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flyway() doesA starling::murmuration()-style linkage
(e.g. case-to-hospital, or “c2h”) produces one linked record per person,
carrying several milestone dates for the same case: onset, hospital
admission, ICU admission, a complication, death. roost()
aggregates a single date column at a time — exactly what you
want for a simple epi curve, but not directly what you want when the
goal is a rate that compares two of those milestones, like an observed
case-hospitalisation rate.
flyway() runs roost() once per milestone
date, then joins the results into one table, aligned by the same time
unit and stratum, with one n_<name> count column per
event. Named after a flyway — the migratory corridor linking a bird
population’s successive stopover sites — because that’s exactly the
shape of the data: the same cohort, traced through each waypoint of a
single journey.
It’s implemented as a thin wrapper around roost(), not a
change to roost() itself. Every event’s own calendar-grid
zero-filling, NA-date handling, and validation is exactly
roost()’s own — nothing about that carefully-tested logic
is duplicated or re-implemented.
set.seed(7)
n <- 500
linked <- data.frame(
onset_date = as.Date("2024-01-01") + sample(0:89, n, replace = TRUE),
age = sample(0:90, n, replace = TRUE),
stringsAsFactors = FALSE
)
linked <- preening(linked, age_col = "age", scheme = "flucan_sentinel")
# Simulate a care pathway: ~8% of cases are admitted, ~15% of those reach
# ICU, ~5% of those die -- each downstream event's date lags the previous
admitted <- sample(seq_len(n), size = round(n * 0.08))
linked$admission_date <- as.Date(NA)
linked$admission_date[admitted] <- linked$onset_date[admitted] + sample(1:5, length(admitted), TRUE)
icu <- sample(admitted, size = round(length(admitted) * 0.15))
linked$icu_date <- as.Date(NA)
linked$icu_date[icu] <- linked$admission_date[icu] + sample(1:3, length(icu), TRUE)
died <- sample(icu, size = max(1, round(length(icu) * 0.30)))
linked$fatality_date <- as.Date(NA)
linked$fatality_date[died] <- linked$icu_date[died] + sample(1:7, length(died), TRUE)
head(linked[, c("onset_date", "age_group", "admission_date", "icu_date", "fatality_date")])
#> onset_date age_group admission_date icu_date fatality_date
#> 1 2024-02-11 50-64 <NA> <NA> <NA>
#> 2 2024-03-23 50-64 <NA> <NA> <NA>
#> 3 2024-01-31 50-64 <NA> <NA> <NA>
#> 4 2024-03-06 5-15 <NA> <NA> <NA>
#> 5 2024-01-15 16-49 <NA> <NA> <NA>
#> 6 2024-03-30 0-4 <NA> <NA> <NA>linked_monthly <- flyway(
linked,
events = c(
cases = "onset_date",
hospitalisations = "admission_date",
icu = "icu_date",
deaths = "fatality_date"
),
time_unit = "month",
group_cols = "age_group"
)
linked_monthly
#> # A tibble: 15 × 6
#> age_group month n_cases n_hospitalisations n_icu n_deaths
#> <ord> <date> <int> <int> <int> <int>
#> 1 0-4 2024-01-01 11 1 0 0
#> 2 0-4 2024-02-01 13 2 0 0
#> 3 0-4 2024-03-01 4 0 0 0
#> 4 5-15 2024-01-01 21 2 0 0
#> 5 5-15 2024-02-01 16 0 0 0
#> 6 5-15 2024-03-01 25 3 1 0
#> 7 16-49 2024-01-01 65 5 0 0
#> 8 16-49 2024-02-01 59 3 0 0
#> 9 16-49 2024-03-01 67 3 0 0
#> 10 50-64 2024-01-01 32 3 0 0
#> 11 50-64 2024-02-01 22 6 1 0
#> 12 50-64 2024-03-01 26 1 0 0
#> 13 65+ 2024-01-01 49 3 0 0
#> 14 65+ 2024-02-01 45 5 3 1
#> 15 65+ 2024-03-01 45 3 1 1
#>
#> -- roost_meta --------------------------------------
#> time_unit : month
#> date_range : 2024-01-01 to 2024-03-31
#> group_cols : age_group
#> hemisphere : southern
#> n_rows_in : 500 (1452 NA dates dropped)
#>
#> -- flyway events ----------------------------------
#> cases <- onset_date (n_cases)
#> hospitalisations <- admission_date (n_hospitalisations)
#> icu <- icu_date (n_icu)
#> deaths <- fatality_date (n_deaths)Every row is one age_group × month stratum,
with n_cases, n_hospitalisations,
n_icu, and n_deaths sitting side by side —
because every one of those counts was aggregated with the same
time_unit and group_cols, they’re directly
comparable.
