Visualise batch reporting over the report-date axis

Usage

plot_report_batches(
  x,
  ...,
  signals = c("volume", "delay"),
  threshold = 3,
  backlog_delay = NULL,
  backlog_quantile = 0.9,
  baseline_window = NULL,
  gap_window = 3,
  min_reports = 1,
  by_strata = FALSE,
  strata = NULL,
  palette = .tbl_now_palette()
)

Arguments

x

A tbl_now object.

...

Unused.

signals

Character vector of the signals to require (AND-ed). Any of "volume", "delay", "span", "gap". Defaults to c("volume", "delay").

threshold

Robust-z cutoff a signal must reach to fire (default 3).

backlog_delay

Delay at/above which a report counts as backlog (for the backlog column). NULL (default) uses the backlog_quantile of the observed delays.

backlog_quantile

Delay quantile used to set backlog_delay when it is NULL (default 0.9).

baseline_window

Rolling-baseline width in report periods. NULL (default) uses roughly two months.

gap_window

Number of preceding periods assessed by the "gap" signal (default 3).

min_reports

Minimum reports for a period to be eligible (default 1).

by_strata

Logical (default FALSE). Detect batches separately per stratum.

strata

Character vector of columns to group on when by_strata = TRUE. NULL (default) uses the object's strata.

palette

A named colour palette (defaults to the package palette).

Value

A ggplot2 object.

Details

Two stacked panels over the report date: the reporting volume and the mean reporting delay of each report date, with the batches detected by detect_report_batches() marked. A batch shows up as a spike in both the volume and the delay (a backlog of old cases cleared at once), which is what separates it from an epidemic peak (a volume spike with a normal, short delay).

See also

detect_report_batches()

Examples

data(denguedat)
dengue <- tbl_now(denguedat,
  event_date = "onset_week", report_date = "report_week", verbose = FALSE
)
plot_report_batches(dengue)