R/worldpop.R

In rsurvstat: Download Infectious Disease Data from 'SurvStat' (Robert Koch Institute)

# https://data.worldpop.org/GIS/AgeSex_structures/Global_2015_2030/R2025A/2020/DEU/v1/1km_ua/constrained/deu_f_00_2020_CN_1km_R2025A_UA_v1.tif
# https://data.worldpop.org/GIS/AgeSex_structures/Global_2015_2030/R2025A/2020/DEU/v1/1km_ua/constrained/deu_f_05_2020_CN_1km_R2025A_UA_v1.tif

# {iso}_{gender}_{age group}_{year}_{type}_{resolution}_{release}_{version}.tif

# age_group 00,01,05,10,...,90
# gender = f,m

# year = 2015 -> 2030

# https://data.worldpop.org/GIS/Population/Global_2015_2030/R2025A/2020/DEU/v1/1km_ua/constrained/deu_pop_2020_CN_1km_R2025A_UA_v1.tif

# https://www-genesis.destatis.de/

#https://ergebnisse.zensus2022.de/datenbank/online/statistic/1000A/table/1000A-1008/table-toolbar#filter=%7B%22hideEmptyCols%22%3Afalse%2C%22hideEmptyRows%22%3Afalse%2C%22caption%22%3A%5B%7B%22variableId%22%3A%221000A%22%2C%22id%22%3A%22filter.0%22%2C%22valuesIds%22%3A%5B%221000A%22%5D%2C%22children%22%3A%5B%5D%2C%22showAsInterline%22%3Afalse%2C%22isHidden%22%3Afalse%2C%22blockCode%22%3A%22s1%22%2C%22possiblePlaces%22%3A%5B%5D%7D%5D%2C%22rowHeader%22%3A%5B%7B%22variableId%22%3A%22STAG%22%2C%22id%22%3A%22rowTitle.0%22%2C%22valuesIds%22%3A%5B%222022-05-15%22%5D%2C%22children%22%3A%5B%7B%22variableId%22%3A%22ALTER1%22%2C%22id%22%3A%22rowTitle.0.0%22%2C%22valuesIds%22%3A%5B%22ALTERU01%22%2C%22ALTER001%22%2C%22ALTER002%22%2C%22ALTER003%22%2C%22ALTER004%22%2C%22ALTER005%22%2C%22ALTER006%22%2C%22ALTER007%22%2C%22ALTER008%22%2C%22ALTER009%22%2C%22ALTER010%22%2C%22ALTER011%22%2C%22ALTER012%22%2C%22ALTER013%22%2C%22ALTER014%22%2C%22ALTER015%22%2C%22ALTER016%22%2C%22ALTER017%22%2C%22ALTER018%22%2C%22ALTER019%22%2C%22ALTER020%22%2C%22ALTER021%22%2C%22ALTER022%22%2C%22ALTER023%22%2C%22ALTER024%22%2C%22ALTER025%22%2C%22ALTER026%22%2C%22ALTER027%22%2C%22ALTER028%22%2C%22ALTER029%22%2C%22ALTER030%22%2C%22ALTER031%22%2C%22ALTER032%22%2C%22ALTER033%22%2C%22ALTER034%22%2C%22ALTER035%22%2C%22ALTER036%22%2C%22ALTER037%22%2C%22ALTER038%22%2C%22ALTER039%22%2C%22ALTER040%22%2C%22ALTER041%22%2C%22ALTER042%22%2C%22ALTER043%22%2C%22ALTER044%22%2C%22ALTER045%22%2C%22ALTER046%22%2C%22ALTER047%22%2C%22ALTER048%22%2C%22ALTER049%22%2C%22ALTER050%22%2C%22ALTER051%22%2C%22ALTER052%22%2C%22ALTER053%22%2C%22ALTER054%22%2C%22ALTER055%22%2C%22ALTER056%22%2C%22ALTER057%22%2C%22ALTER058%22%2C%22ALTER059%22%2C%22ALTER060%22%2C%22ALTER061%22%2C%22ALTER062%22%2C%22ALTER063%22%2C%22ALTER064%22%2C%22ALTER065%22%2C%22ALTER066%22%2C%22ALTER067%22%2C%22ALTER068%22%2C%22ALTER069%22%2C%22ALTER070%22%2C%22ALTER071%22%2C%22ALTER072%22%2C%22ALTER073%22%2C%22ALTER074%22%2C%22ALTER075%22%2C%22ALTER076%22%2C%22ALTER077%22%2C%22ALTER078%22%2C%22ALTER079%22%2C%22ALTER080%22%2C%22ALTER081%22%2C%22ALTER082%22%2C%22ALTER083%22%2C%22ALTER084%22%2C%22ALTER085%22%2C%22ALTER086%22%2C%22ALTER087%22%2C%22ALTER088%22%2C%22ALTER089%22%2C%22ALTER090%22%2C%22ALTER091%22%2C%22ALTER092%22%2C%22ALTER093%22%2C%22ALTER094%22%2C%22ALTER095%22%2C%22ALTER096%22%2C%22ALTER097%22%2C%22ALTER098%22%2C%22ALTER099%22%2C%22ALTER100UM%22%5D%2C%22children%22%3A%5B%5D%2C%22showAsInterline%22%3Afalse%2C%22showVariable%22%3Atrue%2C%22showVariableValue%22%3A%5B%22LABEL%22%5D%2C%22isHidden%22%3Afalse%2C%22blockCode%22%3A%22v9%22%2C%22possiblePlaces%22%3A%5B%7B%22prevParent%22%3A%7B%22blockCode%22%3A%22v1%22%2C%22id%22%3A%22rowTitle.0%22%7D%2C%22elementAbove%22%3Anull%2C%22elementBelow%22%3A%7B%22blockCode%22%3A%22v2%22%2C%22id%22%3A%22colTitle.0%22%7D%2C%22newSiblingIndex%22%3A0%2C%22hasTransposePart%22%3Atrue