R/contact_matrix.r

In socialmixr: Social Mixing Matrices for Infectious Disease Modelling

#' Generate a contact matrix from diary survey data
#'
#' Samples a contact survey
#'
#' @param survey a [survey()] object
#' @param countries limit to one or more countries; if not given, will use all countries in the survey; these can be given as country names or 2-letter (ISO Alpha-2) country codes
#' @param survey.pop survey population -- either a data frame with columns 'lower.age.limit' and 'population', or a character vector giving the name(s) of a country or countries from the list that can be obtained via `wpp_countries`; if not given, will use the country populations from the chosen countries, or all countries in the survey if `countries` is not given
#' @param age.limits lower limits of the age groups over which to construct the matrix
#' @param filter any filters to apply to the data, given as list of the form (column=filter_value) - only contacts that have 'filter_value' in 'column' will be considered. If multiple filters are given, they are all applied independently and in the sequence given.
#' @param counts whether to return counts (instead of means)
#' @param symmetric whether to make matrix symmetric, such that \eqn{c_{ij}N_i = c_{ji}N_j}.
#' @param split whether to split the contact matrix into the mean number of contacts, in each age group (split further into the product of the mean number of contacts across the whole population (`mean.contacts`), a normalisation constant (`normalisation`) and age-specific variation in contacts (`contacts`)), multiplied with an assortativity matrix (`assortativity`) and a population multiplier (`demograpy`). For more detail on this, see the "Getting Started" vignette.
#' @param sample.participants whether to sample participants randomly (with replacement); done multiple times this can be used to assess uncertainty in the generated contact matrices. See the "Bootstrapping" section in the vignette for how to do this..
#' @param estimated.participant.age if set to "mean" (default), people whose ages are given as a range (in columns named "..._est_min" and "..._est_max") but not exactly (in a column named "..._exact") will have their age set to the mid-point of the range; if set to "sample", the age will be sampled from the range; if set to "missing", age ranges will be treated as missing
#' @param estimated.contact.age if set to "mean" (default), contacts whose ages are given as a range (in columns named "..._est_min" and "..._est_max") but not exactly (in a column named "..._exact") will have their age set to the mid-point of the range; if set to "sample", the age will be sampled from the range; if set to "missing", age ranges will be treated as missing
#' @param missing.participant.age if set to "remove" (default), participants without age information are removed; if set to "keep", participants with missing age are kept and treated as a separate age group
#' @param missing.contact.age if set to "remove" (default), participants that have contacts without age information are removed; if set to "sample", contacts without age information are sampled from all the contacts of participants of the same age group; if set to "keep", contacts with missing age are kept and treated as a separate age group; if set to "ignore", contact with missing age are ignored in the contact analysis
#' @param weights column names(s) of the participant data of the [survey()] object with user-specified weights (default = empty vector)
#' @param weigh.dayofweek whether to weigh social contacts data by the day of the week (weight (5/7 / N_week / N) for weekdays and (2/7 / N_weekend / N) for weekends)
#' @param weigh.age whether to weigh social contacts data by the age of the participants (vs. the populations' age distribution)
#' @param weight.threshold threshold value for the standardized weights before running an additional standardisation (default 'NA' = no cutoff)
#' @param sample.all.age.groups what to do if sampling participants (with `sample.participants = TRUE`) fails to sample participants from one or more age groups; if FALSE (default), corresponding rows will be set to NA, if TRUE the sample will be discarded and a new one taken instead
#' @param return.part.weights boolean to return the participant weights
#' @param return.demography boolean to explicitly return demography data that corresponds to the survey data (default 'NA' = if demography data is requested by other function parameters)
#' @param per.capita whether to return a matrix with contact rates per capita (default is FALSE and not possible if 'counts=TRUE' or 'split=TRUE')
#' @param ... further arguments to pass to [get_survey()], [check()] and [pop_age()] (especially column names)
#' @return a contact matrix, and the underlying demography of the surveyed population
#' @importFrom stats xtabs runif median
#' @importFrom utils data globalVariables
#' @importFrom countrycode countrycode
#' @import data.table
#' @export
#' @autoglobal
#' @examples
#' data(polymod)
#' contact_matrix(polymod, countries = "United Kingdom", age.limits = c(0, 1, 5, 15))
#' @author Sebastian Funk
contact_matrix <- function(survey, countries = NULL, survey.pop, age.limits, filter, counts = FALSE, symmetric = FALSE, split = FALSE, sample.participants = FALSE, estimated.participant.age = c("mean", "sample", "missing"), estimated.contact.age = c("mean", "sample", "missing"), missing.participant.age = c("remove", "keep"), missing.contact.age = c("remove", "sample", "keep", "ignore"), weights = NULL, weigh.dayofweek = FALSE, weigh.age = FALSE, weight.threshold = NA, sample.all.age.groups = FALSE, return.part.weights = FALSE, return.demography = NA, per.capita = FALSE, ...) {
  surveys <- c("participants", "contacts")

