In pahanc/malariasimulation_Ace_params: An individual based model for malaria

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

suppressPackageStartupMessages(library(ggplot2))
library(malariasimulation)
library(malariaEquilibrium)
library(reshape2)

Parameterisation

We are going to set the default parameters to run the simulation from an equilibrium.

year <- 365
sim_length <- 3 * year
human_population <- 1000
starting_EIR <- 50

simparams <- get_parameters(
  list(
    human_population = human_population,
    model_seasonality = TRUE, # Let's try a bi-modal model
    g0 = 0.28605,
    g = c(0.20636, -0.0740318, -0.0009293),
    h = c(0.173743, -0.0730962, -0.116019)
  )
)
simparams <- set_equilibrium(simparams, starting_EIR)
simparams <- set_drugs(simparams, list(SP_AQ_params))

# Plotting functions
plot_prevalence <- function(output) {
  ggplot(output) + geom_line(
    aes(x = timestep, y = (n_detect_730_3650/n_730_3650))) + 
    labs(x = "timestep", y = "PfPR2-10")
}

plot_state_counts <- function(output) {
  ggplot(
    melt(
      output[c(
        'timestep',
        'S_count',
        'D_count',
        'A_count',
        'U_count',
        'Tr_count'
      )],
      id.vars='timestep'
    )
  ) + geom_line(
    aes(
      x = timestep,
      y = value,
      group = variable,
      color = variable
    )
  )
}

add_mda_lines <- function(plot, events) {
  plot + geom_vline(
    data = events,
    mapping = aes(xintercept=timestep),
    color="blue"
  ) + geom_text(
    data = events,
    mapping = aes(x = timestep, y = 0, label = name),
    size = 4,
    angle = 90,
    vjust = -0.4,
    hjust = 0
  )
}

Then we can run the simulation for a variety of MDA strategies:

MDA

This is a dose of SP-AQ to 80% of the population once a year.

mdaparams <- simparams

# Add MDA strategy
mda_events = data.frame(
  timestep = c(1, 2) * 365,
  name=c("MDA 1", "MDA 2")
)

mdaparams <- set_mda(
  mdaparams,
  drug = 1,
  timesteps = mda_events$timestep,
  coverages = rep(.8, 2),
  min_age = 0,
  max_age = 200 * 365
)

output <- run_simulation(sim_length, mdaparams)

add_mda_lines(plot_state_counts(output), mda_events)

add_mda_lines(plot_prevalence(output), mda_events)

SMC

This is a dose of SP-AQ to 90% of 2 - 11 year olds once a year a month before the peak season for mosquitos.

smcparams <- simparams

# Add SMC strategy
peak <- peak_season_offset(smcparams)
smc_events = data.frame(
  timestep = c(1, 2) * 365 + peak - 30,
  name=c("SMC 1", "SMC 2")
)
smcparams <- set_smc(
  smcparams,
  drug = 1,
  timesteps = smc_events$timestep,
  coverages = rep(.9, 2),
  min_age = 2 * 365 - 1,
  max_age = 11 * 365
)

output <- run_simulation(sim_length, smcparams)

add_mda_lines(plot_state_counts(output), smc_events)

add_mda_lines(plot_prevalence(output), smc_events)

pahanc/malariasimulation_Ace_params documentation built on March 12, 2024, 2:21 a.m.