Measure how fast the counts of a reporting triangle arrive

Pools the settled weeks of each series, and returns the share of cases reported by each delay day, and the mean delay. Use it to choose max_delay_days, and with period to see reporting speed up or slow down.

Usage

reporting_completion_v1(
  triangle,
  max_delay_days,
  delay_window = NULL,
  period = c("all", "year", "month")
)

Arguments

triangle: The csfmt_reporting_triangle_v3 to measure.
max_delay_days: The delay horizon in days, delay day 0 to max_delay_days - 1.
delay_window: Use only the settled weeks of about this many recent weeks. It counts weeks, not days. NULL uses every settled week.
period: "all" gives one pooled row. "year" and "month" give one row per calendar year or month of the Thursday of each week, so the year is the ISO year.

Value

A data.table with one row per series and period: the identity columns, period, n_settled, mean_delay, complete_by_md, and pct_delay0 to pct_delay<max_delay_days - 1>.

n_settled: the settled weeks with a total above 0.
mean_delay: the mean delay in days over the pooled cases. A report at delay max_delay_days or later counts at max_delay_days - 1, so it is a lower bound.
pct_delayD: the percentage of pooled cases reported by the end of day D from the reference Monday, with no interpolation. pct_delay0 is the Monday itself.

Details

A week is settled when its Monday is at least max_delay_days - 1 days before the as-of date. In each period, the function drops a week with a total of 0. A period with fewer than 3 weeks left gets no row, and no warning.

complete_by_md is always 1

complete_by_md is the last cumulative share of the total. So it is 1 for every series and period, and pct_delay<max_delay_days - 1> is 100. It does NOT show whether reporting continues after max_delay_days.

This function adds the reports past the horizon to the last column, so 100 - pct_delay<max_delay_days - 2> is the share reported on the last day or later. To see the tail, run it again with a larger max_delay_days, and compare mean_delay and the pct_delayD curve.

Examples

monday <- as.Date("2023-01-02") + 7 * rep(0:39, each = 3)
set.seed(1)
d <- data.table::data.table(
  isoyearweek_reference = format(monday, "%G-%V"),
  reporting_date = monday + rep(c(3, 10, 17), 40),
  numerator = rpois(120, c(30, 15, 5)),
  indicator_tag = "x", location_code = "nation", age = "total", sex = "total"
)
d <- d[reporting_date <= as.Date("2023-01-02") + 7 * 39 + 6]
tri <- csfmt_reporting_triangle_v3(
  d,
  id_cols = c("indicator_tag", "location_code", "age", "sex")
)

# One pooled curve over 21 delay days. This series reports on days 3, 10 and
# 17 after the reference Monday, so the curve steps on those three days.
rc <- reporting_completion_v1(tri, max_delay_days = 21)
rc[, .(n_settled, mean_delay, pct_delay3, pct_delay10, pct_delay17)]
#>    n_settled mean_delay pct_delay3 pct_delay10 pct_delay17
#>        <int>      <num>      <num>       <num>       <num>
#> 1:        37       6.59       58.9        89.8         100

# there is one pct_delay column per delay day
sum(grepl("^pct_delay", names(rc)))
#> [1] 21

# sliced by month, to show a change in reporting speed
head(
  reporting_completion_v1(tri, max_delay_days = 21, period = "month")[,
    .(period, n_settled, mean_delay, pct_delay10)
  ],
  3
)
#>     period n_settled mean_delay pct_delay10
#>     <char>     <int>      <num>       <num>
#> 1: 2023-01         4       6.48        91.0
#> 2: 2023-02         4       6.48        90.0
#> 3: 2023-03         5       6.72        88.4