adjust_infection_to_report.RdStochastic mapping from cases by date of infection to date of report via date of
onset. Essentially reversal of nowcast_pipeline.
adjust_infection_to_report( infections, delay_def, incubation_def, reporting_effect, reporting_model, type = "sample", return_onset = FALSE, truncate_future = TRUE )
| infections |
|
|---|---|
| delay_def | A single row data.table that defines the delay distribution (model, parameters and maximum delay for each model).
See |
| incubation_def | A single row data.table that defines the incubation distribution (model, parameters and maximum delay for each model).
See |
| reporting_effect | A numeric vector of length 7 that allows the scaling of reported cases by the day on which they report (1 = Monday, 7 = Sunday). By default no scaling occurs. |
| reporting_model | A function that takes a single numeric vector as an argument and returns a single numeric vector. Can be used to apply stochastic reporting effects. See the examples for details. |
| type | Character string indicating the method to use to transfrom counts. Supports either "sample" which approximates sampling or "median" would shift by the median of the distribution. |
| return_onset | Logical, defaults to |
| truncate_future | Logical, should cases be truncted if they occur after the first date reported in the data.
Defaults to |
A data.table containing a date variable (date of report) and a cases variable. If return_onset = TRUE there will be
a third variable reference which indicates what the date variable refers to.
## Define example cases cases <- data.table::as.data.table(EpiSoon::example_obs_cases) cases <- cases[, `:=`(confirm = as.integer(cases), import_status = "local")] ## Define a single report delay distribution delay_def <- EpiNow::lognorm_dist_def(mean = 5, mean_sd = 1, sd = 3, sd_sd = 1, max_value = 30, samples = 1, to_log = TRUE) ## Define a single incubation period incubation_def <- EpiNow::lognorm_dist_def(mean = EpiNow::covid_incubation_period[1, ]$mean, mean_sd = EpiNow::covid_incubation_period[1, ]$mean_sd, sd = EpiNow::covid_incubation_period[1, ]$sd, sd_sd = EpiNow::covid_incubation_period[1, ]$sd_sd, max_value = 30, samples = 1) ## Perform a nowcast nowcast <- nowcast_pipeline(reported_cases = cases, target_date = max(cases$date), delay_defs = delay_def, incubation_defs = incubation_def) infections <- nowcast[type %in% "infection_upscaled" & import_status %in% "local"] infections <- infections[, `:=`(type = NULL, import_status = NULL)] ## Simple mapping report <- adjust_infection_to_report(infections, delay_def, incubation_def) print(report)#> date cases #> 1: 2020-01-16 1 #> 2: 2020-01-17 1 #> 3: 2020-01-18 0 #> 4: 2020-01-19 0 #> 5: 2020-01-20 0 #> 6: 2020-01-21 0 #> 7: 2020-01-22 1 #> 8: 2020-01-23 0 #> 9: 2020-01-24 0 #> 10: 2020-01-25 3 #> 11: 2020-01-26 0 #> 12: 2020-01-27 1 #> 13: 2020-01-28 0 #> 14: 2020-01-29 0 #> 15: 2020-01-30 0 #> 16: 2020-01-31 0 #> 17: 2020-02-01 0 #> 18: 2020-02-02 0 #> 19: 2020-02-03 1 #> 20: 2020-02-04 1 #> 21: 2020-02-05 0 #> 22: 2020-02-06 0 #> 23: 2020-02-07 0 #> 24: 2020-02-08 4 #> 25: 2020-02-09 4 #> 26: 2020-02-10 6 #> 27: 2020-02-11 3 #> 28: 2020-02-12 1 #> 29: 2020-02-13 4 #> 30: 2020-02-14 7 #> 31: 2020-02-15 4 #> 32: 2020-02-16 14 #> 33: 2020-02-17 13 #> 34: 2020-02-18 15 #> 35: 2020-02-19 16 #> 36: 2020-02-20 20 #> 37: 2020-02-21 28 #> 38: 2020-02-22 21 #> 39: 2020-02-23 35 #> 40: 2020-02-24 36 #> 41: 2020-02-25 44 #> 42: 2020-02-26 41 #> 43: 2020-02-27 55 #> 44: 