  dot.args <- list(...)
  unknown.args <- setdiff(names(dot.args), union(names(formals(check.survey)), names(formals(pop_age))))
  if (length(unknown.args) > 0) {
    stop("Unknown argument(s): ", paste(unknown.args, sep = ", "), ".")
  }

  ## record if 'missing.participant.age' and 'missing.contact.age' are set, for later
  missing.participant.age.set <- !missing(missing.participant.age)
  missing.contact.age.set <- !missing(missing.contact.age)

  ## read arguments
  estimated.participant.age <- match.arg(estimated.participant.age)
  estimated.contact.age <- match.arg(estimated.contact.age)
  missing.participant.age <- match.arg(missing.participant.age)
  missing.contact.age <- match.arg(missing.contact.age)

  if (!inherits(survey, "survey")) {
    stop(
      "`survey` must be a survey object (created using `survey()` ",
      "or `get_survey()`)"
    )
  }

  if (!missing(age.limits)) {
    age.limits <- as.integer(age.limits)
    if (anyNA(age.limits) || any(diff(age.limits) <= 0)) {
      stop("'age.limits' must be an increasing integer vector of lower age limits.")
    }
  }

  ## clean the survey
  survey <- clean(survey)
  ## check and get columns
  columns <- suppressMessages(check(survey, ...))

  ## check if specific countries are requested (if a survey contains data from multiple countries)
  if (length(countries) > 0 && columns[["country"]] %in% colnames(survey$participants)) {
    if (all(nchar(countries) == 2)) {
      corrected_countries <- suppressWarnings(
        countrycode(countries, "iso2c", "country.name"))
    } else {
      corrected_countries <- suppressWarnings(
        countrycode(countries, "country.name", "country.name"))
    }
    present_countries <- unique(as.character(survey$participants[[columns[["country"]]]]))
    missing_countries <- countries[which(is.na(corrected_countries))]
    if (length(missing_countries) > 0) {
      stop("Survey data not found for ", paste(missing_countries, sep = ", "), ".")
    }
    countries <- corrected_countries
    survey$participants <- survey$participants[get(columns[["country"]]) %in% countries]
    if (nrow(survey$participants) == 0) {
      stop("No participants left after selecting countries.")
    }
  }

  ## check maximum participant age in the data
  part_exact.column <- paste(columns[["participant.age"]], "exact", sep = "_")
  part_min.column <- paste(columns[["participant.age"]], "est_min", sep = "_")
  part_max.column <- paste(columns[["participant.age"]], "est_max", sep = "_")

  if (part_exact.column %in% colnames(survey$participants)) {
    survey$participants[,
      paste(columns[["participant.age"]]) := as.integer(get(part_exact.column))
    ]
  } else if (!(columns[["participant.age"]] %in% colnames(survey$participants))) {
    survey$participants[, paste(columns[["participant.age"]]) := NA_integer_]
  }

