Infer max_time for custom epidemic processes — infer_max

Infers max_time from a model and a tbl_now. max_time is the number of event-time points the model spans: the epidemic process runs from t = 0 to t = max_time - 1.

Usage

infer_max_time(data, model = diseasenowcasting::model())

Arguments

data: A tbl_now object.
model: A model() object (default model()). Only its dimensions matter here, so any model works as a placeholder.

Value

The number of event-time points in the model (an integer).

Details

This function is useful for building custom epidemic processes whose intensity function loops over time and therefore needs to know the number of event-times before it is written (e.g. an SIR recursion or a random walk with one innovation per time point).

Examples

library(tbl.now)
library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
mpox_pooled <- mpoxdat |>
  ungroup() |>
  summarise(n = sum(n), .by = c(dx_date, dx_report_date))
mpox_tn <- tbl_now(mpox_pooled,
                   event_date  = dx_date,
                   report_date = dx_report_date,
                   case_count  = n,
                   data_type   = "count-incidence",
                   verbose     = FALSE)

# The result is 316: the model spans from time t = 0 (min event_num)
# to time t = 315 (max report_num).
infer_max_time(mpox_tn)
#> [1] 316