stochastic matrices, just binary matrices that say if i,j can exchange population. Diagonal elements must be FALSE, and both matrices are checked for validity; the function will stop with errors if the adjacency matrix specifies illegal movement rules (e.g.; mosquito movement from a "h" node to a "b" node)

For examples of using this function, see: vignette("seir-dynamics", package = "MGDrivE2")

Value

a list with two elements: T contains transitions packets as lists, v is the character vector of transitions (T)

Make Transitions (T) For a Node (SEI Mosquitoes - SEIR Humans)

Description

This function makes the set of transitions (T) for a SPN. It is used alone if our model is a single-node metapopulation of mosquito and human dynamics; otherwise it is used as part of other functions to make SPN models with larger state spaces (metapopulation models, see spn_T_epiSEIR_network).

Usage

spn_T_epiSEIR_node(spn_P, params, cube)

Arguments

Details

This function takes the places produced from spn_P_epiSEIR_node and builds all possible transitions between subsets of those places.

The params argument supplies all of the ecological parameters necessary to calculate equilibrium values. This function requires the nE, nL, nP, and nEIP parameters to be specified. For more details, see equilibrium_SEI_SEIR

While this function produces all structural information related to transitions, hazards are produced by a separate function, spn_hazards.

For examples of using this function, see: vignette("seir-dynamics", package = "MGDrivE2")

Value

a list with two elements: T contains transitions packets as lists, v is the character vector of transitions (T)

Make Transitions (T) For a Network (SEI Mosquitoes - SIS Humans)

Description

This function makes the set of transitions (T) for a SPN model of a metapopulation network for simulation of coupled SEI-SIS dynamics. It is the network version of spn_T_epiSIS_node.

Usage

spn_T_epiSIS_network(node_list, spn_P, params, cube, h_move, m_move)

Arguments

Details

This function takes the places produced from spn_P_epiSIS_network and builds all possible transitions between subsets of those places.

The params argument supplies all of the ecological parameters necessary to calculate equilibrium values. This function requires the nE, nL, nP, and nEIP parameters to be specified. For more details, see equilibrium_SEI_SIS

While this function produces all structural information related to transitions, hazards are produced by a separate function, spn_hazards.

For larger networks, this function may take some time to return, please be patient; the Petri Net modeling formalism trades additional computation time at model initialization for faster sampling of trajectories within a simulation.

Please note, the movement matrices (h_move and m_move) are NOT stochastic matrices, just binary matrices that say if i,j can exchange population. Diagonal elements must be FALSE, and both matrices are checked for validity; the function will stop with errors if the adjacency matrix specifies illegal movement rules (e.g.; mosquito movement from a "h" node to a "b" node)

For examples of using this function, see: vignette("epi-network", package = "MGDrivE2")

Value

a list with two elements: T contains transitions packets as lists, v is the character vector of transitions (T)

Make Transitions (T) For a Node (SEI Mosquitoes - SIS Humans)

Description

This function makes the set of transitions (T) for a SPN. It is used alone if our model is a single-node metapopulation of mosquito and human dynamics; otherwise it is used as part of other functions to make SPN models with larger state spaces (metapopulation models, see spn_T_epiSIS_network).

Usage

spn_T_epiSIS_node(spn_P, params, cube)

Arguments

Details

This function takes the places produced from spn_P_epiSIS_node and builds all possible transitions between subsets of those places.

The params argument supplies all of the ecological parameters necessary to calculate equilibrium values. This function requires the nE, nL, nP, and nEIP parameters to be specified. For more details, see equilibrium_SEI_SIS

While this function produces all structural information related to transitions, hazards are produced by a separate function, spn_hazards.

For examples of using this function, see: vignette("epi-node", package = "MGDrivE2")

Value

a list with two elements: T contains transitions packets as lists, v is the character vector of transitions (T)

Make Transitions (T) For a Node (SEI Mosquitoes)

Description

This function makes the set of transitions (T) for a SPN. It is used alone if our model is a single-node metapopulation of mosquito; otherwise it is used as part of other functions to make SPN models with larger state spaces (metapopulation models, see spn_T_epiSIS_network).

Usage

spn_T_epi_decoupled_node(spn_P, params, cube)

Arguments

Details

This function takes the places produced from spn_P_epi_decoupled_node and builds all possible transitions between subsets of those places.

