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Extract AUDPC Value from Disease Model Output

Source: R/audpc.R
get_audpc.Rd

Retrieves the pre-calculated Area Under the Disease Progress Curve (AUDPC) from an epicrop.sim object. AUDPC is automatically computed by seir() and stored as an object attribute.

Usage

get_audpc(x)

Arguments

x

An epicrop.sim object returned by seir(), bacterial_blight(), leaf_blast(), or other disease model functions.

Value

Numeric AUDPC value (double).

Purpose

This function provides a safe, documented way to access AUDPC without directly querying attributes. It includes validation to ensure the object is a valid model result with a calculated AUDPC.

AUDPC Interpretation

AUDPC values:

  • Near 0: Minimal disease throughout season (resistant or unfavorable conditions)

  • Mid-range: Moderate disease development

  • Near duration: Severe, sustained disease pressure

Typical ranges depend on disease and duration:

  • 120-day rice season: 0-120 (perfect health to constant epidemic)

  • 240-day wheat season: 0-240

Author

Adam H. Sparks, adamhsparks@gmail.com

Examples

# Example 1: Basic usage - extract AUDPC from model output
wth <- get_wth(
  lonlat = c(121.25562, 14.6774),
  dates = "2000-06-30",
  duration = 120L
)

sim <- bacterial_blight(wth, emergence = "2000-06-30")
audpc <- get_audpc(sim)
paste("Bacterial blight AUDPC:", round(audpc, 2))
#> [1] "Bacterial blight AUDPC: 37.74"

# Example 2: Compare AUDPC across multiple diseases
diseases <- list(
  bb = bacterial_blight(wth, emergence = "2000-06-30"),
  bs = brown_spot(wth, emergence = "2000-06-30"),
  lb = leaf_blast(wth, emergence = "2000-06-30"),
  sb = sheath_blight(wth, emergence = "2000-06-30")
)

audpc_values <- data.frame(
  disease = names(diseases),
  AUDPC = sapply(diseases, get_audpc)
)
audpc_values
#> # Data frame like object (class data.frame) 2 x 4:
#>   │disease│AUDPC
#>   │<chr>  │<dbl>
#> bb│bb     │37.74
#> bs│bs     │ 0.98
#> lb│lb     │40.75
#> sb│sb     │29.02
# Identify most damaging disease
worst <- audpc_values[which.max(audpc_values$AUDPC), ]
paste("Most severe:", worst$disease, "AUDPC =", round(worst$AUDPC, 1))
#> [1] "Most severe: lb AUDPC = 40.8"

# Example 3: Use in analysis pipeline
locations <- list(
  IRRI = c(121.25562, 14.6774),
  Manila = c(120.985, 14.6042)
)

audpc_by_location <- sapply(locations, function(lonlat) {
  wth <- get_wth(lonlat = lonlat, dates = "2000-06-30", duration = 120L)
  sim <- leaf_blast(wth, emergence = "2000-06-30")
  get_audpc(sim)
})

audpc_by_location
#>     IRRI   Manila 
#> 40.75393 40.75393