  ## sample estimated participant ages
  if (part_min.column %in% colnames(survey$participants) &&
    part_max.column %in% colnames(survey$participants)) {
    if (estimated.participant.age == "mean") {
      survey$participants[
        is.na(get(part_exact.column)) &
          !is.na(get(part_min.column)) & !is.na(get(part_max.column)),
        paste(columns[["participant.age"]]) :=
          as.integer(rowMeans(.SD)),
        .SDcols = c(part_min.column, part_max.column)
      ]
    } else if (estimated.participant.age == "sample") {
      survey$participants[
        is.na(get(columns[["participant.age"]])) &
          !is.na(get(part_min.column)) & !is.na(get(part_max.column)) &
          get(part_min.column) <= get(part_max.column),
        paste(columns[["participant.age"]]) :=
          as.integer(runif(
            .N,
            get(part_min.column),
            get(part_max.column)
          ))
      ]
    }
    # note: do nothing when "missing" is specified
  }

  if (part_max.column %in% colnames(survey$participants)) {
    max.age <- max(
      c(
        survey$participants[, get(part_exact.column)],
        survey$participants[, get(part_max.column)]
      ),
      na.rm = TRUE
    ) + 1
  } else {
    max.age <- max(
      survey$participants[, get(columns[["participant.age"]])], na.rm = TRUE
    ) + 1
  }

  if (missing(age.limits)) {
    all.ages <-
      unique(as.integer(survey$participants[, get(columns[["participant.age"]])]))
    all.ages <- all.ages[!is.na(all.ages)]
    all.ages <- sort(all.ages)
    age.limits <- union(0, all.ages)
  }

  if (missing.participant.age == "remove" &&
    nrow(survey$participants[is.na(get(columns[["participant.age"]])) |
      get(columns[["participant.age"]]) < min(age.limits)]) > 0) {
    if (!missing.participant.age.set) {
      message(
        "Removing participants without age information. ",
        "To change this behaviour, set the 'missing.participant.age' option"
      )
    }
    survey$participants <-
      survey$participants[!is.na(get(columns[["participant.age"]])) &
        get(columns[["participant.age"]]) >= min(age.limits)]
  }

  exact.column <- paste(columns[["contact.age"]], "exact", sep = "_")
  min.column <- paste(columns[["contact.age"]], "est_min", sep = "_")
  max.column <- paste(columns[["contact.age"]], "est_max", sep = "_")

  ## set contact age if it's not in the data
  if (exact.column %in% colnames(survey$contacts)) {
    survey$contacts[,
      paste(columns[["contact.age"]]) := as.integer(get(exact.column))
    ]
  } else {
    survey$contacts[, paste(columns[["contact.age"]]) := NA_integer_]
  }

  ## convert factors to integers
  for (age_column in
    c(columns[["contact.age"]], min.column, max.column, exact.column)) {
    if (age_column %in% colnames(survey$contacts) &&
      is.factor(survey$contacts[[age_column]])) {
      survey$contacts[, paste(age_column) :=
        as.integer(levels(get(age_column)))[get(age_column)]]
    }
  }

  ## sample estimated contact ages
  if (min.column %in% colnames(survey$contacts) &&
    max.column %in% colnames(survey$contacts)) {
    if (estimated.contact.age == "mean") {
      survey$contacts[
        is.na(get(columns[["contact.age"]])) &
          !is.na(get(min.column)) & !is.na(get(max.column)),
        paste(columns[["contact.age"]]) :=
          as.integer(rowMeans(.SD)),
        .SDcols = c(min.column, max.column)
      ]
    } else if (estimated.contact.age == "sample") {
      survey$contacts[
        is.na(get(columns[["contact.age"]])) &
          !is.na(get(min.column)) & !is.na(get(max.column)) &
          get(min.column) <= get(max.column),
        paste(columns[["contact.age"]]) :=
          as.integer(runif(
            .N,
            get(min.column),
            get(max.column)
          ))
      ]
    }
    # note: do nothing when "missing" is specified
  }

  if (missing.contact.age == "remove" &&
    nrow(survey$contacts[is.na(get(columns[["contact.age"]])) |
      get(columns[["contact.age"]]) < min(age.limits)]) > 0) {
    if (!missing.contact.age.set) {
      message(
        "Removing participants that have contacts without age information. ",
        "To change this behaviour, set the 'missing.contact.age' option"
      )
    }
    missing.age.id <-
      survey$contacts[
        is.na(get(columns[["contact.age"]])) |
          get(columns[["contact.age"]]) < min(age.limits),
        get(columns[["id"]])
      ]
    survey$participants <- survey$participants[!(get(columns[["id"]]) %in% missing.age.id)]
  }

  if (missing.contact.age == "ignore" &&
    nrow(survey$contacts[is.na(get(columns[["contact.age"]])) |
      get(columns[["contact.age"]]) < min(age.limits)]) > 0) {
    if (!missing.contact.age.set) {
      message(
        "Ignore contacts without age information. ",
        "To change this behaviour, set the 'missing.contact.age' option"
      )
    }
    survey$contacts <- survey$contacts[!is.na(get(columns[["contact.age"]])) &
      get(columns[["contact.age"]]) >= min(age.limits), ]
  }