The params argument supplies all of the ecological parameters necessary to calculate equilibrium values. This function requires the nE, nL, nP, and nEIP parameters to be specified. For more details, see equilibrium_SEI_SIS

While this function produces all structural information related to transitions, hazards are produced by a separate function, spn_hazards. This is used by both decoupled SIS and Imperial transmission model sampling. For examples of using this function, see: vignette("epi-node-decoupled", package = "MGDrivE2")

Value

a list with two elements: T contains transitions packets as lists, v is the character vector of transitions (T)

Make Transitions (T) For a Network (Mosquitoes only)

Description

This function makes the set of transitions (T) for a SPN model of a metapopulation network. It is the network version of spn_T_lifecycle_node.

Usage

spn_T_lifecycle_network(spn_P, params, cube, n = NULL, m_move = NULL)

Arguments

Details

This function takes the places produced from spn_P_lifecycle_network and builds all possible transitions between subsets of those places.

The params argument supplies all of the ecological parameters necessary to calculate equilibrium values. This function requires the nE, nL, and nP parameters to be specified. For more details, see equilibrium_lifeycle

While this function produces all structural information related to transitions, hazards are produced by a separate function, spn_hazards.

For larger networks, this function may take some time to return, please be patient; the Petri Net modeling formalism trades additional computation time at model initialization for faster sampling of trajectories within a simulation.

Please note, the movement matrix (m_move) is NOT a stochastic matrices, just a binary matrix that say if i,j can exchange population. Diagonal elements must be FALSE.

At least one of the arguments n and m_move must be provided. If both are provided n is ignored.

For examples of using this function, see: vignette("lifecycle-network", package = "MGDrivE2")

Value

a list with two elements: T contains transitions packets as lists, v is the character vector of transitions (T)

Make Transitions (T) For a Node (Mosquitoes only)

Description

This function makes the set of transitions (T) for a SPN. It is used alone if our model is a single-node metapopulation for mosquito dynamics only; otherwise it is used as part of other functions to make SPN models with larger state spaces (metapopulation models, see spn_T_lifecycle_network).

Usage

spn_T_lifecycle_node(spn_P, params, cube)

Arguments

Details

This function takes the places produced from spn_P_lifecycle_node and builds all possible transitions between subsets of those places.

The params argument supplies all of the ecological parameters necessary to calculate equilibrium values. This function requires the nE, nL, and nP parameters to be specified. For more details, see equilibrium_lifeycle

While this function produces all structural information related to transitions, hazards are produced by a separate function, spn_hazards.

For examples of using this function, see: vignette("lifecycle-node", package = "MGDrivE2")

Value

a list with two elements: T contains transitions packets as lists, v is the character vector of transitions (T)

Make Hazards (Lambda) For a MGDrivE2: Node and Network Simulations

Description

Using the structural (topological) SPN model as well as parameters in the cube and params objects, generate a list (of length |v|) of hazards, each implemented as a function closure.

Usage

spn_hazards(
  spn_P,
  spn_T,
  cube,
  params,
  type = "life",
  log_dd = TRUE,
  exact = TRUE,
  tol = 1e-12,
  verbose = TRUE,
  trap_idx = NULL
)

Arguments

Details

If these hazards will be used in a continuous approximation algorithm, such as an ODE method (step_ODE) or Gillespie's Direct Method (step_DM), it is recommended to use exact=FALSE. If the hazards will be used in an integer state space method, such as tau-leaping (step_PTS) or Chemical Langevin (step_CLE) methods, it is recommended to use exact=TRUE.

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

The set of transitions (spn_T) is generated from one of the following: spn_T_lifecycle_node, spn_T_lifecycle_network, spn_T_epiSIS_node, spn_T_epiSIS_network, spn_T_epiSEIR_node, spn_T_epiSEIR_network.

The params objected is generated from either equilibrium_lifeycle or equilibrium_SEI_SIS; it is the "params" object in the return list. The equilibrium function used must match the type parameter.

The type parameter indicates what type of simulation is being run. It is one of: "life", "SIS", or "SEIR". This must match the params object supplied.

