Vector Control: Bed nets

Parameterisation

Use the get_parameters() function to generate the list of parameters for a perennial profile, accepting the default values to run the simulation from an equilibrium starting point.

year <- 365
sim_length <- 6 * year
human_population <- 1000
starting_EIR <- 50

simparams <- get_parameters(
  list(human_population = human_population)
)

simparams <- set_equilibrium(parameters = simparams, init_EIR = starting_EIR)

output_control <- run_simulation(timesteps = sim_length, parameters = simparams)

simparams$species
#> [1] "gamb"
simparams$species_proportions
#> [1] 1

Simulation

Having established a base set of parameters, we can now create a copy of this parameter list and update it to specify a net distribution campaign. In the example below, we distribute bed nets to a random 50% of the population every three years. It is possible to change the characteristics of the bed nets for each distribution timestep if different types of bed nets are distributed that have different insecticides, different levels of insecticide resistance, etc. This can be done by modifying the matrices for dn0, rn, rnm, and gamman. Because we are using the default proportions of mosquito species and there are two timesteps when nets are distributed, the matrices for dn0, rn, and rnm have 1 column and 2 rows.

The parameter values for pyrethroid-only and pyrethroid-PBO nets at various resistance levels can be found in Table S1.3 in the Supplementary Appendix 2 of Sherrard-Smith, et al., 2022.

bednetstimesteps <- c(1, 4) * year # The bed nets will be distributed at the end of the first and the 4th year. 

bednetparams <- set_bednets(
  simparams,
  timesteps = bednetstimesteps,
  coverages = c(.5, .5),  # Each round is distributed to 50% of the population.
  retention = 5 * year, # Nets are kept on average 5 years
  dn0 = matrix(c(.533, .533), nrow = 2, ncol = 1), # Matrix of death probabilities for each mosquito species over time
  rn = matrix(c(.56, .56), nrow = 2, ncol = 1), # Matrix of repelling probabilities for each mosquito species over time
  rnm = matrix(c(.24, .24), nrow = 2, ncol = 1), # Matrix of minimum repelling probabilities for each mosquito species over time
  gamman = rep(2.64 * 365, 2) # Vector of bed net half-lives for each distribution timestep
)

output <- run_simulation(timesteps = sim_length, parameters = bednetparams)

Visualisation

# Plot prevalence
plot_prev()

Comparing coverage and population bed net usage

It is important to understand the difference between the input coverages argument of set_bednets() and the resulting population bed net usage over time in the model. When we set coverages in the above example to 0.5, we are telling the model to distribute bed nets to a random 50% of the population at year one and to a random 50% of the population at year 4. However, the level of average bed net usage is not necessarily equal to 50%. Between these time points, bed net use will slowly decline over time (in reality this decline stems from things like disuse, holes in nets, lost nets, etc.).

The average population bed net usage will be influenced by:

• The size and frequency of distributions specified in set_bednets()
  
• The assumed net retention half life (gamman)
• Correlations in the recipients of nets between rounds

The output from malariasimulation::run_simulation() has a variable n_use_net that shows the number of people using bed nets at any given timestep. We can visualise the proportion of the population using bed nets over time to understand how bed net usage changes.

output$prop_use_net <- output$n_use_net / human_population

plot(x = output$timestep, y = output$prop_use_net, type = "l", 
     col = cols[3], lwd = 2.5, ylim = c(0,1),
     xlab = "Timestep (days)", ylab = "Proportion of population using bed nets",
     xaxs = "i", yaxs = "i")
grid(lty = 2, col = "grey80", lwd = 0.5)
axis(side = 1, lty = 1, col = "black", pos = 0); axis(side = 2, lty = 1, col = "black")