%7D%5D%7D%5D%2C%22showAsInterline%22%3Atrue%2C%22showVariable%22%3Afalse%2C%22showVariableValue%22%3A%5B%22LABEL%22%5D%2C%22isHidden%22%3Afalse%2C%22blockCode%22%3A%22v1%22%2C%22possiblePlaces%22%3A%5B%5D%7D%5D%2C%22columnHeader%22%3A%5B%7B%22variableId%22%3A%22GEODL1%22%2C%22id%22%3A%22colTitle.0%22%2C%22valuesIds%22%3A%5B%22DG%22%5D%2C%22children%22%3A%5B%7B%22variableId%22%3A%22PRS001%22%2C%22id%22%3A%22colTitle.0.0%22%2C%22valuesIds%22%3A%5B%22ID0004%22%5D%2C%22children%22%3A%5B%5D%2C%22showAsInterline%22%3Afalse%2C%22showVariableValue%22%3A%5B%22LABEL%22%5D%2C%22isHidden%22%3Afalse%2C%22blockCode%22%3A%22c1%22%2C%22possiblePlaces%22%3A%5B%7B%22prevParent%22%3A%7B%22blockCode%22%3A%22v2%22%2C%22id%22%3A%22colTitle.0%22%7D%2C%22elementAbove%22%3Anull%2C%22elementBelow%22%3A%7B%22blockCode%22%3A%22v2%22%2C%22id%22%3A%22colTitle.0%22%7D%2C%22newSiblingIndex%22%3A0%2C%22hasTransposePart%22%3Afalse%7D%5D%7D%5D%2C%22showAsInterline%22%3Afalse%2C%22showVariable%22%3Afalse%2C%22showVariableValue%22%3A%5B%22LABEL%22%5D%2C%22sort%22%3A%22CodeAsc%22%2C%22isHidden%22%3Atrue%2C%22blockCode%22%3A%22v2%22%2C%22possiblePlaces%22%3A%5B%7B%22newParent%22%3A%7B%22blockCode%22%3A%22c1%22%2C%22id%22%3A%22colTitle.0.0%22%7D%2C%22elementAbove%22%3A%7B%22blockCode%22%3A%22c1%22%2C%22id%22%3A%22colTitle.0.0%22%7D%2C%22elementBelow%22%3A%7B%22blockCode%22%3A%22v3%22%2C%22id%22%3A%22colTitle.1%22%7D%2C%22newSiblingIndex%22%3A0%2C%22hasTransposePart%22%3Afalse%7D%5D%7D%2C%7B%22variableId%22%3A%22GEOLK4%22%2C%22id%22%3A%22colTitle.1%22%2C%22valuesIds%22%3A%5B%2201001%22%2C%2201002%22%2C%2201003%22%2C%2201004%22%2C%2201051%22%2C%2201053%22%2C%2201054%22%2C%2201055%22%2C%2201056%22%2C%2201057%22%2C%2201058%22%2C%2201059%22%2C%2201060%22%2C%2201061%22%2C%2201062%22%2C%2202000%22%2C%2203101%22%2C%2203102%22%2C%2203103%22%2C%2203151%22%2C%2203153%22%2C%2203154%22%2C%2203155%22%2C%2203157%22%2C%2203158%22%2C%2203159%22%2C%2203241%22%2C%2203251%22%2C%2203252%22%2C%2203254%22%2C%2203255%22%2C%2203256%22%2C%2203257%22%2C%2203351%22%2C%2203352%22%2C%2203353%22%2C%2203354%22%2C%2203355%22%2C%2203356%22%2C%2203357%22%2C%2203358%2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#' Infer and fit a population model from `SurvStat` output
#'
#' `SurvStat` can be queried for count or incidence. From the combination of
#' these metrics queried across the whole range of disease notifications for any
#' given year we can infer a stratified population size, that `SurvStat` is using
#' to calculate it's incidence. This is simply modelled with a local polynomial
#' over time to allow us to fill in weekly population denominators.
#'
#' @param count_df a dataframe from the output of `get_timeseries()` or `get_snapshot()`
#' @inheritParams get_timeseries
#'
#' @returns the `count_df` dataframe with an additional `population` column
#' @export
#' @concept survstat
#'
#' @examples
#' \donttest{
#'
#' # snapshot:
#' get_snapshot(
#'   disease = diseases$`COVID-19`,
#'   geography = "state",
#'   season=2024
#' ) %>%
#' fit_population() %>%
#' dplyr::glimpse()
#'
#' # timeseries
#' # A weekly population estimate is inferred from the yearly data:
#' get_timeseries(
#'   diseases$`COVID-19`,
#'   measure = "Count",
#'   age_group = age_groups$children_coarse
#' ) %>%
#' fit_population() %>%
#' dplyr::glimpse()
#'
#' }
fit_population = function(count_df, .progress = TRUE) {
  age_group = if ("age_code" %in% colnames(count_df)) {
    unique(stats::na.omit(stringr::str_extract(
      count_df$age_code,
      "^(.*)\\.&\\[.+\\]$",
      1
    )))
  } else {
    NULL
  }

