sample_approx_dist: Approximate Sampling a Distribution using Counts

In epiforecasts/EpiNow: Estimate Realtime Case Counts and Time-varying Epidemiological Parameters

Description

Approximate Sampling a Distribution using Counts

Usage

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 earlies 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 transfrom counts. Supports either "sample" which approximates sampling or "median" would shift by the median of the distribution.
truncate_future	Logical, should cases be truncted 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 <- data.table::as.data.table(EpiSoon::example_obs_cases) 

cases <- cases[, cases := as.integer(cases)] 

## Reported case distribution
print(cases)

## Total cases
sum(cases$cases)

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)
  
## Check that sum is equal to reported cases
total_onsets <- median(
   purrr::map_dbl(1:1000, 
                  ~ sum(sample_approx_dist(cases = cases,
                  dist_fn = delay_fn)$cases))) 
                   
total_onsets
 
                   
## 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")

epiforecasts/EpiNow documentation built on Oct. 26, 2020, 2:38 p.m.