NA (missing) if the 7-day average is above a
threshold.This function aims to detect spurious zeroes by comparing the 7-day average
of the case counts to a threshold. If the 7-day average is above the
threshold, the zero case count is replaced with NA.
apply_zero_threshold(data, threshold = Inf, obs_column = "confirm")A data.table with the zero threshold applied.
A <data.frame> of disease reports (confirm) by date (date).
confirm must be numeric and date must be in date format. Optionally,
data can also have a logical accumulate column which indicates whether
data should be added to the next data point. This is useful when modelling
e.g. weekly incidence data. See also the fill_missing() function which
helps add the accumulate column with the desired properties when dealing
with non-daily data. If any accumulation is done this happens after
truncation as specified by the truncation argument. If all entries
of confirm are missing (NA) the returned estimates will represent the
prior distributions.
Numeric, defaults to Inf. Indicates if detected zero
cases are meaningful by using a threshold number of cases based on the
7-day average. If the average is above this threshold at the time of a
zero observation count then the zero is replaced with a missing (NA)
count and thus ignored in the likelihood.
Character (default: "confirm"). If given, only the column specified here will be used for checking missingness. This is useful if using a data set that has multiple columns of hwich one of them corresponds to observations that are to be processed here.