  geography = if ("geo_code" %in% colnames(count_df)) {
    unique(stats::na.omit(stringr::str_extract(
      count_df$geo_code,
      "^(.*)\\.&\\[.+\\]$",
      1
    )))
  } else {
    NULL
  }

  if ("year" %in% colnames(count_df)) {
    years = unique(count_df$year)
  } else {
    years = unique(as.numeric(format(count_df$date, "%Y")))
  }
  pop_df = infer_population(
    age_group = age_group,
    geography = geography,
    years = years,
    .progress = .progress
  )
  by = intersect(colnames(pop_df), colnames(count_df))

  if (!"date" %in% colnames(count_df)) {
    return(
      count_df %>%
        dplyr::inner_join(pop_df, by = by)
    )
  }

  if (length(years) == 1) {
    return(
      count_df %>%
        dplyr::inner_join(pop_df %>% dplyr::select(-year), by = by)
    )
  }

  model_df = pop_df %>%
    tidyr::nest(data = c(population, year)) %>%
    dplyr::mutate(
      model = purrr::map(
        data,
        ~ suppressWarnings(locfit::locfit(population ~ locfit::lp(year), .x))
      )
    )

  out = count_df %>%
    dplyr::mutate(
      year = as.numeric(date - as.Date("2020-07-01")) / 365.25 + 2020
    ) %>%
    tidyr::nest(
      new_data = dplyr::any_of(c("year", "date", "count", "incidence"))
    ) %>%
    dplyr::inner_join(model_df %>% dplyr::select(-data), by = by) %>%
    dplyr::mutate(
      data2 = purrr::map2(
        model,
        new_data,
        ~ {
          .y %>% dplyr::mutate(population = stats::predict(.x, newdata = .y))
        }
      )
    ) %>%
    dplyr::select(-model, -new_data) %>%
    tidyr::unnest(data2) %>%
    dplyr::select(-year)

