Classify every draw into MEM intensity levels

Fits Moving Epidemic Method (MEM) thresholds for each season from the earlier seasons of the same series, and classifies every draw against them. After ens_collapse(), each week has the share of its draws at each of five levels.

Usage

mem_thresholds_v1(x, ...)

# S3 method for class 'csfmt_ensemble_v3'
mem_thresholds_v1(
  x,
  measure,
  min_seasons = 2,
  prefer_seasons = 5,
  i.seasons = 10,
  min_weeks_per_season = 30,
  exclude_seasons = NULL,
  ...
)

Arguments

x: The csfmt_ensemble_v3 that holds measure.
...: Passed to the method.
measure: The draw matrix to classify, a count or a rate.
min_seasons: The fewest training seasons for a fit.
prefer_seasons: A fit on fewer training seasons is provisional.
i.seasons: The most training seasons in one fit, passed to mem::memmodel().
min_weeks_per_season: The fewest weeks that a training season needs.
exclude_seasons: Seasons to leave out of every training set, written as cstime::isoyearweek_to_season_c() writes them, such as "2019/2020". Use it for a pandemic year or a season with a data gap. An excluded season still gets thresholds, and a message names the excluded seasons in the data.

Value

x with a new draw matrix, <measure>_status, of level codes with a levels attribute. It also adds mem_preepidemic, mem_medium, mem_high, mem_veryhigh and mem_n_seasons, the number of training seasons, to $data. It adds them by reference, so the input ensemble gets them too.

Details

The fit uses the median of the draws of each week. A season is fit only on earlier seasons: those with at least min_weeks_per_season weeks, minus exclude_seasons, and at most the latest i.seasons.

A season gets no thresholds, and NA in every draw, in three cases:

it has fewer than min_seasons training seasons,
fewer than 2 of them hold a non-zero week,
mem::memmodel() fails with its default method and with i.method = 3.

A fit on fewer than prefer_seasons seasons is provisional, and a message counts those seasons.

The levels, coded 1 to 5, are:

preepidemic: below mem_preepidemic, the MEM epidemic threshold,
low: below mem_medium, the 40% intensity threshold,
medium: below mem_high, the 90% intensity threshold,
high: below mem_veryhigh, the 97.5% intensity threshold,
veryhigh: at or above mem_veryhigh.

It needs the mem package.

Examples

# MEM needs several earlier seasons, so this series spans four:
# 212 weeks with a winter peak in each.
if (requireNamespace("mem", quietly = TRUE)) {
  w <- cstime::dates_by_isoyearweek$isoyearweek
  i <- match("2018-30", w)
  n <- 212L
  iyw <- w[i + 0:(n - 1)]
  set.seed(1)
  lam <- 20 + 60 * exp(-(cstime::isoyearweek_to_seasonweek_n(iyw) - 25)^2 / 40)
  ens <- csfmt_ensemble_v3(
    data.table::data.table(
      isoyearweek = iyw, location_code = "nation", age = "total"
    ),
    id_cols = c("location_code", "age"),
    draws = list(numerator_nowcasted = matrix(rpois(n * 50, lam), nrow = n))
  )

  ens <- mem_thresholds_v1(ens, measure = "numerator_nowcasted")

  # each season is fit on earlier seasons only, so the first seasons get none
  print(unique(ens$data[
    !is.na(mem_high),
    .(
      season = cstime::isoyearweek_to_season_c(isoyearweek),
      mem_preepidemic, mem_medium, mem_high, mem_veryhigh, mem_n_seasons
    )
  ]))

  # every draw of a week in the first two seasons is NA
  status <- ens$draws$numerator_nowcasted_status
  print(c(
    weeks = nrow(status),
    weeks_with_no_threshold = sum(apply(status, 1, function(r) all(is.na(r))))
  ))

  # a week with thresholds gets a share of draws at each level
  r <- ens_collapse(ens, probs = 0.5)
  pcols <- grep("_status_prob_", names(r), value = TRUE)
  print(r[
    isoyearweek %in% c("2021-45", "2022-02"),
    c("isoyearweek", pcols),
    with = FALSE
  ])
}
#> mem_thresholds_v1: 3 season(s) fit on < 5 training seasons (provisional); see mem_n_seasons.
#>       season mem_preepidemic mem_medium mem_high mem_veryhigh mem_n_seasons
#>       <char>           <num>      <num>    <num>        <num>         <int>
#> 1: 2020/2021        37.26995   64.43633 81.87090     91.01148             2
#> 2: 2021/2022        40.28893   66.68661 82.29283     90.30798             3
#> 3: 2022/2023        41.83822   71.02438 81.28250     86.27663             4
#>                   weeks weeks_with_no_threshold 
#>                     212                     104 
#>    isoyearweek numerator_nowcasted_status_prob_preepidemic
#>         <char>                                       <num>
#> 1:     2021-45                                        1.00
#> 2:     2022-02                                        0.02
#>    numerator_nowcasted_status_prob_low numerator_nowcasted_status_prob_medium
#>                                  <num>                                  <num>
#> 1:                                0.00                                   0.00
#> 2:                                0.94                                   0.04
#>    numerator_nowcasted_status_prob_high
#>                                   <num>
#> 1:                                    0
#> 2:                                    0
#>    numerator_nowcasted_status_prob_veryhigh
#>                                       <num>
#> 1:                                        0
#> 2:                                        0

Usage

Arguments

Value

Details

See also

Examples