Use of this function is demonstrated in many vignettes, browseVignettes(package = "MGDrivE2")

Value

list of length 2: hazards is a list of named closures for every state transition in the model, flag is a boolean indicating exact or approximate

Make Hazards (Lambda) For a MGDrivE2: Node and Network Simulations

Description

Using the structural (topological) SPN model as well as parameters in the cube and params objects, generate a list (of length |v|) of hazards, each implemented as a function closure.

Usage

spn_hazards_decoupled(
  spn_P,
  spn_T,
  cube,
  params,
  type = "SIS",
  log_dd = TRUE,
  exact = TRUE,
  tol = 1e-12,
  verbose = TRUE
)

Arguments

Details

If these hazards will be used in a continuous approximation algorithm, such as an ODE method (step_ODE) or Gillespie's Direct Method (step_DM), it is recommended to use exact=FALSE. If the hazards will be used in an integer state space method, such as tau-leaping (step_PTS) or Chemical Langevin (step_CLE) methods, it is recommended to use exact=TRUE.

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

The set of transitions (spn_T) is generated from one of the following: spn_T_lifecycle_node, spn_T_lifecycle_network, spn_T_epiSIS_node, spn_T_epiSIS_network, spn_T_epiSEIR_node, spn_T_epiSEIR_network.

The params objected is generated from either equilibrium_lifeycle or equilibrium_SEI_SIS; it is the "params" object in the return list. The equilibrium function used must match the type parameter.

The type parameter indicates what type of simulation is being run. It is one of: "life", "SIS", or "SEIR". This must match the params object supplied.

Use of this function is demonstrated in many vignettes, browseVignettes(package = "MGDrivE2")

Value

list of length 2: hazards is a list of named closures for every state transition in the model, flag is a boolean indicating exact or approximate

Make Chemical Langevin (CLE) Sampler for a SPN model

Description

Make a function closure to implement a chemical Langevin (continuous-state) approximation for a SPN.

Usage

step_CLE(S, Sout, haz, dt = 0.01, maxhaz = 1e+06)

Arguments

Details

The chemical Langevin approximation is a numerical simulation of a Fokker-Planck approximation to the Master equations (Kolmogorov Forwards Equations) governing the stochastic model; the CLE approximation is a second-order approximation that will get the correct mean and variance but higher order moments will be incorrect.

The design of step_CLE is from: Wilkinson, D. J. (2011). Stochastic modeling for systems biology. CRC press

Elements of the N list come from two places: The stoichiometry matrix (S) is generated in spn_S and the hazards (h) come from spn_hazards.

For other samplers, see: step_PTS, step_DM, step_ODE

Value

function closure for use in sim_trajectory_R or sim_trajectory_CSV

Make Gillespie's Direct Method (DM) Sampler for a SPN model

Description

Make a function closure to implement Gillespie's Direct Method sampler for a SPN.

Usage

step_DM(S, Sout, haz, maxhaz = 1e+06)

Arguments

Details

The direct method is an exact sampling algorithm; it simulates each event individually. Because of this it may be extremely slow for non-trivial population sizes, and thus should be used to debug and test rather than for serious Monte Carlo simulation.

The design of step_DM is from: Wilkinson, D. J. (2011). Stochastic modeling for systems biology. CRC press

Elements of the N list come from two places: The stoichiometry matrix (S) is generated in spn_S and the hazards (h) come from spn_hazards.

For other samplers, see: step_CLE, step_PTS, step_ODE

Value

function closure for use in sim_trajectory_R or sim_trajectory_CSV

Make Mean-field Approximation (ODE) Numerical Integrator for a SPN Model

Description

Make a function closure to implement a first order mean-field ODE approximation for a SPN.

Usage

step_ODE(S, Sout, haz, method = "lsoda")

Arguments

Details

This method is equivalent to considering the ODEs describing the time evolution of the mean trajectory (first moment) and setting all higher order moments which appear on the right hand side to zero.

The solvers used within can be found in the deSolve package, see ode. For inhomogeneous systems, consider using the "rk4" method to avoid excessive integration times.

The stoichiometry matrix (S) is generated in spn_S.

The list of hazards (haz) come from spn_hazards.

For other samplers, see: step_CLE, step_PTS, step_DM

Value

function closure for use in sim_trajectory_R or sim_trajectory_CSV

Make Mean-field Approximation (ODE) Numerical Integrator for a SPN Model for Decoupled Epi Dynamics

Description

Make a function closure to implement a first order mean-field ODE approximation for a SPN.