  return(out)
}


#' @describeIn fit_population Query `SurvStat` for data to impute a population denominator
#'
#' @inheritParams get_timeseries
#' @param geography (optional) one of `"state"`, `"nuts"`, or `"county"` to define the
#'   resolution of the query. Does not accept a `sf` map or subset of
#'   (unlike `get_timeseries()`).
#'
#' @returns a dataframe with geography, age grouping, year and population columns
#' @export
#'
#' @examples
#' \donttest{
#' infer_population(years=2020:2025) %>% dplyr::glimpse()
#' }
infer_population = function(
  age_group = NULL,
  geography = NULL,
  years = NULL,
  .progress = TRUE
) {
  #
  if (!is.null(geography)) {
    if (geography %in% names(geography_resolution)) {
      geography = geography_resolution[[geography]]
    }
    colhier = geography
  } else {
    colhier = NULL
  }

  if (!is.null(age_group)) {
    if (age_group %in% names(age_groups)) {
      rowhier = age_groups[[age_group]]
    } else {
      rowhier = age_group
    }
  } else {
    rowhier = NULL
  }

  this_year = as.numeric(format(Sys.Date(), "%Y"))
  if (is.null(years)) {
    years = 2001:this_year
  }
  years = .year_filter(years)
  out = NULL

  if (.progress) {
    cli::cli_progress_bar(total = length(years))
  }

  # Change the cache settings:
  old_cache_settings = set_cache_settings(stale = Inf)
  on.exit(set_cache_settings(old_cache_settings), add = TRUE)

  for (year in years) {
    if (year$values$year < this_year) {
      # Never clear cache of old years
      set_cache_settings(stale = Inf)
    } else {
      set_cache_settings(stale = 14)
    }

    count_req = .get_request(
      commands$olap_data,
      cube = cubes$survstat,
      language = languages$german,
      column_hierarchy = colhier,
      measure = "Count",
      filters = year,
      row_hierarchy = rowhier
    )

    # cat(as.character(count_req))

    count_res = count_req %>%
      .do_survstat_command(quiet = TRUE)
    count_df = count_res %>%
      .process_olap_data_result() %>%
      dplyr::rename(count = value)

    incid_req = .get_request(
      commands$olap_data,
      cube = cubes$survstat,
      language = languages$german,
      column_hierarchy = colhier,
      measure = "Incidence",
      filters = year,
      row_hierarchy = rowhier
    )
    # cat(as.character(incid_req))

    incid_res = incid_req %>% .do_survstat_command(quiet = TRUE)
    incid_df = incid_res %>%
      .process_olap_data_result() %>%
      dplyr::rename(incid = value)

    if (.progress) {
      cli::cli_progress_update()
    }

    join_cols = c(
      if (!is.null(colhier)) c("col_code", "col_name") else character(),
      if (!is.null(rowhier)) c("row_code", "row_name") else character()
    )
    if (length(join_cols) > 0) {
      pop_df = count_df %>%
        dplyr::inner_join(incid_df, by = join_cols) %>%
        dplyr::select(dplyr::all_of(c(join_cols, "count", "incid")))
    } else {
      pop_df = count_df %>% dplyr::cross_join(incid_df)
    }

    pop_df = pop_df %>%
      dplyr::mutate(
        population = count / incid * 100000,
        !!!year$values
      ) %>%
      dplyr::filter(!is.na(population)) %>%
      dplyr::select(-count, -incid)

    out = dplyr::bind_rows(pop_df, out)
  }

  if ("col_name" %in% colnames(out)) {
    out = out %>% dplyr::rename(geo_name = col_name, geo_code = col_code)
  }

  if ("row_name" %in% colnames(out)) {
    out = out %>% dplyr::rename(age_name = row_name, age_code = row_code)
  }

  if ("age_code" %in% colnames(out)) {
    tmp = .fmt_range(out$age_code)
    out = out %>% dplyr::mutate(!!!tmp)
  }

  out = out %>%
    dplyr::group_by(dplyr::across(-dplyr::any_of(c("population", "year"))))

  # Interpolate to dates...?

  if (.progress) {
    cli::cli_progress_done()
  }

  return(out)
}