  ## check if any filters have been requested
  if (!missing(filter)) {
    missing_columns <- list()
    for (table in surveys) {
      if (nrow(survey[[table]]) > 0) {
        missing_columns <-
          c(missing_columns, list(setdiff(names(filter), colnames(survey[[table]]))))
        ## filter contact data
        for (column in names(filter)) {
          if (column %in% colnames(survey[[table]])) {
            survey[[table]] <- survey[[table]][get(column) == filter[[column]]]
          }
        }
      }
    }
    missing_all <- do.call(intersect, missing_columns)
    if (length(missing_all) > 0) {
      warning("filter column(s) ", toString(missing_all), " not found")
    }
  }

  # adjust age.group.brakes to the lower and upper ages in the survey
  survey$participants[, lower.age.limit := reduce_agegroups(
    get(columns[["participant.age"]]),
    age.limits[age.limits < max.age]
  )]
  part.age.group.breaks <- c(age.limits[age.limits < max.age], max.age)
  part.age.group.present <- age.limits[age.limits < max.age]
  survey$participants[, age.group :=
    cut(survey$participants[, get(columns[["participant.age"]])],
      breaks = part.age.group.breaks,
      right = FALSE
    )]
  age.groups <- survey$participants[, levels(age.group)]
  age.groups[length(age.groups)] <-
    sub("\\[([0-9]+),.*$", "\\1+", age.groups[length(age.groups)])
  survey$participants[, age.group :=
    factor(age.group, levels = levels(age.group), labels = age.groups)]

  ## add upper age limits
  lower.upper.age.limits <- data.table(
    lower.age.limit = part.age.group.present,
    upper.age.limit = part.age.group.breaks[-1]
  )
  survey$participants <-
    merge(survey$participants, lower.upper.age.limits, by = "lower.age.limit", all.x = TRUE)

  ## if split, symmetric or age weights are requested, get demographic data (survey population)
  need.survey.pop <- split || symmetric || weigh.age ||
    (!is.na(return.demography) && return.demography) || per.capita
  if (need.survey.pop) {
    ## check if survey population is either not given or given as a vector of countries
    if (missing(survey.pop) || is.character(survey.pop)) {
      survey.representative <- FALSE
      if (!missing(survey.pop)) {
        ## survey population is given as vector of countries
        survey.countries <- survey.pop
      } else if (!missing(countries)) {
        ## survey population not given but countries requested from
        ## survey - get population data from those countries
        survey.countries <- countries
      } else {
        ## neither survey population nor country names given - try to
        ## guess country or countries surveyed from participant data
        if (columns[["country"]] %in% colnames(survey$participants)) {
          survey.countries <- unique(survey$participants[, get(columns[["country"]])])
        } else {
          warning(
            "No 'survey.pop' or 'countries' given, and no '", columns[["country"]],
            "' column found in the data. ",
            "I don't know which population this is from. ",
            "Assuming the survey is representative"
          )
          survey.representative <- TRUE
        }
      }

      if (!survey.representative) {
        ## get population data for countries from 'wpp' package
        country.pop <- data.table(wpp_age(survey.countries))

        # !! warning: spelling can differ between wpp_age and wpp_countries (e.g. Viet Nam vs Vietnam)
        # fix: rename countries using the same approach as in clean(survey,...)
        country.pop$country <- suppressWarnings(countrycode(country.pop$country, "country.name", "country.name"))

        ## check if survey data are from a specific year - in that case
        ## use demographic data from that year, otherwise latest
        if (columns[["year"]] %in% colnames(survey$participants)) {
          survey.year <-
            survey$participants[, median(get(columns[["year"]]), na.rm = TRUE)]
        } else {
          survey.year <- country.pop[, max(year, na.rm = TRUE)]
          warning(
            "No '", columns[["year"]], "' column found in the data. Will use ",
            survey.year, " population data."
          )
        }

