diff --git a/NAMESPACE b/NAMESPACE
index 3ca6f47d8..b28f13928 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -33,6 +33,7 @@ export(LogNormal)
 export(NonParametric)
 export(Normal)
 export(R_to_growth)
+export(add_breakpoints)
 export(adjust_infection_to_report)
 export(apply_tolerance)
 export(backcalc_opts)
@@ -64,6 +65,7 @@ export(extract_inits)
 export(extract_samples)
 export(extract_stan_param)
 export(fill_missing)
+export(filter_leading_zeros)
 export(fix_dist)
 export(fix_parameters)
 export(forecast_infections)
diff --git a/NEWS.md b/NEWS.md
index 28df3b2a2..29b8a2ebf 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -19,6 +19,10 @@
 - A bug was fixed where an internal function for applying a default cdf cutoff failed due to a difference a vector length issue. By @jamesmbaazam in #858 and reviewed by @sbfnk.
 - All parameters have been changed to the new parameter interface. By @sbfnk in #871 and reviewed by @seabbs.
 
+## Package changes
+
+- The internal functions `create_clean_reported_cases()` has been broken up into several functions, with relevant ones `filter_leading_zeros()`, `add_breakpoints()` and `apply_zero_threshold()` exposed to the user. By @sbfnk in #884 and reviewed by @seabbs and @jamesmbaazam.
+
 ## Documentation
 
 - Brought the docs on `alpha_sd` up to date with the code change from prior PR #853. By @zsusswein in #862 and reviewed by @jamesmbaazam.
diff --git a/R/create.R b/R/create.R
index ed4524003..ade71b2e4 100644
--- a/R/create.R
+++ b/R/create.R
@@ -1,5 +1,5 @@
 #' Create Clean Reported Cases
-#' @description `r lifecycle::badge("stable")`
+#' @description `r lifecycle::badge("deprecated")`
 #' Filters leading zeros, completes dates, and applies an optional threshold at
 #' which point 0 cases are replaced with a user supplied value (defaults to
 #' `NA`).
@@ -12,16 +12,12 @@
 #' number of cases based on the 7-day average. If the average is above this
 #' threshold then the zero is replaced using `fill`.
 #'
-#' @param fill Numeric, defaults to NA. Value to use to replace NA values or
-#' zeroes that are flagged because the 7-day average is above the
-#' `zero_threshold`. If the default NA is used then dates with NA values or with
-#' 7-day averages above the `zero_threshold` will be skipped in model fitting.
-#' If this is set to 0 then the only effect is to replace NA values with 0.
+#' @param fill Deprecated; zero dates with  7-day averages above the
+#'   `zero_threshold` will be skipped in model fitting.
 #' @param add_breakpoints Logical, defaults to TRUE. Should a breakpoint column
 #' be added to the data frame if it does not exist.
 #'
 #' @inheritParams estimate_infections
-#' @importFrom data.table copy merge.data.table setorder setDT frollsum
 #' @return A cleaned data frame of reported cases
 #' @keywords internal
 #' @examples
@@ -33,55 +29,15 @@ create_clean_reported_cases <- function(data, horizon = 0,
                                         zero_threshold = Inf,
                                         fill = NA_integer_,
                                         add_breakpoints = TRUE) {
-  reported_cases <- data.table::setDT(data)
-  reported_cases_grid <- data.table::copy(reported_cases)[,
-   .(date = seq(min(date), max(date) + horizon, by = "days"))
-  ]
-
-  reported_cases <- data.table::merge.data.table(
-    reported_cases, reported_cases_grid,
-    by = "date", all.y = TRUE
-  )
-
-  if (is.null(reported_cases$breakpoint) && add_breakpoints) {
-    reported_cases$breakpoint <- 0
+  reported_cases <- add_horizon(data, horizon = horizon)
+  if (add_breakpoints) {
+    reported_cases <- add_breakpoints(reported_cases)
   }
-  if (!is.null(reported_cases$breakpoint)) {
-    reported_cases[is.na(breakpoint), breakpoint := 0]
-  }
-  reported_cases <- data.table::setorder(reported_cases, date)
-  ## Filter out 0 reported cases from the beginning of the data
   if (filter_leading_zeros) {
-    reported_cases <- reported_cases[order(date)][
-      date >= min(date[confirm[!is.na(confirm)] > 0])
-    ]
-  }
-  # Calculate `average_7_day` which for rows with `confirm == 0`
-  # (the only instance where this is being used) equates to the 7-day
-  # right-aligned moving average at the previous data point.
