[Soft-deprecated] Convolves cases by a PMF function. This function will soon be removed or replaced with a more robust stan implementation.

sample_approx_dist(
  cases = NULL,
  dist_fn = NULL,
  max_value = 120,
  earliest_allowed_mapped = NULL,
  direction = "backwards",
  type = "sample",
  truncate_future = TRUE
)

Arguments

cases

A dataframe of cases (in date order) with the following variables: date and cases.

dist_fn

Function that takes two arguments with the first being numeric and the second being logical (and defined as dist). Should return the probability density or a sample from the defined distribution. See the examples for more.

max_value

Numeric, maximum value to allow. Defaults to 120 days

earliest_allowed_mapped

A character string representing a date ("2020-01-01"). Indicates the earliest allowed mapped value.

direction

Character string, defato "backwards". Direction in which to map cases. Supports either "backwards" or "forwards".

type

Character string indicating the method to use to transform counts. Supports either "sample" which approximates sampling or "median" would shift by the median of the distribution.

truncate_future

Logical, should cases be truncated if they occur after the first date reported in the data. Defaults to TRUE.

Value

A data.table of cases by date of onset

Examples

cases <- example_confirmed
cases <- cases[, cases := as.integer(confirm)]
print(cases)
#>            date confirm cases
#>   1: 2020-02-22      14    14
#>   2: 2020-02-23      62    62
#>   3: 2020-02-24      53    53
#>   4: 2020-02-25      97    97
#>   5: 2020-02-26      93    93
#>  ---                         
#> 126: 2020-06-26     296   296
#> 127: 2020-06-27     255   255
#> 128: 2020-06-28     175   175
#> 129: 2020-06-29     174   174
#> 130: 2020-06-30     126   126

# total cases
sum(cases$cases)
#> [1] 240581

delay_fn <- function(n, dist, cum) {
  if (dist) {
    pgamma(n + 0.9999, 2, 1) - pgamma(n - 1e-5, 2, 1)
  } else {
    as.integer(rgamma(n, 2, 1))
  }
}

onsets <- sample_approx_dist(
  cases = cases,
  dist_fn = delay_fn
)

# estimated onset distribution
print(onsets)
#>            date cases
#>   1: 2020-02-12     1
#>   2: 2020-02-13     0
#>   3: 2020-02-14     0
#>   4: 2020-02-15     1
#>   5: 2020-02-16     0
#>  ---                 
#> 136: 2020-06-26   225
#> 137: 2020-06-27   184
#> 138: 2020-06-28   124
#> 139: 2020-06-29    71
#> 140: 2020-06-30    32

# check that sum is equal to reported cases
total_onsets <- median(
  purrr::map_dbl(
    1:100,
    ~ sum(sample_approx_dist(
      cases = cases,
      dist_fn = delay_fn
    )$cases)
  )
)
total_onsets
#> [1] 240620.5


# map from onset cases to reported
reports <- sample_approx_dist(
  cases = cases,
  dist_fn = delay_fn,
  direction = "forwards"
)


# map from onset cases to reported using a mean shift
reports <- sample_approx_dist(
  cases = cases,
  dist_fn = delay_fn,
  direction = "forwards",
  type = "median"
)