        ## check if any survey countries are not in wpp
        missing.countries <- setdiff(survey.countries, unique(country.pop$country))
        if (length(missing.countries) > 0) {
          stop(
            "Could not find population data for ",
            toString(missing.countries), ". ",
            " Use wpp_countries() to get a list of country names."
          )
        }

        ## get demographic data closest to survey year
        country.pop.year <- unique(country.pop[, year])
        survey.year <-
          min(country.pop.year[which.min(abs(survey.year - country.pop.year))])
        survey.pop <-
          country.pop[year == survey.year][, list(population = sum(population)),
            by = "lower.age.limit"
          ]
      }
      if (survey.representative) {
        survey.pop <-
          survey$participants[, lower.age.limit :=
            reduce_agegroups(
              get(columns[["participant.age"]]),
              age.limits
            )]
        survey.pop <- survey.pop[, list(population = .N), by = lower.age.limit]
        survey.pop <- survey.pop[!is.na(lower.age.limit)]
        if (columns[["year"]] %in% colnames(survey$participants)) {
          survey.year <-
            survey$participants[, median(get(columns[["year"]]), na.rm = TRUE)]
        }
      }
    } else {
      # if survey.pop is a data frame with columns 'lower.age.limit' and 'population'
      survey.pop <- data.table(survey.pop)
      # make sure the maximum survey.pop age exceeds the participant age group breaks
      if (max(survey.pop$lower.age.limit) < max(part.age.group.present)) {
        survey.pop <- rbind(
          survey.pop,
          list(max(part.age.group.present + 1), 0)
        )
      }

      # add dummy survey.year
      survey.year <- NA_integer_
    }

    # add upper.age.limit after sorting the survey.pop ages (and add maximum age > given ages)
    survey.pop <- survey.pop[order(lower.age.limit), ]
    # if any lower age limits are missing remove them
    survey.pop <- survey.pop[!is.na(population)]
    survey.pop$upper.age.limit <- unlist(c(
      survey.pop[-1, "lower.age.limit"],
      1 + max(
        survey.pop$lower.age.limit,
        part.age.group.present
      )
    ))

    if (weigh.age) {
      ## keep reference of survey.pop
      survey.pop.full <-
        data.table(pop_age(
          survey.pop,
          seq(
            min(survey.pop$lower.age.limit),
            max(survey.pop$upper.age.limit)
          ), ...
        ))
    }

    ## adjust age groups by interpolating, in case they don't match between
    ## demographic and survey data
    survey.pop.max <- max(survey.pop$upper.age.limit)
    survey.pop <- data.table(pop_age(survey.pop, part.age.group.present, ...))

    ## set upper age limits
    survey.pop[, upper.age.limit := c(part.age.group.present[-1], survey.pop.max)]
  }

  ## weights
  survey$participants[, weight := 1]

  ## assign weights to participants to account for weekend/weekday variation
  if (weigh.dayofweek) {
    found.dayofweek <- FALSE
    if ("dayofweek" %in% colnames(survey$participants)) {
      ## Add column sum_weight: Number of entries on weekdays / weekends
      survey$participants[, sum_weight := nrow(.SD),
        by = (dayofweek %in% 1:5),
      ]

      ## The sum of the weights on weekdays is 5
      survey$participants[dayofweek %in% 1:5, weight := 5 / sum_weight]
      ## The sum of the weights on weekend is 2
      survey$participants[!(dayofweek %in% 1:5), weight := 2 / sum_weight]

      survey$participants[, sum_weight := NULL]
      found.dayofweek <- TRUE

      # add boolean for "weekday"
      survey$participants[, is.weekday := dayofweek %in% 1:5]
    }
    if (!found.dayofweek) {
      warning(
        "'weigh.dayofweek' is TRUE, but no 'dayofweek' column in the data. ",
        "Will ignore."
      )
    }
  }

  ## assign weights to participants, to account for age variation
  if (weigh.age) {
    # get number and proportion of participants by age
    survey$participants[, age.count := .N, by = eval(columns[["participant.age"]])]
    survey$participants[, age.proportion := age.count / .N]