-  reported_cases <-
-    reported_cases[
-      ,
-      `:=`(average_7_day = (
-          data.table::frollsum(confirm, n = 8, na.rm = TRUE)
-        ) / 7
-      )
-    ]
-  # Check case counts preceding zero case counts and set to 7 day average if
-  # average over last 7 days is greater than a threshold
-  if (!is.infinite(zero_threshold)) {
-    reported_cases <- reported_cases[
-      confirm == 0 & average_7_day > zero_threshold,
-      confirm := NA_integer_
-    ]
-  }
-  reported_cases[is.na(confirm), confirm := fill]
-  reported_cases[, "average_7_day" := NULL]
-  ## set accumulate to FALSE in added rows
-  if ("accumulate" %in% colnames(reported_cases)) {
-    reported_cases[is.na(accumulate), accumulate := FALSE]
+    reported_cases <- filter_leading_zeros(reported_cases)
   }
-  return(reported_cases)
+  reported_cases <- apply_zero_threshold(reported_cases, zero_threshold)
+  return(reported_cases[])
 }
 
 #' Create complete cases
diff --git a/R/estimate_infections.R b/R/estimate_infections.R
index 1b4fff209..1d5b5f004 100644
--- a/R/estimate_infections.R
+++ b/R/estimate_infections.R
@@ -140,6 +140,20 @@ estimate_infections <- function(data,
       "estimate_infections(data)"
     )
   }
+  if (!missing(filter_leading_zeros)) {
+    lifecycle::deprecate_warn(
+      "1.7.0",
+      "estimate_infections(filter_leading_zeros)",
+      "filter_leading_zeros()"
+    )
+  }
+  if (!missing(zero_threshold)) {
+    lifecycle::deprecate_warn(
+      "1.7.0",
+      "estimate_infections(zero_threshold)",
+      "apply_zero_threshold()"
+    )
+  }
   # Validate inputs
   check_reports_valid(data, model = "estimate_infections")
   assert_class(generation_time, "generation_time_opts")
@@ -184,6 +198,19 @@ estimate_infections <- function(data,
   )
   # Fill missing dates
   reported_cases <- default_fill_missing_obs(data, obs, "confirm")
+  # Check initial zeros to check for deprecated filter zero functionality
+  if (filter_leading_zeros &&
+      !is.na(reported_cases[date == min(date), "confirm"]) &&
+      reported_cases[date == min(date), "confirm"] == 0) {
+    cli_warn(c(
+      "!" = "Filtering initial zero observations in the data. This
+      functionality will be removed in future versions of EpiNow2. In order
+      to retain the default behaviour and filter initial zero observations
+      use the {.fn filter_leading_zeros()} function on the data before
+      calling {.fn estimate_infections()}."
+    ))
+  }
+
   # Create clean and complete cases
   reported_cases <- create_clean_reported_cases(
     reported_cases, horizon,
diff --git a/R/estimate_secondary.R b/R/estimate_secondary.R
index 13054e956..09b635d7d 100644
--- a/R/estimate_secondary.R
+++ b/R/estimate_secondary.R
@@ -172,6 +172,20 @@ estimate_secondary <- function(data,
       "estimate_secondary(data)"
     )
   }
+  if (!missing(filter_leading_zeros)) {
+    lifecycle::deprecate_warn(
+      "1.7.0",
+      "estimate_secondary(filter_leading_zeros)",
+      "filter_leading_zeros()"
+    )
+  }
+  if (!missing(zero_threshold)) {
+    lifecycle::deprecate_warn(
+      "1.7.0",
+      "estimate_secondary(zero_threshold)",
+      "apply_zero_threshold()"
+    )
+  }
   # Validate the inputs
   check_reports_valid(data, model = "estimate_secondary")
   assert_class(secondary, "secondary_opts")
@@ -200,6 +214,16 @@ estimate_secondary <- function(data,
 
   secondary_reports_dirty <-
     reports[, list(date, confirm = secondary, accumulate)]
+  if (filter_leading_zeros &&
+      !is.na(secondary_reports_dirty[date == min(date), "confirm"]) &&
+      secondary_reports_dirty[date == min(date), "confirm"] == 0) {
+    cli_warn(c(
+      "!" = "Filtering initial zero observations in the data. This
+      functionality will be removed in future versions of EpiNow2. In order
+      to filter initial zero observations use the {.fn filter_leading_zeros}
+      function on the data before calling {.fn estimate_secondary}."
