In pahanc/malariasimulation_Ace_params: An individual based model for malaria

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

suppressPackageStartupMessages(library(ggplot2))
library(malariasimulation)

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),
    prevalence_rendering_min_ages = 0,
    prevalence_rendering_max_ages = 18 * year,
    incidence_rendering_min_ages = 0,
    incidence_rendering_max_ages = 18 * year,
    individual_mosquitoes = FALSE
  )
)

simparams <- set_equilibrium(simparams, starting_EIR)

# Plotting functions
plot_prevalence <- function(output) {
  ggplot(output) + geom_line(
    aes(x = timestep, y = (n_inc_0_6570 / n_0_6570))) + 
    labs(x = "timestep", y = "incidence 0-18")
}

add_intervention_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 Vaccination strategies:

Mass RTS,S

This is a round of RTS,S vaccine for individuals between 5 - 17 months and a booster after 18 months; coverage of 80%; for 10 years:

rtssparams <- simparams

peak <- peak_season_offset(rtssparams)
month <- 30

# Add RTS,S strategy
rtssevents = data.frame(
  timestep = c(1, 2) * year + peak - month, #vaccine efficacy kicks off a month before the peak
  name=c("RTS,S 1", "RTS,S 2")
)

rtssparams <- set_mass_rtss(
  rtssparams,
  timesteps = rtssevents$timestep,
  coverages = rep(0.8, 2),
  min_wait = 2 * 365,
  min_ages = 5 * month,
  max_ages = 17 * year,
  boosters = 18 * month,
  booster_coverage = .7
)

output <- run_simulation(sim_length, rtssparams)

add_intervention_lines(plot_prevalence(output), rtssevents)

You can plot the distribution of doses using the n_rtss_mass_dose_* outputs:

plot_dosage <- function(output, strategy, doses, boosters) {
  distributed <- NULL
  label <- NULL
  timestep <- NULL
  for (dose in seq(doses)) {
    distributed <- c(
      distributed,
      cumsum(output[[paste0('n_rtss_', strategy, '_dose_', dose)]])
    )
    label <- c(label, rep(paste0('dose ', dose), nrow(output)))
    timestep <- c(timestep, output$timestep)
  }
  for (booster in seq(boosters)) {
    distributed <- c(
      distributed,
      cumsum(output[[paste0('n_rtss_', strategy, '_booster_', booster)]])
    )
    label <- c(label, rep(paste0('booster ', booster), nrow(output)))
    timestep <- c(timestep, output$timestep)
  }
  long_output <- data.frame(
    distributed = distributed,
    label = label,
    timestep = timestep
  )
  ggplot(long_output) + geom_line(aes(x = timestep, y = distributed, group=label, color = label))
}

add_intervention_lines(plot_dosage(output, 'mass', 3, 1), rtssevents)

RTS,S EPI

You can opt for a more gradual dosing using the EPI strategy. Individuals will be vaccinated once they reach a certain age...

rtssepiparams <- simparams

# Add RTS,S strategy
rtssepievents = data.frame(
  timestep = c(1, 2) * year,
  name=c("RTS,S EPI start", "RTS,S EPI end")
)

rtssepiparams <- set_rtss_epi(
  rtssepiparams,
  start = rtssepievents$timestep[[1]],
  end = rtssepievents$timestep[[2]],
  age = 5 * month,
  coverage = 0.8,
  min_wait = 0,
  boosters = 18 * month,
  booster_coverage = .7
)

output <- run_simulation(sim_length, rtssepiparams)

add_intervention_lines(plot_dosage(output, 'epi', 3, 1), rtssepievents)

RTS,S seasonal boosters

We can set booster timesteps relative to the start of the year. This allows us to target seasonal dynamics

rtssepiseasonalparams <- simparams

# Add RTS,S seasonal strategy
rtssepiseasonalparams <- set_rtss_epi(
  rtssepiseasonalparams,
  start = rtssepievents$timestep[[1]],
  end = rtssepievents$timestep[[2]],
  age = 5 * month,
  coverage = 0.8,
  min_wait = 6 * month,
  boosters = (peak - 3 * month) + c(0, year),
  booster_coverage = rep(.7, 2),
  seasonal_boosters = TRUE
)

output <- run_simulation(5 * year, rtssepiseasonalparams)

add_intervention_lines(plot_dosage(output, 'epi', 3, 2), rtssepievents)

RTS,S dosing

You can try different dosing schedules using the rtss_doses parameter

rtssepiparams2 <- rtssepiparams
rtssepiparams2$rtss_doses <- c(0, 30, 60)
rtssepiparams2 <- set_rtss_epi(
  rtssepiparams2,
  start = rtssepievents$timestep[[1]],
  end = rtssepievents$timestep[[2]],
  age = 5 * month,
  coverage = 0.8,
  min_wait = 0,
  boosters = c(12 * month, 18 * month, 24 * month),
  booster_coverage = c(1, 1, 1)
)

output <- run_simulation(5 * year, rtssepiparams2)
add_intervention_lines(plot_dosage(output, 'epi', 3, 3), rtssepievents)

TBV

This is a round of the TBV (transmission blocking) vaccine at ages 1, 2, 3 and 18; coverage of 80%; for 10 years:

tbvparams <- simparams

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

tbvparams <- set_tbv(
  tbvparams,
  timesteps = tbvevents$timestep,
  coverages = rep(0.9, 2),
  ages = seq(18)
)

output <- run_simulation(sim_length, tbvparams)

add_intervention_lines(plot_prevalence(output), tbvevents)