    # get reference population by age (absolute and proportional)
    part.age.all <- range(unique(survey$participants[, get(columns[["participant.age"]])]))
    survey.pop.detail <- data.table(pop_age(survey.pop.full, seq(part.age.all[1], part.age.all[2] + 1)))
    names(survey.pop.detail) <- c(columns[["participant.age"]], "population.count")
    survey.pop.detail[, population.proportion := population.count / sum(population.count)]

    # merge reference and survey population data
    survey$participants <- merge(survey$participants, survey.pop.detail, by = eval(columns[["participant.age"]]))

    # calculate age-specific weights
    survey$participants[, weight.age := population.proportion / age.proportion]

    # merge 'weight.age' into 'weight'
    survey$participants[, weight := weight * weight.age]

    ## Remove the additional columns
    survey$participants[, age.count := NULL]
    survey$participants[, age.proportion := NULL]
    survey$participants[, population.count := NULL]
    survey$participants[, population.proportion := NULL]
    survey$participants[, weight.age := NULL]
  }

  ## option to weigh the contact data with user-defined participant weights
  if (length(weights) > 0) {
    for (i in seq_along(weights)) {
      if (weights[i] %in% colnames(survey$participants)) {
        ## Compute the overall weight
        survey$participants[, weight := weight * get(weights[i])]
      }
    }
  }

  # post-stratification weight standardisation: by age.group
  survey$participants[, weight := weight / sum(weight) * .N,
    by = age.group
  ]

  # option to truncate overall participant weights (if not NULL or NA)
  if (!is.null(weight.threshold) && !is.na(weight.threshold)) {
    survey$participants[weight > weight.threshold, weight := weight.threshold]
    # re-normalise
    survey$participants[, weight := weight / sum(weight) * .N,
      by = age.group
    ]
  }

  ## merge participants and contacts into a single data table
  setkeyv(survey$participants, columns[["id"]])
  participant_ids <- unique(survey$participants[[columns[["id"]]]])

  survey$contacts <-
    merge(survey$contacts, survey$participants,
      by = columns[["id"]], all = FALSE,
      allow.cartesian = TRUE, suffixes = c(".cont", ".part")
    )

  setkeyv(survey$contacts, columns[["id"]])

  ## sample contacts
  if (missing.contact.age == "sample" &&
    nrow(survey$contacts[is.na(get(columns[["contact.age"]]))]) > 0) {
    for (this.age.group in
      unique(survey$contacts[is.na(get(columns[["contact.age"]])), age.group])) {
      ## first, deal with missing age
      if (nrow(survey$contacts[!is.na(get(columns[["contact.age"]])) &
        age.group == this.age.group]) > 0) {
        ## some contacts in the age group have an age, sample from these
        survey$contacts[
          is.na(get(columns[["contact.age"]])) &
            age.group == this.age.group,
          paste(columns[["contact.age"]]) :=
            sample(
              survey$contacts[
                !is.na(get(columns[["contact.age"]])) &
                  age.group == this.age.group,
                get(columns[["contact.age"]])
              ],
              size = .N,
              replace = TRUE
            )
        ]
      } else {
        ## no contacts in the age group have an age, sample uniformly between limits
        min.contact.age <-
          survey$contacts[, min(get(columns[["contact.age"]]), na.rm = TRUE)]
        max.contact.age <-
          survey$contacts[, max(get(columns[["contact.age"]]), na.rm = TRUE)]
        survey$contacts[
          is.na(get(columns[["contact.age"]])) &
            age.group == this.age.group,
          paste(columns[["contact.age"]]) :=
            as.integer(floor(runif(.N,
              min = min.contact.age,
              max = max.contact.age + 1
            )))
        ]
      }
    }
  }