2020-02-28 62 #> 45: 2020-02-29 70 #> 46: 2020-03-01 63 #> 47: 2020-03-02 80 #> 48: 2020-03-03 97 #> 49: 2020-03-04 102 #> 50: 2020-03-05 98 #> 51: 2020-03-06 83 #> 52: 2020-03-07 146 #> 53: 2020-03-08 129 #> 54: 2020-03-09 150 #> 55: 2020-03-10 164 #> 56: 2020-03-11 177 #> 57: 2020-03-12 188 #> 58: 2020-03-13 221 #> 59: 2020-03-14 246 #> date cases## Mapping with a weekly reporting effect report_weekly <- adjust_infection_to_report( infections, delay_def, incubation_def, reporting_effect = c(1.1, rep(1, 4), 0.95, 0.95)) print(report_weekly)#> date cases #> 1: 2020-01-16 1 #> 2: 2020-01-17 0 #> 3: 2020-01-18 0 #> 4: 2020-01-19 0 #> 5: 2020-01-20 0 #> 6: 2020-01-21 1 #> 7: 2020-01-22 0 #> 8: 2020-01-23 1 #> 9: 2020-01-24 1 #> 10: 2020-01-25 0 #> 11: 2020-01-26 0 #> 12: 2020-01-27 0 #> 13: 2020-01-28 0 #> 14: 2020-01-29 0 #> 15: 2020-01-30 1 #> 16: 2020-01-31 0 #> 17: 2020-02-01 0 #> 18: 2020-02-02 0 #> 19: 2020-02-03 1 #> 20: 2020-02-04 0 #> 21: 2020-02-05 2 #> 22: 2020-02-06 2 #> 23: 2020-02-07 2 #> 24: 2020-02-08 4 #> 25: 2020-02-09 2 #> 26: 2020-02-10 1 #> 27: 2020-02-11 6 #> 28: 2020-02-12 10 #> 29: 2020-02-13 8 #> 30: 2020-02-14 9 #> 31: 2020-02-15 1 #> 32: 2020-02-16 6 #> 33: 2020-02-17 13 #> 34: 2020-02-18 17 #> 35: 2020-02-19 25 #> 36: 2020-02-20 21 #> 37: 2020-02-21 28 #> 38: 2020-02-22 21 #> 39: 2020-02-23 32 #> 40: 2020-02-24 34 #> 41: 2020-02-25 40 #> 42: 2020-02-26 52 #> 43: 2020-02-27 49 #> 44: 2020-02-28 53 #> 45: 2020-02-29 70 #> 46: 2020-03-01 63 #> 47: 2020-03-02 61 #> 48: 2020-03-03 91 #> 49: 2020-03-04 87 #> 50: 2020-03-05 108 #> 51: 2020-03-06 106 #> 52: 2020-03-07 121 #> 53: 2020-03-08 131 #> 54: 2020-03-09 181 #> 55: 2020-03-10 144 #> 56: 2020-03-11 166 #> 57: 2020-03-12 184 #> 58: 2020-03-13 220 #> 59: 2020-03-14 228 #> date cases## Map using a deterministic median shift for both delays report_median <- adjust_infection_to_report(infections, delay_def, incubation_def, type = "median") ## Map with a weekly reporting effect and stochastic reporting model report_stochastic <- adjust_infection_to_report( infections, delay_def, incubation_def, reporting_effect = c(1.1, rep(1, 4), 0.95, 0.95), reporting_model = function(n) { out <- suppressWarnings(rnbinom(length(n), as.integer(n), 0.5)) out <- ifelse(is.na(out), 0, out) }) print(report_stochastic)#> date cases #> 1: 2020-01-17 3 #> 2: 2020-01-18 0 #> 3: 2020-01-19 0 #> 4: 2020-01-20 2 #> 5: 2020-01-21 0 #> 6: 2020-01-22 0 #> 7: 2020-01-23 0 #> 8: 2020-01-24 0 #> 9: 2020-01-25 0 #> 10: 2020-01-26 0 #> 11: 2020-01-27 0 #> 12: 2020-01-28 0 #> 13: 2020-01-29 0 #> 14: 2020-01-30 0 #> 15: 2020-01-31 0 #> 16: 2020-02-01 0 #> 17: 2020-02-02 0 #> 18: 2020-02-03 0 #> 19: 2020-02-04 0 #> 20: 2020-02-05 3 #> 21: 2020-02-06 9 #> 22: 2020-02-07 0 #> 23: 2020-02-08 1 #> 24: 2020-02-09 0 #> 25: 2020-02-10 6 #> 26: 2020-02-11 5 #> 27: 2020-02-12 1 #> 28: 2020-02-13 5 #> 29: 2020-02-14 7 #> 30: 2020-02-15 4 #> 31: 2020-02-16 15 #> 32: 2020-02-17 16 #> 33: 2020-02-18 13 #> 34: 2020-02-19 20 #> 35: 2020-02-20 22 #> 36: 2020-02-21 19 #> 37: 2020-02-22 16 #> 38: 2020-02-23 29 #> 39: 2020-02-24 37 #> 40: 2020-02-25 38 #> 41: 2020-02-26 39 #> 42: 2020-02-27 40 #> 43: 2020-02-28 40 #> 44: 2020-02-29 62 #> 45: 2020-03-01 37 #> 46: 2020-03-02 89 #> 47: 2020-03-03 54 #> 48: 2020-03-04 102 #> 49: 2020-03-05 103 #> 50: 2020-03-06 132 #> 51: 2020-03-07 106 #> 52: 2020-03-08 131 #> 53: 2020-03-09 158 #> 54: 2020-03-10 188 #> 55: 2020-03-11 206 #> 56: 2020-03-12 139 #> 57: 2020-03-13 220 #> 58: 2020-03-14 248 #> date cases