linked_monthly$chr_obs <- linked_monthly$n_hospitalisations / linked_monthly$n_cases
linked_monthly$cfr_obs <- linked_monthly$n_deaths / linked_monthly$n_cases
linked_monthly[, c("age_group", "month", "n_cases", "n_hospitalisations", "n_deaths",
"chr_obs", "cfr_obs")]
#> # A tibble: 15 × 7
#> age_group month n_cases n_hospitalisations n_deaths chr_obs cfr_obs
#> <ord> <date> <int> <int> <int> <dbl> <dbl>
#> 1 0-4 2024-01-01 11 1 0 0.0909 0
#> 2 0-4 2024-02-01 13 2 0 0.154 0
#> 3 0-4 2024-03-01 4 0 0 0 0
#> 4 5-15 2024-01-01 21 2 0 0.0952 0
#> 5 5-15 2024-02-01 16 0 0 0 0
#> 6 5-15 2024-03-01 25 3 0 0.12 0
#> 7 16-49 2024-01-01 65 5 0 0.0769 0
#> 8 16-49 2024-02-01 59 3 0 0.0508 0
#> 9 16-49 2024-03-01 67 3 0 0.0448 0
#> 10 50-64 2024-01-01 32 3 0 0.0938 0
#> 11 50-64 2024-02-01 22 6 0 0.273 0
#> 12 50-64 2024-03-01 26 1 0 0.0385 0
#> 13 65+ 2024-01-01 49 3 0 0.0612 0
#> 14 65+ 2024-02-01 45 5 1 0.111 0.0222
#> 15 65+ 2024-03-01 45 3 1 0.0667 0.0222
#>
#> -- roost_meta --------------------------------------
#> time_unit : month
#> date_range : 2024-01-01 to 2024-03-31
#> group_cols : age_group
#> hemisphere : southern
#> n_rows_in : 500 (1452 NA dates dropped)
#>
#> -- flyway events ----------------------------------
#> cases <- onset_date (n_cases)
#> hospitalisations <- admission_date (n_hospitalisations)
#> icu <- icu_date (n_icu)
#> deaths <- fatality_date (n_deaths)These are naive (uncorrected) rates — exactly the quantity
corncrake()’s method = "severity_anchor"
expects as its starting point (see vignette("corncrake")),
since correcting for under-ascertainment means comparing this observed
ratio against a reference rate from a well-ascertained source.
roost()Two events rarely share the same calendar range — deaths lag onset by
definition, so a roost() call on fatality_date
alone would only start its calendar grid wherever the first
death happens to fall. flyway() handles this by
zero-filling after the join: a stratum/month with cases but no
deaths yet gets n_deaths = 0, not NA,
consistent with roost()’s own zero-filling philosophy —
just extended to work correctly across several joined calendar grids
rather than one.
# No NAs in any count column, even though the underlying events have very
# different calendar coverage
colSums(is.na(linked_monthly[, c("n_cases", "n_hospitalisations", "n_icu", "n_deaths")]))
#> n_cases n_hospitalisations n_icu n_deaths
#> 0 0 0 0corncrake()Because flyway() output inherits roost_tbl,
corncrake()’s time_col auto-detection works on
it exactly as it would on plain roost() output — just point
count_col and severity_count_col at the two
event columns you want to compare:
corncrake(
linked_monthly,
count_col = "n_cases",
method = "severity_anchor",
group_by = "age_group",
severity_count_col = "n_deaths",
reference_rate = 0.01,
reference_rate_lower = 0.005,
reference_rate_upper = 0.02,
reference_source = "Illustrative reference IFR"
)[, c("age_group", "month", "n_cases", "n_deaths", "ascertainment_factor", "corrected_count")]
#> # A tibble: 15 × 6
#> age_group month n_cases n_deaths ascertainment_factor corrected_count
#> <ord> <date> <int> <int> <dbl> <dbl>
#> 1 0-4 2024-01-01 11 0 0 0
#> 2 0-4 2024-02-01 13 0 0 0
#> 3 0-4 2024-03-01 4 0 0 0
#> 4 5-15 2024-01-01 21 0 0 0
#> 5 5-15 2024-02-01 16 0 0 0
#> 6 5-15 2024-03-01 25 0 0 0
#> 7 16-49 2024-01-01 65 0 0 0
#> 8 16-49 2024-02-01 59 0 0 0
#> 9 16-49 2024-03-01 67 0 0 0
#> 10 50-64 2024-01-01 32 0 0 0
#> 11 50-64 2024-02-01 22 0 0 0
#> 12 50-64 2024-03-01 26 0 0 0
#> 13 65+ 2024-01-01 49 0 0 0
#> 14 65+ 2024-02-01 45 1 2.22 100
#> 15 65+ 2024-03-01 45 1 2.22 100
#>
#> -- roost_meta --------------------------------------
#> time_unit : month
#> date_range : 2024-01-01 to 2024-03-31
#> group_cols : age_group
#> hemisphere : southern
#> n_rows_in : 500 (1452 NA dates dropped)
#>
#> -- flyway events ----------------------------------
#> cases <- onset_date (n_cases)
#> hospitalisations <- admission_date (n_hospitalisations)
#> icu <- icu_date (n_icu)
#> deaths <- fatality_date (n_deaths)See vignette("corncrake") for the full detail on
method = "severity_anchor", including why the confidence
bounds invert relative to reference_rate’s own bounds.
If events isn’t named, flyway() falls back
to event_1, event_2, … with a message —
usable, but n_hospitalisations is a lot more legible three
months from now than n_event_2:
flyway(linked, events = c("onset_date", "admission_date"))
#> # A tibble: 3 × 3
#> month n_event_1 n_event_2
#> <date> <int> <int>
#> 1 2024-01-01 178 14
#> 2 2024-02-01 155 16
#> 3 2024-03-01 167 10
#>
#> -- roost_meta --------------------------------------
#> time_unit : month
#> date_range : 2024-01-01 to 2024-03-30
#> hemisphere : southern
#> n_rows_in : 500 (460 NA dates dropped)
#>
#> -- flyway events ----------------------------------
#> event_1 <- onset_date (n_event_1)
#> event_2 <- admission_date (n_event_2)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.