  ## set contact age groups
  max.contact.age <-
    survey$contacts[, max(get(columns[["contact.age"]]), na.rm = TRUE) + 1]

  contact.age.group.breaks <- part.age.group.breaks
  if (max.contact.age > max(contact.age.group.breaks)) {
    contact.age.group.breaks[length(contact.age.group.breaks)] <- max.contact.age
  }
  survey$contacts[, contact.age.group :=
    cut(get(columns[["contact.age"]]),
      breaks = contact.age.group.breaks,
      labels = age.groups,
      right = FALSE
    )]

  ret <- list()
  if (sample.participants) {
    good.sample <- FALSE
    while (!good.sample) {
      ## take a sample from the participants
      part.sample <- sample(participant_ids, replace = TRUE)
      part.age.limits <-
        unique(survey$participants[
          get(columns[["id"]]) %in% part.sample,
          lower.age.limit
        ])
      good.sample <- !sample.all.age.groups ||
        (length(setdiff(age.limits, part.age.limits)) == 0)

      sample.table <-
        data.table(id = part.sample, weight = 1)
      sample.table <-
        sample.table[, list(bootstrap.weight = sum(weight)), by = id]
      setnames(sample.table, "id", columns[["id"]])
      setkeyv(sample.table, columns[["id"]])

      sampled.contacts <- merge(survey$contacts, sample.table)
      sampled.contacts[, sampled.weight := weight * bootstrap.weight]

      sampled.participants <-
        merge(survey$participants, sample.table)
      sampled.participants[, sampled.weight := weight * bootstrap.weight]
    }
  } else {
    ## just use all participants
    sampled.contacts <- survey$contacts
    sampled.contacts[, sampled.weight := weight]
    sampled.participants <- survey$participants
    sampled.participants[, sampled.weight := weight]
  }

  ## calculate weighted contact matrix
  weighted.matrix <-
    xtabs(
      data = sampled.contacts,
      formula = sampled.weight ~ age.group + contact.age.group,
      addNA = TRUE
    )

  dims <- dim(weighted.matrix)
  dim.names <- dimnames(weighted.matrix)

  if (!counts) { ## normalise to give mean number of contacts
    ## calculate normalisation vector
    norm.vector <-
      xtabs(
        data = sampled.participants,
        formula = sampled.weight ~ age.group, addNA = TRUE
      )

    ## normalise contact matrix
    weighted.matrix <-
      array(apply(weighted.matrix, 2, function(x) x / norm.vector),
        dim = dims,
        dimnames = dim.names
      )
    ## set non-existent data to NA
    weighted.matrix[is.nan(weighted.matrix)] <- NA_real_
  }

  ## construct a warning in case there are NAs
  na.headers <- anyNA(dimnames(weighted.matrix), recursive = TRUE)
  na.content <- anyNA(weighted.matrix)
  na.present <- na.headers || na.content

  if (na.present) {
    warning.suggestion <- "  Consider "
    if (na.headers) {
      warning.suggestion <- paste0(warning.suggestion, "setting ")
      suggested.options <- NULL
      if (anyNA(rownames(weighted.matrix))) {
        suggested.options <- c(suggested.options, "'missing.participant.age'")
      }
      if (anyNA(colnames(weighted.matrix))) {
        suggested.options <- c(suggested.options, "'missing.contact.age'")
      }

      warning.suggestion <-
        paste0(warning.suggestion, paste(suggested.options, collapse = " and "))
      if (na.content) {
        warning.suggestion <- paste0(warning.suggestion, ", and ")
      } else {
        warning.suggestion <- paste0(warning.suggestion, ".")
      }
    }
    if (na.content) {
      warning.suggestion <- paste0(warning.suggestion, "adjusting the age limits.")
    }
  }

  if (symmetric && prod(dim(as.matrix(weighted.matrix))) > 1) {
    if (counts) {
      warning(
        "'symmetric=TRUE' does not make sense with 'counts=TRUE'; ",
        "will not make matrix symmetric."
      )
    } else if (na.present) {
      warning(
        "'symmetric=TRUE' does not work with missing data; ",
        "will not make matrix symmetric\n",
        warning.suggestion
      )
    } else {
      ## set c_{ij} N_i and c_{ji} N_j (which should both be equal) to
      ## 0.5 * their sum; then c_{ij} is that sum / N_i
      normalised.weighted.matrix <- diag(survey.pop$population) %*% weighted.matrix
      weighted.matrix <- 0.5 * diag(1 / survey.pop$population) %*%
        (normalised.weighted.matrix + t(normalised.weighted.matrix))
    }
  }

