R/get_p_Salje.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 Salje et al. (2020)
#'
#' @description
#' Estimate Covid19 outcome probabilities including hospitalizion|infection,
#' ICU|hospitalization, death|hospitalization, and death|infection, using
#' age-severity estimates from Salje et al. (2020), 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")
#' @param p_stat Statistic of the severity estimates to use (either "mean",
#'   "low_95", or "up_95")
#' @param p_sex Use severity estimate for which sex (either "female", "male", or
#'   "total")
#'
#' @return
#' Estimated outcome probability (scalar)
#'
#' @author Anton Camacho
#' @author Patrick Barks <patrick.barks@@epicentre.msf.org>
#'
#' @source
#' Salje, H., Kiem, C.T., Lefrancq, N., Courtejoie, N., Bosetti, P., Paireau,
#' J., Andronico, A., Hoze, N., Richet, J., Dubost, C.L., and Le Strat, Y.
#' (2020) Estimating the burden of SARS-CoV-2 in France. Science.
#' \url{https://doi.org/10.1126/science.abc3517}
#'
#' @examples
#' # mean Pr(hospitalization|infection) for Canada (ISO3 code "CAN"), taking age
#' # distribution from WPP2019
#' get_p_Salje(x = "CAN", p_type = "p_hosp_inf", p_stat = "mean", p_sex = "total")
#'
#' # 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_Salje(x = age_df, p_type = "p_hosp_inf", p_stat = "mean", p_sex = "total")
#'
#' @export get_p_Salje
get_p_Salje <- function(x,
                        p_type = c("p_hosp_inf", "p_icu_hosp", "p_dead_hosp", "p_dead_inf"),
                        p_stat = c("mean", "low_95", "up_95"),
                        p_sex = c("total", "male", "female")) {

  p_type <- match.arg(p_type)
  p_stat <- match.arg(p_stat)
  p_sex <- match.arg(p_sex)

  # for testing purposes only
  if (FALSE) {
    x <- "FRA"
    p_type <- "p_dead_hosp"
    p_stat <- "mean"
    p_sex <- "total"
  }

  # get estimates from Salje for given sex and statistic
  est_salje <- get_est_salje(sex = p_sex, stat = p_stat)

  # 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_salje$age_group)

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

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