Parameterise the model using a baseline prevalence (pfpr2-10) about malariasimulation HOT 4 CLOSED

library(malariasimulation)

# Objective (internal) function
objective <- function(eir, parameters, target_var, target, target_tt, summary_function, tolerance){
  message("Trying EIR: ", round(eir * 365, 4))
  p <- parameters %>%
    set_equilibrium(init_EIR = eir)
  # Setting seed - this might help??
  set.seed(1233445)
  raw <- malariasimulation::run_simulation(timesteps = timesteps, parameters = p)
  summary <- summary_function(raw)
  difference <- (summary[target_tt, target_var] - target)
  message("Current difference: ", round(difference, 3))
  difference[abs(difference) < tolerance] <- 0
  difference <- sum(difference)
  return(difference)
}

# Calibrate (user facing)
# target_var: Name of variable to calibrate to
# target: target values of target_var to calibrate to 
# target_tt: target timesteps at which to match target_var targets
# summary_function: a post-processing function, to allow second order outputs to be calibrated to
# tolerance: the tolerance require for calibration (average error on the same scale as target_var)
calibrate <- function(timesteps, parameters, target_var, target, target_tt, summary_function, tolerance){
  uniroot(objective,
          interval = c(0.01, 500) / 365,
          extendInt = "no",
          parameters = parameters,
          target_var = target_var,
          target = target,
          target_tt = target_tt,
          summary_function = summary_function,
          tolerance = tolerance, 
          trace = 1)
}


###  Example: ##################################################################
# Allow the user to define a processing function for second order output
sf <- function(x){
  x$prev_2_10 <- x$n_detect_730_3650 / x$n_730_3650
  return(x)
}
# Name the target variable to fit to
target_var <- "prev_2_10"
# Define target (in this case prev) to find
target <- c(0.1, 0.1)
# Time points at which to match target
target_tt <- c(500, 600)

p <- get_parameters(list(human_population = 5000))
timesteps <- 1000
out <- calibrate(timesteps = timesteps,
                 parameters = p,
                 target_var = target_var,
                 target = target,
                 target_tt = target_tt,
                 summary_function = sf,
                 tolerance = 0.001)
pars <- p %>%
  set_equilibrium(init_EIR = out$root)
raw <- malariasimulation::run_simulation(timesteps = timesteps, parameters = pars)
summary <- sf(raw)
plot(summary[,c("timestep", target_var)], t = "l", ylim = c(0, 0.3))
points(target ~ target_tt, col = "red", pch = 19)
################################################################################