+    ))
+  }
   secondary_reports <- create_clean_reported_cases(
     secondary_reports_dirty,
     filter_leading_zeros = filter_leading_zeros,
diff --git a/R/preprocessing.R b/R/preprocessing.R
index a43171bad..913300d7b 100644
--- a/R/preprocessing.R
+++ b/R/preprocessing.R
@@ -42,7 +42,7 @@
 ##'   using a data set that has multiple columns of hwich one of them
 ##'   corresponds to observations that are to be processed here.
 ##' @param by Character vector. Name(s) of any additional column(s) where
-##'   missing data should be processed separately for each value in the column.
+##'   data processing should be done separately for each value in the column.
 ##'   This is useful when using data representing e.g. multiple geographies. If
 ##'   NULL (default) no such grouping is done.
 ##' @return a data.table with an `accumulate` column that indicates whether
@@ -177,3 +177,158 @@ default_fill_missing_obs <- function(data, obs, obs_column) {
   }
   return(data)
 }
+
+##' Add missing values for future dates
+##'
+##' @param data Data frame with a `date` column. The other columns depend on the
+##'   model that the data are to be used, e.g. [estimate_infections()] or
+##'   [estimate_secondary()]. See the documentation there for the expected
+##'   format.
+##' @param accumulate The number of days to accumulate when generating posterior
+##'   prediction, e.g. 7 for weekly accumulated forecasts.
+##' @inheritParams fill_missing
+##' @inheritParams estimate_infections
+##' @importFrom data.table copy merge.data.table setDT
+##' @return A data.table with missing values for future dates
+##' @keywords internal
+add_horizon <- function(data, horizon, accumulate = 1L,
+                        obs_column = "confirm", by = NULL) {
+  assert_data_frame(data)
+  assert_character(obs_column)
+  assert_character(by, null.ok = TRUE)
+  assert_names(
+    colnames(data),
+    must.include = c("date", by, obs_column)
+  )
+  assert_integerish(horizon, lower = 0)
+  assert_integerish(accumulate, lower = 1)
+  assert_date(data$date, any.missing = FALSE)
+
+  reported_cases <- data.table::setDT(data)
+  if (horizon > 0) {
+    reported_cases_future <- data.table::copy(reported_cases)[,
+      .(date = seq(max(date) + 1, max(date) + horizon, by = "days")),
+      by = by
+    ]
+    ## if we accumulate add the column
+    if (accumulate > 1 || "accumulate" %in% colnames(data)) {
+      reported_cases_future[, accumulate := TRUE]
+      ## set accumulation to FALSE where appropriate
+      if (horizon >= accumulate) {
+        reported_cases_future[
+          as.integer(date - min(date) - 1) %% accumulate == 0,
+          accumulate := FALSE
+        ]
+      }
+    }
+    ## fill any missing columns
+    reported_cases <- rbind(
+      reported_cases, reported_cases_future,
+      fill = TRUE
+    )
+  }
+  return(reported_cases[])
+}
+
+##' Add breakpoints to certain dates in a data set.
+##'
+##' @param dates A vector of dates to use as breakpoints.
+##' @inheritParams estimate_infections
+##' @return A data.table with `breakpoint` set to 1 on each of the specified
+##'   dates.
