A reporting_triangle object demonstrating how to handle systematic downward corrections in reporting data. This represents a realistic case where data quality reviews at delay 2 consistently identify false positives or reclassify cases, producing negative values at that specific delay.
Format
A reporting_triangle object with 8 reference dates and 4 delays:
- reporting_triangle_matrix
8x4 matrix with negative values at delay 2
- reference_dates
8 dates starting from 2024-01-01
- delays_unit
"days"
Details
Use this example to understand:
How to work with negative corrections in delay distributions
The difference between
preprocess = NULL(preserves negatives) andpreprocess = preprocess_negative_values(redistributes)How PMFs and CDFs behave with systematic downward corrections
See also
example_reporting_triangle for a clean example without corrections
estimate_delay()withpreprocessparameterpreprocess_negative_values()to handle negative values
Example datasets
example_reporting_triangle,
germany_covid19_hosp,
syn_nssp_df,
syn_nssp_line_list
Examples
# View the example triangle with downward corrections
example_downward_corr_rt
#> Reporting Triangle
#> Delays unit: days
#> Reference dates: 2024-01-01 to 2024-01-08
#> Max delay: 3
#> Structure: 1
#>
#> 0 1 2 3
#> 2024-01-01 100 60 -20 10
#> 2024-01-02 120 70 -25 15
#> 2024-01-03 110 65 -22 12
#> 2024-01-04 130 75 -28 18
#> 2024-01-05 115 68 -24 14
#> 2024-01-06 125 72 -26 NA
#> 2024-01-07 105 62 NA NA
#> 2024-01-08 95 NA NA NA
# Estimate delay with and without preprocessing
delay_raw <- estimate_delay(example_downward_corr_rt, n = 5,
preprocess = NULL)
delay_processed <- estimate_delay(example_downward_corr_rt, n = 5,
preprocess = preprocess_negative_values)
#> ℹ Negative values detected in reporting triangle and will be corrected
# Compare the resulting PMFs
delay_raw
#> 0 1 2 3
#> 0.66544955 0.38806216 -0.14046823 0.08695652
delay_processed
#> 0 1 2 3
#> 0.64346536 0.26957812 0.00000000 0.08695652