Usage

step_ODE_decoupled(S, Sout, haz, method = "lsoda", human_ode = "SIS")

Arguments

Details

This method is equivalent to considering the ODEs describing the time evolution of the mean trajectory (first moment) and setting all higher order moments which appear on the right hand side to zero.

The solvers used within can be found in the deSolve package, see ode. For inhomogeneous systems, consider using the "rk4" method to avoid excessive integration times.

The stoichiometry matrix (S) is generated in spn_S.

The list of hazards (haz) come from spn_hazards.

For other samplers, see: step_CLE, step_PTS, step_DM

Value

function closure for use in sim_trajectory_R or sim_trajectory_CSV

Make Poisson Time-Step (PTS) Sampler for a SPN Model

Description

Make a function closure to implement a Poisson time-step (tau-leaping with fixed tau) sampler for a SPN.

Usage

step_PTS(S, Sout, haz, dt = 0.01, maxhaz = 1e+06)

Arguments

Details

This sampling algorithm is based on representing a SPN as a set of competing Poisson processes; it thus uses an integer valued state space but approximates the number of events over dt.

The design of step_PTS is from: Wilkinson, D. J. (2011). Stochastic modeling for systems biology. CRC press

Elements of the N list come from two places: The stoichiometry matrix (S) is generated in spn_S and the hazards (h) come from spn_hazards.

For other samplers, see: step_CLE, step_DM, step_ODE

Value

function closure for use in sim_trajectory_R or sim_trajectory_CSV

Make Poisson Time-Step (PTS) Sampler for a SPN Model

Description

Make a function closure to implement a Poisson time-step (tau-leaping with fixed tau) sampler for a SPN.

Usage

step_PTS_decoupled(S, Sout, haz, dt = 0.01, maxhaz = 1e+06, human_ode = "SIS")

Arguments

Details

This sampling algorithm is based on representing a SPN as a set of competing Poisson processes; it thus uses an integer valued state space but approximates the number of events over dt.

The design of step_PTS is from: Wilkinson, D. J. (2011). Stochastic modeling for systems biology. CRC press

Elements of the N list come from two places: The stoichiometry matrix (S) is generated in spn_S and the hazards (h) come from spn_hazards.

For other samplers, see: step_CLE, step_DM, step_ODE

Value

function closure for use in sim_trajectory_R or sim_trajectory_CSV

Summarize Eggs by Genotype

Description

This function summarizes egg stage by genotype. It calls base_aquatic_geno to do all of the work.

Usage

summarize_eggs_geno(out, spn_P)

Arguments

Details

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

The return object depends on the data provided. If the simulation was only 1 node, then no node designation is returned. If only one repetition was performed, no rep designation is returned. Columns always returned include: time, genotype, and value.

For examples of using this function, see: vignette("lifecycle-node", package = "MGDrivE2")

Value

a 3 to 5 column dataframe for plotting with ggplot2

Summarize Eggs by Erlang-Stage

Description

This function summarizes egg stage by Erlang-stages. It calls base_aquatic_stage to do all of the work.

Usage

summarize_eggs_stage(out, spn_P)

Arguments

Details

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

The return object depends on the data provided. If the simulation was only 1 node, then no node designation is returned. If only one repetition was performed, no rep designation is returned. Columns always returned include: time, Erlang-stage, and value.

For examples of using this function, see: vignette("lifecycle-node", package = "MGDrivE2")

Value

a 3 to 5 column dataframe for plotting with ggplot2

Summarize Adult Females (One Node or Metapopulation Network, Lifecycle Model)

Description

For MGDrivE2 simulations of mosquito lifecycle dynamics in a single node or metapopulation network, this function sums over the male mate genotype to get population trajectories of adult female mosquitoes by their genotype.

Usage

summarize_females(out, spn_P)

Arguments

Details

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node or spn_P_lifecycle_network.

The return object depends on the data provided. If the simulation was only 1 node, then no node designation is returned. If only one repetition was performed, no rep designation is returned. Columns always returned include: time, genotype, and value.

