Package index • MGDrivE2

Petri net & Hazards

Functions to construct the Petri net, and generate the hazard functions.

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

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

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

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

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

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

spn_P_epi_decoupled_node(): 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.

spn_S(): Make stoichiometry Matrix For a Petri Net

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

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

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

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

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

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

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

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

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

spn_Post(): Make Post Matrix For a Petri Net

spn_Pre(): Make Pre Matrix For a Petri Net

Equilibrium & Parameters

Functions that are helpful to calculate equilibrium solutions as initial conditions or construct parameter sets for simulation.

calc_move_rate(): Calculate Outbound Movement Rate

movement_prob2rate(): Convert Stochastic Matrix to Rate Matrix

solve_muAqua(): Solve for Constant Aquatic Mortality

get_shape(): Calculate Erlang shape parameter

imperial_model_param_list_create(): Model Parameter List Creation

make_Q_SEI(): Rate Matrix (Q) for Adult Mosquito SEI Dynamics

make_Q_Imperial(): Rate Matrix (Q) for Adult Mosquito SEI Dynamics

equilibrium_SEI_SEIR(): Calculate Equilibrium for Mosquito SEI - Human SEIR Model

equilibrium_SEI_SIS(): Calculate Equilibrium for Mosquito SEI - Human SIS Model

equilibrium_SEI_Imperial(): Calculate Equilibrium for Mosquito SEI - Human Imperial Model

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

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

equilibrium_Imperial_decoupled_human(): 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.

equilibrium_SEI_decoupled_mosy(): Calculate Equilibrium for Decoupled Mosquito SEI model. Human states will be handled separately.

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

convert_prevalence_to_eir(): 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

batch_migration(): Sample Batch Migration Events

mu_ts: Mosquito Death Rates, Comoros Islands

Interventions

These are functions useful for setting up interventions.

add_interventions(): 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)

Simulation

These are the interfaces for sampling trajectories or constructing time stepping functions from a fully constructed Petri net model.

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

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

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

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

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

step_CLE(): Make Chemical Langevin (CLE) Sampler for a SPN model

sim_trajectory_CSV(): Simulate Trajectory From a SPN Model

sim_trajectory_R(): Simulate Trajectory From a SPN Model

sim_trajectory_CSV_decoupled(): Simulate Trajectory From a SPN Model

sim_trajectory_R_decoupled(): Simulate Trajectory From a SPN Model

sim_trajectory_base_CSV(): Simulate Trajectory From one SPN Model

sim_trajectory_base_CSV_decoupled(): Simulate Trajectory From one SPN Model

sim_trajectory_base_R(): Simulate Trajectory From one SPN Model

sim_trajectory_base_R_decoupled_Imperial(): Simulate Trajectory From one SPN Model using Imperial Malaria model

sim_trajectory_base_R_decoupled_SIS(): Simulate Trajectory From one SPN Model using Human SIS model

human_Imperial_ODE(): 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

track_hinf(): Make tracking matrix for human infection events

Output Processing

These functions help users summarize output, either arrays returned to R or CSV files.

split_aggregate_CSV(): Split CSV output by Patch and Aggregate by Mate or Dwell-Stage

split_aggregate_CSV_decoupled(): Split CSV output for decoupled sampling with Imperial malaria model

summarize_stats_CSV(): Summary Statistics for MGDrivE2

summarize_stats_CSV_decoupled(): Summary Statistics for MGDrivE2 - Decoupled samples

summarize_eggs_geno(): Summarize Eggs by Genotype

summarize_eggs_stage(): Summarize Eggs by Erlang-Stage

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

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

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

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

summarize_humans_epiImperial(): Summarize Humans for Imperial Model

summarize_larvae_geno(): Summarize Larvae by Genotype

summarize_larvae_stage(): Summarize Larval by Erlang-Stage

summarize_males(): Summarize Adult Males (One Node or Metapopulation Network)

summarize_pupae_geno(): Summarize Pupal by Genotype

summarize_pupae_stage(): Summarize Pupal by Erlang-Stage