+##' @export
+##' @importFrom data.table setDT
+##' @examples
+##' reported_cases <- add_breakpoints(example_confirmed, as.Date("2020-03-26"))
+add_breakpoints <- function(data, dates = as.Date(character(0))) {
+  assert_data_frame(data)
+  assert_names(colnames(data), must.include = "date")
+  assert_date(dates)
+  assert_date(data$date, any.missing = FALSE)
+  reported_cases <- data.table::setDT(data)
+  if (is.null(reported_cases$breakpoint)) {
+    reported_cases$breakpoint <- 0
+  }
+  missing_dates <- setdiff(dates, data$date)
+  if (length(missing_dates) > 0) {
+    cli_abort("Breakpoint date{?s} not found in data: {.var {missing_dates}}")
+  }
+  reported_cases[date %in% dates, breakpoint := 1]
+  reported_cases[is.na(breakpoint), breakpoint := 0]
+  return(reported_cases)
+}
+
+##' Filter leading zeros from a data set.
+##'
+##' @inheritParams estimate_infections
+##' @inheritParams fill_missing
+##' @return A data.table with leading zeros removed.
+##' @export
+##' @importFrom data.table setDT
+##' @examples
+##' cases <- data.frame(
+##'   date = as.Date("2020-01-01") + 0:10,
+##'   confirm = c(0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
+##' )
+##' filter_leading_zeros(cases)
+filter_leading_zeros <- function(data, obs_column = "confirm", by = NULL) {
+  assert_data_frame(data)
+  assert_character(obs_column)
+  assert_character(by, null.ok = TRUE)
+  assert_names(
+    colnames(data),
+    must.include = c("date", by, obs_column)
+  )
+  reported_cases <- data.table::setDT(data)
+  reported_cases <- reported_cases[order(date)][
+    date >= min(date[get(obs_column)[!is.na(get(obs_column))] > 0])
+  ]
+  return(reported_cases[])
+}
+
+##' Convert zero case counts to `NA` (missing) if the 7-day average is above a
+##' threshold.
+##'
+##' This function aims to detect spurious zeroes by comparing the 7-day average
+##' of the case counts to a threshold. If the 7-day average is above the
+##' threshold, the zero case count is replaced with `NA`.
+##'
+##' @param threshold Numeric, defaults to `Inf`. Indicates if detected zero
+##'   cases are meaningful by using a threshold number of cases based on the
+##'   7-day average. If the average is above this threshold at the time of a
+##'   zero observation count then the zero is replaced with a missing (`NA`)
+##'   count and thus ignored in the likelihood.
+##'
+##' @inheritParams estimate_infections
+##' @inheritParams fill_missing
+##' @importFrom data.table setDT frollsum
+##' @return A data.table with the zero threshold applied.
+apply_zero_threshold <- function(data, threshold = Inf,
+                                 obs_column = "confirm") {
+  assert_data_frame(data)
+  assert_numeric(threshold)
+  reported_cases <- data.table::setDT(data)
+
+  # Calculate `average_7_day` which for rows with `confirm == 0`
+  # (the only instance where this is being used) equates to the 7-day
+  # right-aligned moving average at the previous data point.
+  reported_cases <-
+    reported_cases[
+      ,
+      `:=`(average_7_day = (
+          data.table::frollsum(get(obs_column), n = 8, na.rm = TRUE)
+        ) / 7
+      )
+    ]
+  # Check case counts preceding zero case counts and set to 7 day average if
+  # average over last 7 days is greater than a threshold
+  if (!is.infinite(threshold)) {
+    reported_cases <- reported_cases[
+      get(obs_column) == 0 & average_7_day > threshold,
+      paste(obs_column) := NA_integer_
+    ]
+  }
+  reported_cases[is.na(get(obs_column)), paste(obs_column) := NA_integer_]
+  reported_cases[, "average_7_day" := NULL]
+  return(reported_cases[])
+}
diff --git a/R/utilities.R b/R/utilities.R
index 5fb413fc5..3a73bacec 100644
--- a/R/utilities.R
+++ b/R/utilities.R
@@ -442,6 +442,6 @@ globalVariables(
     "..lowers", "..upper_CrI", "..uppers", "timing", "dataset", "last_confirm",
     "report_date", "secondary", "id", "conv", "meanlog", "primary", "scaled",
     "scaling", "sdlog", "lookup", "new_draw", ".draw", "p", "distribution",
-    "accumulate", "..present"
+    "accumulate", "..present", "reported_cases"
   )
 )
diff --git a/_pkgdown.yml b/_pkgdown.yml
index ae09e56fe..370830c69 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -78,6 +78,13 @@ reference:
     contents:
       - contains("_opts")
       - opts_list
+  - title: Preprocess data
+    desc: Functions used for prepropcessing data
+    contents:
+      - fill_missing
+      - add_breakpoints
+      - filter_leading_zeros
+      - apply_zero_threshold
   - title: Summarise Across Regions
     desc: Functions used for summarising across regions (designed for use with regional_epinow)
     contents:
diff --git a/man/add_breakpoints.Rd b/man/add_breakpoints.Rd
new file mode 100644
index 000000000..761d310d9
--- /dev/null
+++ b/man/add_breakpoints.Rd
@@ -0,0 +1,30 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/preprocessing.R
+\name{add_breakpoints}
+\alias{add_breakpoints}
+\title{Add breakpoints to certain dates in a data set.}
+\usage{
+add_breakpoints(data, dates = as.Date(character(0)))
+}
+\arguments{
+\item{data}{A \verb{<data.frame>} of confirmed cases (confirm) by date (date).
