+
+
+
+
+In malariasimulation
+we can implement the bed
+net intervention by specifying the % of the population who are
+distributed a bed net (and assumed to use it) at given time points.
+We often want to match malariasimulation distributions so that they
+result in and observed time-series of usage that we have data for. The
+netz package can help us to do this.
+
+# Create some example usage data
+data <- data.frame(
+ year = 2000:2009,
+ usage = c(0, 0.02, 0.03, 0.5, 0.4, 0.3, 0.6, 0.5, 0.4, 0.7)
+)
+
+# Estimate the model inputs to achieve the observed usage
+## We assume that
+## Usage is measured at the mid point of the year
+## Distributions occur at the start of the year
+data$model_input <- usage_to_model_distribution(
+ usage = data$usage,
+ usage_timesteps = (data$year - 2000) * 365 + 1 + (365 / 2),
+ distribution_timesteps = (data$year - 2000) * 365 + 1,
+ mean_retention = 700
+)
+
+# Estimate model usage over time to validate
+pred <- data.frame(
+ t <- 1:(365 * 10)
+)
+pred$model_usage <- model_distribution_to_usage(
+ usage_timesteps = pred$t,
+ distribution = data$model_input,
+ distribution_timesteps = (data$year - 2000) * 365 + 1,
+ mean_retention = 700
+)
+
+# Validate with usage outputs from a malariasimulation run
+set.seed(123)
+year <- 365
+sim_length <- 10 * year
+human_population <- 1000
+starting_EIR <- 1
+simparams <- get_parameters(
+ list(human_population = human_population)
+) |>
+ set_equilibrium(init_EIR = starting_EIR) |>
+ set_bednets(
+ timesteps = (data$year - 2000) * 365 + 1,
+ coverages = data$model_input,
+ retention = 700,
+ dn0 = matrix(rep(0.533, 10), nrow = 10, ncol = 1),
+ rn = matrix(rep(0.56, 10), nrow = 10, ncol = 1),
+ rnm = matrix(rep(0.24, 10), nrow = 10, ncol = 1),
+ gamman = rep(2.64 * 365, 10)
+ )
+validate <- run_simulation(timesteps = sim_length, parameters = simparams)
+validate$model_use <- validate$n_use_net / simparams$human_population
+
+ggplot() +
+ geom_line(
+ data = pred,
+ aes(x = 2000 + (t / 365), y = model_usage)
+ ) +
+ geom_line(
+ data = validate,
+ aes(x = 2000 + (timestep / 365), y = model_use),
+ col = "forestgreen"
+ ) +
+ geom_point(
+ data = data,
+ aes(x = year + 0.5, y = usage),
+ col = "darkred",
+ size = 2
+ ) +
+ ylab("Predicted model usage") +
+ xlab("Year") +
+ scale_x_continuous(breaks = 2000:2010) +
+ theme_classic()
+In some cases, the function may struggle to fit the usage
+observations well. This may especially be the case if we are uncertain
+about bed net parameters, such as the rate of net loss, or if early data
+points are spurious.
+Please note, that in these functions we always use the
+net_loss_exp() net retention function, as this aligns with
+the internal implementation in malariasimulation.
+These function assumed random correlation between
+recipients.
+