  if (split) {
    if (counts) {
      warning(
        "'split=TRUE' does not make sense with 'counts=TRUE'; ",
        "will not split the contact matrix."
      )
    } else if (na.present) {
      warning(
        "'split=TRUE' does not work with missing data; ",
        "will not split contact.matrix.\n",
        warning.suggestion
      )
      ret[["mean.contacts"]] <- NA
      ret[["normalisation"]] <- NA
      ret[["contacts"]] <- rep(NA, nrow(weighted.matrix))
    } else {
      ## get rid of name but preserve row and column names
      weighted.matrix <- unname(weighted.matrix)

      nb.contacts <- rowSums(weighted.matrix)
      mean.contacts <- sum(survey.pop$population * nb.contacts) /
          sum(survey.pop$population)
      spectrum.matrix <- weighted.matrix
      spectrum.matrix[is.na(spectrum.matrix)] <- 0
      spectrum <- as.numeric(eigen(spectrum.matrix, only.values = TRUE)$values[1])
      ret[["mean.contacts"]] <- mean.contacts
      ret[["normalisation"]] <- spectrum / mean.contacts

      age.proportions <- survey.pop$population / sum(survey.pop$population)
      weighted.matrix <-
        diag(1 / nb.contacts) %*% weighted.matrix %*% diag(1 / age.proportions)
      nb.contacts <- nb.contacts / spectrum
      ret[["contacts"]] <- nb.contacts
    }
  }
  # make sure the dim.names are retained after symmetric or split procedure
  dimnames(weighted.matrix) <- dim.names

  ret[["matrix"]] <- weighted.matrix

  # option to add matrix per capita, i.e. the contact rate of age i with one individual of age j in the population.
  if (per.capita) {
    if (counts) {
      warning(
        "'per.capita=TRUE' does not make sense with 'counts=TRUE'; ",
        "will not return the contact matrix per capita."
      )
    } else if (split) {
      warning(
        "'per.capita=TRUE' does not make sense with 'split=TRUE'; ",
        "will not return the contact matrix per capita."
      )
    } else {
      survey.pop$population
      weighted.matrix.per.capita <- weighted.matrix / matrix(rep(survey.pop$population, nrow(survey.pop)), ncol = nrow(survey.pop), byrow = TRUE)
      weighted.matrix.per.capita
      ret[["matrix.per.capita"]] <- weighted.matrix.per.capita
    }
  }

  if (exists("survey.year")) {
    survey.pop[, year := survey.year]
    survey.pop <-
      merge(
        survey.pop,
        unique(survey$participants[, list(lower.age.limit, age.group)])
      )
    survey.pop <- survey.pop[, list(age.group, population,
      proportion = population / sum(population), year
    )]
  }

  ## get number of participants in each age group
  if (anyNA(survey$participants$age.group)) {
    useNA <- "always"
  } else {
    useNA <- "no"
  }

  part.pop <- data.table(table(survey$participants[, age.group], useNA = useNA))
  setnames(part.pop, c("age.group", "participants"))
  part.pop[, proportion := participants / sum(participants)]

  if (!is.null(ret)) {
    if (need.survey.pop && (is.na(return.demography) || return.demography)) {
      # change survey.pop$age.group factors into characters (cfr. part.pop)
      survey.pop[, age.group := as.character(age.group)]
      ret[["demography"]] <- survey.pop[]
    }
    ret[["participants"]] <- part.pop[]
  }

  # option to return participant weights
  if (return.part.weights) {
    # default
    part.weights <- survey$participants[, .N, by = list(age.group, weight)]
    part.weights <- part.weights[order(age.group, weight), ]

    # add age and/or dayofweek info
    if (weigh.age && weigh.dayofweek) {
      part.weights <- survey$participants[, .N, by = list(age.group, participant.age = get(columns[["participant.age"]]), is.weekday, weight)]
    } else if (weigh.age) {
      part.weights <- survey$participants[, .N, by = list(age.group, participant.age = get(columns[["participant.age"]]), weight)]
    } else if (weigh.dayofweek) {
      part.weights <- survey$participants[, .N, by = list(age.group, is.weekday, weight)]
    }

    # order (from left to right)
    part.weights <- part.weights[order(part.weights), ] # nolint

    # set name of last column
    names(part.weights)[ncol(part.weights)] <- "participants"

    # add proportion and add to ret
    part.weights[, proportion := participants / sum(participants)]
    ret[["participants.weights"]] <- part.weights[]
  }

  return(ret)
}