The epidemic process and the reporting process

The same cases, counted on two different clocks. Comparing the two is the single most useful thing you can do to tell a real outbreak from a reporting artifact.

• plot_epidemic_process() counts by event date – when the cases actually happened. Epidemics grow and shrink smoothly, so this curve should be smooth.

• plot_reporting_process() counts by report date – when news of them arrived. Reporting is administrative, so this curve is spiky: weekends, holidays and backlog releases all show up here.

A lone spike in the reporting process with nothing under it in the epidemic process is a batch – a day the system cleared its inbox, not a day people got sick. A spike in both is a genuine surge.

Usage

plot_reporting_process(
  x,
  plotly = FALSE,
  axis = c("report", "validation"),
  palette = .tbl_now_palette()
)

plot_epidemic_process(
  x,
  plotly = FALSE,
  axis = c("report", "validation"),
  palette = .tbl_now_palette()
)

Arguments

x

A tbl_now() object.

plotly

If TRUE, return an interactive plotly widget (hover, zoom) instead of a static ggplot2 plot. Default FALSE.

axis

Which time axis to draw: "report" (default) or "validation". On the validation axis the picture answers the laboratory's version of the question – when results arrived, rather than when reports did. Needs a validation process (see add_validation_date()); cases still "pending" have no validation date and are left out.

palette

A named colour palette. Defaults to the package palette.

Value

A ggplot2 object (or a plotly widget when plotly = TRUE).

Details

Both are facetted by stratum when the object has strata.

See also

diagnostic_plot(), which draws these alongside the rest of the reporting-process gallery; plot_observed_cases() for the epidemic process with the incompleteness cutoff marked; plot_scalogram() to separate the two processes by timescale; diagnose_batches() to test a suspicious spike rather than eyeball it.

Examples

data(denguedat)
dn <- tbl_now(denguedat, onset_week, report_week, verbose = FALSE)

# When cases happened: smooth, because epidemics are.
plot_epidemic_process(dn)


# When news of them arrived: spikier, because reporting is administrative.
plot_reporting_process(dn)