![[Soft-deprecated]](figures/lifecycle-soft-deprecated.svg)
Convolves latent infections to reported cases via an observation model.
Likely to be removed/replaced in later releases by functionality drawing on
the stan implementation.
Usage
report_cases(
case_estimates,
case_forecast = NULL,
delays,
type = "sample",
reporting_effect,
CrIs = c(0.2, 0.5, 0.9)
)Arguments
- case_estimates
A data.table of case estimates with the following variables: date, sample, cases
- case_forecast
A data.table of case forecasts with the following variables: date, sample, cases. If not supplied the default is not to incorporate forecasts.
- delays
A call to
delay_opts()defining delay distributions and options. See the documentation ofdelay_opts()and the examples below for details.- 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.
- reporting_effect
A
data.tablegiving the weekly reporting effect with the following variables:sample(must be the same as innowcast),effect(numeric scaling factor for each weekday),day(numeric 1 - 7 (1 = Monday and 7 = Sunday)). If not supplied then no weekly reporting effect is assumed.- CrIs
Numeric vector of credible intervals to calculate.
Value
A list of data.tables. The first entry contains the following
variables sample, date and cases with the second being summarised
across samples.
Examples
# \donttest{
# define example cases
cases <- example_confirmed[1:40]
# set up example delays
generation_time <- get_generation_time(
disease = "SARS-CoV-2", source = "ganyani"
)
incubation_period <- get_incubation_period(
disease = "SARS-CoV-2", source = "lauer"
)
reporting_delay <- dist_spec(
mean = convert_to_logmean(2, 1), mean_sd = 0.1,
sd = convert_to_logsd(2, 1), sd_sd = 0.1, max = 10
)
# Instead of running them model we use example
# data for speed in this example.
cases <- cases[, cases := as.integer(confirm)]
cases <- cases[, confirm := NULL][, sample := 1]
reported_cases <- report_cases(
case_estimates = cases,
delays = delay_opts(incubation_period + reporting_delay),
type = "sample"
)
print(reported_cases)
#> $samples
#> sample date value
#> 1: 1 2020-02-23 6
#> 2: 1 2020-02-24 10
#> 3: 1 2020-02-25 16
#> 4: 1 2020-02-26 17
#> 5: 1 2020-02-27 25
#> 6: 1 2020-02-28 52
#> 7: 1 2020-02-29 58
#> 8: 1 2020-03-01 76
#> 9: 1 2020-03-02 107
#> 10: 1 2020-03-03 150
#> 11: 1 2020-03-04 165
#> 12: 1 2020-03-05 183
#> 13: 1 2020-03-06 215
#> 14: 1 2020-03-07 274
#> 15: 1 2020-03-08 324
#> 16: 1 2020-03-09 410
#> 17: 1 2020-03-10 497
#> 18: 1 2020-03-11 585
#> 19: 1 2020-03-12 670
#> 20: 1 2020-03-13 834
#> 21: 1 2020-03-14 936
#> 22: 1 2020-03-15 1172
#> 23: 1 2020-03-16 1267
#> 24: 1 2020-03-17 1490
#> 25: 1 2020-03-18 1627
#> 26: 1 2020-03-19 1720
#> 27: 1 2020-03-20 1927
#> 28: 1 2020-03-21 2223
#> 29: 1 2020-03-22 2619
#> 30: 1 2020-03-23 2854