For examples of using this function, this or any vignette which visualizes output: vignette("lifecycle-node", package = "MGDrivE2")

Value

a 3 to 5 column dataframe for plotting with ggplot2

Summarize Adult Females (One Node or Metapopulation Network, SEI Mosquitoes)

Description

For MGDrivE2 simulations of mosquito epidemiological dynamics in a single node or metapopulation network, this function sums over the male mate genotype as well as EIP bins to get population trajectories of adult female mosquitoes by their genotype and (S,E,I) status.

Usage

summarize_females_epi(out, spn_P)

Arguments

Details

The places (spn_P) object is generated from one of the following: spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

The return object depends on the data provided. If the simulation was only 1 node, then no node designation is returned. If only one repetition was performed, no rep designation is returned. Columns always returned include: time, inf, genotype, and value.

For examples of using this function, this or any vignette which simulates epi dynamics: vignette("epi-node", package = "MGDrivE2")

Value

a 4 to 6 column dataframe for plotting with ggplot2

Summarize Humans for Imperial Model

Description

The Imperial model outputs six human states for each age compartment. This function accepts the output matrix and the desired index of an age compartment and returns the trajectory of all human states in that given age compartment (default 1)

Usage

summarize_humans_epiImperial(out, index = 1)

Arguments

Value

dataframe for plotting with ggplot2

Summarize Humans (One Node or Metapopulation Network, SEI Mosquitoes - SEIR Humans)

Description

For MGDrivE2 simulations of mosquito epidemiological dynamics in a node or network, this function summarizes human infection status, S, E, I, and R. It uses base_summarize_humans to do all of the work.

Usage

summarize_humans_epiSEIR(out)

Arguments

Details

The return object depends on the data provided. If the simulation was only 1 node, then no node designation is returned. If only one repetition was performed, no rep designation is returned. Columns always returned include: time, inf, genotype, and value.

For examples of using this function, see: vignette("seir-dynamics", package = "MGDrivE2")

Value

a 4 to 6 column dataframe for plotting with ggplot2

Summarize Humans (One Node or Metapopulation Network, SEI Mosquitoes - SIS Humans)

Description

For MGDrivE2 simulations of mosquito epidemiological dynamics in a node or network, this function summarizes human infection status, S and I. It uses base_summarize_humans to do all of the work.

Usage

summarize_humans_epiSIS(out)

Arguments

Details

The return object depends on the data provided. If the simulation was only 1 node, then no node designation is returned. If only one repetition was performed, no rep designation is returned. Columns always returned include: time, inf, genotype, and value.

For examples of using this function, see: vignette("epi-node", package = "MGDrivE2")

Value

a 4 to 6 column dataframe for plotting with ggplot2

Summarize Larvae by Genotype

Description

This function summarizes larval stage by genotype. It calls base_aquatic_geno to do all of the work.

Usage

summarize_larvae_geno(out, spn_P)

Arguments

Details

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

The return object depends on the data provided. If the simulation was only 1 node, then no node designation is returned. If only one repetition was performed, no rep designation is returned. Columns always returned include: time, genotype, and value.

For examples of using this function, see: vignette("lifecycle-node", package = "MGDrivE2")

Value

a 3 to 5 column dataframe for plotting with ggplot2

Summarize Larval by Erlang-Stage

Description

This function summarizes larval stage by Erlang-stages. It calls base_aquatic_stage to do all of the work.

Usage

summarize_larvae_stage(out, spn_P)

Arguments

Details

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

The return object depends on the data provided. If the simulation was only 1 node, then no node designation is returned. If only one repetition was performed, no rep designation is returned. Columns always returned include: time, Erlang-stage, and value.

For examples of using this function, see: vignette("lifecycle-node", package = "MGDrivE2")

Value

a 3 to 5 column dataframe for plotting with ggplot2

Summarize Adult Males (One Node or Metapopulation Network)

Description

For MGDrivE2 simulations of mosquito lifecycle dynamics or human infection dynamics, in a node or metapopulation network, this function summarizes population trajectories of adult male mosquitoes by their genotype.

Usage

summarize_males(out)

Arguments

Details

The return object depends on the data provided. If the simulation was only 1 node, then no node designation is returned. If only one repetition was performed, no rep designation is returned. Columns always returned include: time, genotype, and value.

