Main entry point for running malaria intervention scenarios using the MINTe neural network emulator. This function takes intervention parameters and returns predicted prevalence and clinical cases over time.
Usage
run_minter_scenarios(
scenario_tag = NULL,
res_use,
py_only,
py_pbo,
py_pyrrole,
py_ppf,
prev,
Q0,
phi,
season,
routine,
irs,
irs_future,
lsm,
itn_future = NULL,
net_type_future = NULL,
predictor = c("prevalence", "cases"),
benchmark = FALSE
)Arguments
- scenario_tag
Character vector. Tags/names for each scenario.
- res_use
Numeric vector. Current resistance level (0-1).
- py_only
Numeric vector. Pyrethroid-only net coverage (0-1).
- py_pbo
Numeric vector. PBO net coverage (0-1).
- py_pyrrole
Numeric vector. Pyrrole net coverage (0-1).
- py_ppf
Numeric vector. PPF net coverage (0-1).
- prev
Numeric vector. Current under-5 prevalence (0-1).
- Q0
Numeric vector. Indoor biting proportion (0-1).
- phi
Numeric vector. Bednet usage proportion (0-1).
- season
Numeric vector. Seasonality indicator (0 = perennial, 1 = seasonal).
- routine
Numeric vector. Routine treatment coverage (0-1).
- irs
Numeric vector. Current IRS coverage (0-1).
- irs_future
Numeric vector. Future IRS coverage (0-1).
- lsm
Numeric vector. LSM (Larval Source Management) coverage (0-1).
- itn_future
Numeric vector. Future ITN coverage (0-1). Optional.
- net_type_future
Character vector. Future net type. One of "py_only", "py_pbo", "py_pyrrole", "py_ppf". Optional.
- predictor
Character vector. Which predictions to run: "prevalence", "cases", or both. Default is c("prevalence", "cases").
- benchmark
Logical. If TRUE, print timing benchmarks. Default FALSE.
Value
A list of class "minter_results" containing:
- prevalence
Data frame of prevalence predictions over time
- cases
Data frame of clinical case predictions over time
- scenario_meta
Data frame with per-scenario metadata
- eir_valid
Logical indicating if EIR is within calibrated range
- benchmarks
List of timing information (if benchmark=TRUE)
Examples
if (FALSE) { # \dontrun{
# Single scenario
results <- run_minter_scenarios(
scenario_tag = "example",
res_use = 0.3,
py_only = 0.4,
py_pbo = 0.3,
py_pyrrole = 0.2,
py_ppf = 0.1,
prev = 0.25,
Q0 = 0.92,
phi = 0.85,
season = 1,
routine = 0.1,
irs = 0.0,
irs_future = 0.3,
lsm = 0.0
)
# Access results
head(results$prevalence)
head(results$cases)
} # }