#> 31: 1 2020-03-24 2993
#> 32: 1 2020-03-25 2938
#> 33: 1 2020-03-26 3054
#> 34: 1 2020-03-27 3164
#> 35: 1 2020-03-28 3177
#> 36: 1 2020-03-29 3445
#> 37: 1 2020-03-30 3499
#> 38: 1 2020-03-31 3324
#> 39: 1 2020-04-01 3333
#> sample date value
#>
#> $summarised
#> date median mean sd lower_90 lower_50 lower_20 upper_20 upper_50
#> 1: 2020-02-23 6 6 NA 6 6 6 6 6
#> 2: 2020-02-24 10 10 NA 10 10 10 10 10
#> 3: 2020-02-25 16 16 NA 16 16 16 16 16
#> 4: 2020-02-26 17 17 NA 17 17 17 17 17
#> 5: 2020-02-27 25 25 NA 25 25 25 25 25
#> 6: 2020-02-28 52 52 NA 52 52 52 52 52
#> 7: 2020-02-29 58 58 NA 58 58 58 58 58
#> 8: 2020-03-01 76 76 NA 76 76 76 76 76
#> 9: 2020-03-02 107 107 NA 107 107 107 107 107
#> 10: 2020-03-03 150 150 NA 150 150 150 150 150
#> 11: 2020-03-04 165 165 NA 165 165 165 165 165
#> 12: 2020-03-05 183 183 NA 183 183 183 183 183
#> 13: 2020-03-06 215 215 NA 215 215 215 215 215
#> 14: 2020-03-07 274 274 NA 274 274 274 274 274
#> 15: 2020-03-08 324 324 NA 324 324 324 324 324
#> 16: 2020-03-09 410 410 NA 410 410 410 410 410
#> 17: 2020-03-10 497 497 NA 497 497 497 497 497
#> 18: 2020-03-11 585 585 NA 585 585 585 585 585
#> 19: 2020-03-12 670 670 NA 670 670 670 670 670
#> 20: 2020-03-13 834 834 NA 834 834 834 834 834
#> 21: 2020-03-14 936 936 NA 936 936 936 936 936
#> 22: 2020-03-15 1172 1172 NA 1172 1172 1172 1172 1172
#> 23: 2020-03-16 1267 1267 NA 1267 1267 1267 1267 1267
#> 24: 2020-03-17 1490 1490 NA 1490 1490 1490 1490 1490
#> 25: 2020-03-18 1627 1627 NA 1627 1627 1627 1627 1627
#> 26: 2020-03-19 1720 1720 NA 1720 1720 1720 1720 1720
#> 27: 2020-03-20 1927 1927 NA 1927 1927 1927 1927 1927
#> 28: 2020-03-21 2223 2223 NA 2223 2223 2223 2223 2223
#> 29: 2020-03-22 2619 2619 NA 2619 2619 2619 2619 2619
#> 30: 2020-03-23 2854 2854 NA 2854 2854 2854 2854 2854
#> 31: 2020-03-24 2993 2993 NA 2993 2993 2993 2993 2993
#> 32: 2020-03-25 2938 2938 NA 2938 2938 2938 2938 2938
#> 33: 2020-03-26 3054 3054 NA 3054 3054 3054 3054 3054
#> 34: 2020-03-27 3164 3164 NA 3164 3164 3164 3164 3164
#> 35: 2020-03-28 3177 3177 NA 3177 3177 3177 3177 3177
#> 36: 2020-03-29 3445 3445 NA 3445 3445 3445 3445 3445
#> 37: 2020-03-30 3499 3499 NA 3499 3499 3499 3499 3499
#> 38: 2020-03-31 3324 3324 NA 3324 3324 3324 3324 3324
#> 39: 2020-04-01 3333 3333 NA 3333 3333 3333 3333 3333
#> date median mean sd lower_90 lower_50 lower_20 upper_20 upper_50
#> upper_90
#> 1: 6
#> 2: 10
#> 3: 16
#> 4: 17
#> 5: 25
#> 6: 52
#> 7: 58
#> 8: 76
#> 9: 107
#> 10: 150
#> 11: 165
#> 12: 183
#> 13: 215
#> 14: 274
#> 15: 324
#> 16: 410
#> 17: 497
#> 18: 585
#> 19: 670
#> 20: 834
#> 21: 936
#> 22: 1172
#> 23: 1267
#> 24: 1490
#> 25: 1627
#> 26: 1720
#> 27: 1927
#> 28: 2223
#> 29: 2619
#> 30: 2854
#> 31: 2993
#> 32: 2938
#> 33: 3054
#> 34: 3164
#> 35: 3177
#> 36: 3445
#> 37: 3499
#> 38: 3324
#> 39: 3333
#> upper_90
#>
# }