Skip to contents

Estimate known outcomes of cases using a delay distribution

Source: R/estimate_outcomes.R
estimate_outcomes.Rd

Estimates the expected number of individuals with known outcomes from a case and outcome time series of outbreak data, and an epidemiological delay distribution of symptom onset to outcome. When calculating a case fatality risk, the outcomes must be deaths, the delay distribution must be an onset-to-death distribution, and the function returns estimates of the known death outcomes.

Usage

estimate_outcomes(data, delay_density)

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.

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> with the columns in data, and with two additional columns:

  • "estimated_outcomes" for the number of cases with an outcome of interest (usually, death) estimated to be known on the dates specified in data, and

  • u_t for the ratio of cumulative number of estimated known outcomes and the cumulative number of cases reported until each date specified in data.

Details

The ratio u_t represents, for the outbreak, the overall proportion of cases whose outcomes are expected to be known by each day $i$. For an ongoing outbreak with relatively long delays between symptom onset and case outcome, a u_t value of 1.0 may indicate that the outbreak has ended, as the outcomes of all cases are expected to be known.

Examples

# Load Ebola 1976 outbreak data
data("ebola1976")

# estimate severity for each day while correcting for delays
# obtain onset-to-death delay distribution parameters from Barry et al. 2018
# examine the first few rows of the output
estimated_outcomes <- estimate_outcomes(
  data = ebola1976,
  delay_density = function(x) dgamma(x, shape = 2.40, scale = 3.33)
)

head(estimated_outcomes)
#>         date cases deaths estimated_outcomes        u_t
#> 1 1976-08-25     1      0         0.00000000 0.00000000
#> 2 1976-08-26     0      0         0.03323030 0.03323030
#> 3 1976-08-27     0      0         0.06494676 0.09817706
#> 4 1976-08-28     0      0         0.08485286 0.18302992
#> 5 1976-08-29     0      0         0.09400738 0.27703731
#> 6 1976-08-30     0      0         0.09515185 0.37218916