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Apply the delay to generate a point nowcast

Source: R/apply_delay.R
apply_delay.Rd

Generate a point estimate of a completed reporting square (or rectangle) from a reporting triangle that we want to complete with a nowcast and a delay PMF. Each element is computed by taking the product of the expected number of total cases assigned to a reference time $t$ and the proportion of those cases reported on delay $d$. The formula to obtain the expected number of total cases as a function of the reporting delay and previous observations was derived elsewhere. This code was adapted from code written (under an MIT license) by the Karlsruhe Institute of Technology RESPINOW German Hospitalization Nowcasting Hub. Modified from: https://github.com/KITmetricslab/RESPINOW-Hub/blob/7cce3ae2728116e8c8cc0e4ab29074462c24650e/code/baseline/functions.R#L55 #nolint

Usage

apply_delay(reporting_triangle, delay_pmf)

Arguments

reporting_triangle

Matrix of the reporting triangle to be nowcasted, with rows representing the time points of reference and columns representing the delays

delay_pmf

Vector of delays assumed to be indexed starting at the first delay column in reporting_triangle.

Value

point_nowcast_matrix Matrix of the same number of rows and columns as the rep_mat_to_nowcast but with the missing values filled in as point estimates

See also

Point nowcast generation functions fill_triangle(), fill_triangles()

Examples

# Example 1: Standard usage with positive delay PMF
triangle <- matrix(
  c(
    80, 50, 25, 10,
    100, 50, 30, 20,
    90, 45, 25, NA,
    80, 40, NA, NA,
    70, NA, NA, NA
  ),
  nrow = 5,
  byrow = TRUE
)
delay_pmf <- estimate_delay(
  reporting_triangle = triangle,
  max_delay = 3,
  n = 4
)
point_nowcast_matrix <- apply_delay(
  reporting_triangle = triangle,
  delay_pmf = delay_pmf
)
print(point_nowcast_matrix)
#>      [,1]     [,2]     [,3]     [,4]
#> [1,]   80 50.00000 25.00000 10.00000
#> [2,]  100 50.00000 30.00000 20.00000
#> [3,]   90 45.00000 25.00000 17.78889
#> [4,]   80 40.00000 23.20529 15.92281
#> [5,]   70 35.24853 20.35851 13.96745

# Example 2: Using delay PMF with negative entries from downward
# corrections. Create a delay PMF with a negative value representing
# systematic corrections
delay_pmf_negative <- c(0.7, 0.4, -0.15, 0.05)

nowcast_with_corrections <- apply_delay(
  reporting_triangle = example_downward_corr_mat,
  delay_pmf = delay_pmf_negative
)
# The nowcast includes negative predictions at delay 2,
# correctly reflecting expected downward corrections
print(nowcast_with_corrections)
#>      [,1]     [,2]      [,3]      [,4]
#> [1,]  100 60.00000 -20.00000 10.000000
#> [2,]  120 70.00000 -25.00000 15.000000
#> [3,]  110 65.00000 -22.00000 12.000000
#> [4,]  130 75.00000 -28.00000 18.000000
#> [5,]  115 68.00000 -24.00000 14.000000
#> [6,]  125 72.00000 -26.00000  9.002632
#> [7,]  105 62.00000 -22.75909  7.594258
#> [8,]   95 54.45714 -20.36688  6.796856