For examples of using this function, this or any vignette which visualizes output: vignette("lifecycle-node", package = "MGDrivE2")

Value

a 3 to 5 column dataframe for plotting with ggplot2

Summarize Pupal by Genotype

Description

This function summarizes pupal stage by genotype. It calls base_aquatic_geno to do all of the work.

Usage

summarize_pupae_geno(out, spn_P)

Arguments

Details

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

The return object depends on the data provided. If the simulation was only 1 node, then no node designation is returned. If only one repetition was performed, no rep designation is returned. Columns always returned include: time, genotype, and value.

For examples of using this function, see: vignette("lifecycle-node", package = "MGDrivE2")

Value

a 3 to 5 column dataframe for plotting with ggplot2

Summarize Pupal by Erlang-Stage

Description

This function summarizes pupal stage by Erlang-stages. It calls base_aquatic_stage to do all of the work.

Usage

summarize_pupae_stage(out, spn_P)

Arguments

Details

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

The return object depends on the data provided. If the simulation was only 1 node, then no node designation is returned. If only one repetition was performed, no rep designation is returned. Columns always returned include: time, Erlang-stage, and value.

For examples of using this function, see: vignette("lifecycle-node", package = "MGDrivE2")

Value

a 3 to 5 column dataframe for plotting with ggplot2

Summary Statistics for MGDrivE2

Description

This function reads in all repetitions for each patch and calculates either the mean, quantiles, or both. User chooses the quantiles, up to 4 decimal places, and enters them as a vector. Quantiles are calculated empirically. (order does not matter)

Usage

summarize_stats_CSV(
  read_dir,
  write_dir = read_dir,
  mean = TRUE,
  quantiles = NULL,
  spn_P,
  tmax,
  dt,
  rem_file = FALSE,
  verbose = TRUE
)

Arguments

Details

Given the read_dir, this function assumes the follow file structure:

• read_dir

  • repetition 1

    • M_0001.csv

    • M_0002.csv

    • FS_0001.csv

    • FS_0001.csv

    • ...
      

  • repetition 2

    • M_0001.csv

    • M_0002.csv

    • FS_0001.csv

    • FS_0001.csv

    • ...
      

  • repetition 3

  • ...
    

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

t0, tt, dt define the first sampling time, the last sampling time, and each sampling time in-between.

Output files are *.csv and contain the mean or quantile in the file name.

For more details about using this function to process CSV output see: vignette("data-analysis", package = "MGDrivE2")

Value

Writes output to files in write_dir

Summary Statistics for MGDrivE2 - Decoupled samples

Description

This function reads in all repetitions for each patch and calculates either the mean, quantiles, or both. User chooses the quantiles, up to 4 decimal places, and enters them as a vector. Quantiles are calculated empirically. (order does not matter)

Usage

summarize_stats_CSV_decoupled(
  read_dir,
  write_dir = read_dir,
  mean = TRUE,
  quantiles = NULL,
  spn_P,
  tmax,
  dt,
  human_states,
  rem_file = FALSE,
  verbose = TRUE
)

Arguments

Details

Given the read_dir, this function assumes the follow file structure:

• read_dir

  • repetition 1

    • M_0001.csv

    • M_0002.csv

    • FS_0001.csv

    • FS_0001.csv

    • ...
      

  • repetition 2

    • M_0001.csv

    • M_0002.csv

    • FS_0001.csv

    • FS_0001.csv

    • ...
      

  • repetition 3

  • ...
    

The places (spn_P) object is generated from one of the following: spn_P_lifecycle_node, spn_P_lifecycle_network, spn_P_epiSIS_node, spn_P_epiSIS_network, spn_P_epiSEIR_node, or spn_P_epiSEIR_network.

t0, tt, dt define the first sampling time, the last sampling time, and each sampling time in-between.

Output files are *.csv and contain the mean or quantile in the file name.

For more details about using this function to process CSV output see: vignette("data-analysis", package = "MGDrivE2")

Value

Writes output to files in write_dir

Make tracking matrix for human infection events

Description

Create a matrix object for tracking incidence in human population to be passed to either sim_trajectory_CSV or sim_trajectory_R.

Usage

track_hinf(spn_T, S)

Arguments

Details

The returned matrix can be passed to the Sout argument of sim_trajectory_CSV or sim_trajectory_R.

Value

a sparseMatrix object