Estimate the ascertainment ratio of a disease — estimate

Estimates the proportion of cases or infections that have been ascertained, given a time-series of cases and deaths, a delay distribution and a baseline severity estimate. The resulting ascertainment estimate is calculated as the ratio of the baseline severity estimate, which is assumed to be the 'true' disease severity, and the delay-adjusted severity estimate.

Usage

estimate_ascertainment(data, severity_baseline, delay_density = NULL)

Arguments

data

A <data.frame> containing the outbreak data. A daily time series with dates or some other absolute indicator of time (e.g. epiday or epiweek) and the numbers of new cases and new deaths at each time point. Note that the required columns are "date" (for the date), "cases" (for the number of reported cases), and "deaths" (for the number of reported deaths) on each day of the outbreak.

Note that the <data.frame> is required to have an unbroken sequence of dates with no missing dates in between. The "date" column must be of class Date (see as.Date()).

Note also that the total number of cases must be greater than the total number of reported deaths.

severity_baseline

A single number in the range 0.0 – 1.0 for the assumed true baseline severity estimate used to estimate the overall ascertainment ratio. Missing by default, which causes the function to error; must be supplied by the user.

delay_density

An optional argument that controls whether delay correction is applied in the severity estimation. May be NULL, for no delay correction, or a function that returns the density function of a distribution to evaluate density at user-specified values, e.g. function(x) stats::dgamma(x = x, shape = 5, scale = 1).

Value

A <data.frame> containing the maximum likelihood estimate estimate and 95% confidence interval of the corrected severity, named "ascertainment_estimate" (for the central estimate), and "ascertainment_low" and "ascertainment_high" for the lower and upper interval limits.

Details

estimate_ascertainment() uses cfr_static() internally to obtain a severity estimate that is compared against the user-specified baseline severity. The profile likelihood method used to obtain the severity estimate is decided by the internal function .estimate_severity() as used in cfr_static(), when delay correction is applied. See the cfr_static() documentation for an explanation of the methods used depending on outbreak size and initial severity guess.

Examples

# get data pre-loaded with the package
data("covid_data")
df_covid_uk <- covid_data[covid_data$country == "United Kingdom", ]

df_covid_uk_subset <- subset(df_covid_uk, date <= "2020-05-31")

# use a severity baseline of 1.4% (0.014) taken from Verity et al. (2020)
# Lancet Infectious Diseases: <https://doi.org/10.1016/S1473-3099(20)30243-7>

# use onset-to-death distribution from Linton et al. (2020)
# J. Clinical Medicine: <https://doi.org/10.3390/jcm9020538>

# subset data until 30th June 2020
data <- df_covid_uk[df_covid_uk$date <= "2020-06-30", ]
estimate_ascertainment(
  data = data,
  delay_density = function(x) dlnorm(x, meanlog = 2.577, sdlog = 0.440),
  severity_baseline = 0.014
)
#>   ascertainment_estimate ascertainment_low ascertainment_high
#> 1             0.06779661        0.06734007         0.06829268