R/get_p_neher.R

In epicentre-msf/covidestim: Estimates Of Epidemiological Parameters For COVID19

#' Estimate Covid2019 outcome probabilities for a population given its age
#' distribution, and age-severity estimates from the Neher Lab
#'
#' @description
#' Estimate Covid19 outcome probabilities including hospitalizion|infection,
#' ICU|hospitalization, death|hospitalization, and death|infection, using
#' age-severity estimates from the Neher Lab, and the population age
#' distribution for a given country, either taken from the UN World Population
#' Prospects 2019 (WPP2019) or directly supplied by the user.
#'
#' @param x Either an ISO3 country code used to extract age-specific population
#'   estimates from the UN World Population Prospects 2019 dataset, \emph{or}, a
#'   data.frame containing age categories in the first column and population
#'   counts (or proportions) in the second column
#' @param p_type Outcome to estimate (either "p_hosp_inf", "p_icu_hosp",
#'   "p_dead_hosp", or "p_dead_inf")
#'
#' @return
#' Estimated outcome probability (scalar)
#'
#' @author Flavio Finger
#' @author Patrick Barks <patrick.barks@@epicentre.msf.org>
#'
#' @source \url{https://covid19-scenarios.org/}
#'
#' @examples
#' # mean Pr(hospitalization|infection) for Canada (ISO3 code "CAN"), taking age
#' # distribution from WPP2019
#' get_p_Neher(x = "CAN", p_type = "p_hosp_inf")
#'
#' # use custom age-distribution
#' age_df <- data.frame(
#'   age = c("0-9", "10-19", "20-29", "30-39", "40-49", "50-59", "60-69", "70-79", "80+"),
#'   pop = c(1023, 1720, 2422, 3456, 3866, 4104, 4003, 3576, 1210),
#'   stringsAsFactors = FALSE
#' )
#'
#' get_p_Neher(x = age_df, p_type = "p_hosp_inf")
#'
#' @export get_p_Neher
get_p_Neher <- function(x,
                        p_type = c("p_hosp_inf", "p_icu_hosp", "p_dead_hosp", "p_dead_inf")) {

  p_type <- match.arg(p_type)

  # for testing purposes only
  if (FALSE) {
    x <- "FRA"
    p_type <- "p_hosp_inf"
  }

  # get estimates from Neher
  est_neher <- get_est_neher()

  # prepare age distribution
  age_distr <- prep_age_distib(x)

  # aggrate population age-classes to match estimate age-classes
  age_distr_agg <- aggregate_ages(age_distr, target = est_neher$age_group)

  # bind estimates to population data by age class
  est_full <- merge(est_neher, age_distr_agg, by = "age_group", all.x = TRUE)

  # return overall population probability
  return(sum(est_full[["pop"]] * est_full[[p_type]]) / sum(est_full[["pop"]]))
}