+\code{confirm} must be numeric and \code{date} must be in date format. Optionally
+this can also have a logical \code{accumulate} column which indicates whether
+data should be added to the next data point. This is useful when modelling
+e.g. weekly incidence data. See also the \code{\link[=fill_missing]{fill_missing()}} function which
+helps add the \code{accumulate} column with the desired properties when dealing
+with non-daily data. If any accumulation is done this happens after
+truncation as specified by the \code{truncation} argument.}
+
+\item{dates}{A vector of dates to use as breakpoints.}
+}
+\value{
+A data.table with \code{breakpoint} set to 1 on each of the specified
+dates.
+}
+\description{
+Add breakpoints to certain dates in a data set.
+}
+\examples{
+reported_cases <- add_breakpoints(example_confirmed, as.Date("2020-03-26"))
+}
diff --git a/man/add_horizon.Rd b/man/add_horizon.Rd
new file mode 100644
index 000000000..ca1ce27cf
--- /dev/null
+++ b/man/add_horizon.Rd
@@ -0,0 +1,37 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/preprocessing.R
+\name{add_horizon}
+\alias{add_horizon}
+\title{Add missing values for future dates}
+\usage{
+add_horizon(data, horizon, accumulate = 1L, obs_column = "confirm", by = NULL)
+}
+\arguments{
+\item{data}{Data frame with a \code{date} column. The other columns depend on the
+model that the data are to be used, e.g. \code{\link[=estimate_infections]{estimate_infections()}} or
+\code{\link[=estimate_secondary]{estimate_secondary()}}. See the documentation there for the expected
+format.}
+
+\item{horizon}{Numeric, defaults to 7. Number of days into the future to
+forecast.}
+
+\item{accumulate}{The number of days to accumulate when generating posterior
+prediction, e.g. 7 for weekly accumulated forecasts.}
+
+\item{obs_column}{Character (default: "confirm"). If given, only the column
+specified here will be used for checking missingness. This is useful if
+using a data set that has multiple columns of hwich one of them
+corresponds to observations that are to be processed here.}
+
+\item{by}{Character vector. Name(s) of any additional column(s) where
+data processing should be done separately for each value in the column.
+This is useful when using data representing e.g. multiple geographies. If
+NULL (default) no such grouping is done.}
+}
+\value{
+A data.table with missing values for future dates
+}
+\description{
+Add missing values for future dates
+}
+\keyword{internal}
diff --git a/man/apply_zero_threshold.Rd b/man/apply_zero_threshold.Rd
new file mode 100644
index 000000000..4d123c9ce
--- /dev/null
+++ b/man/apply_zero_threshold.Rd
@@ -0,0 +1,38 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/preprocessing.R
+\name{apply_zero_threshold}
+\alias{apply_zero_threshold}
+\title{Convert zero case counts to \code{NA} (missing) if the 7-day average is above a
+threshold.}
+\usage{
+apply_zero_threshold(data, threshold = Inf, obs_column = "confirm")
+}
+\arguments{
+\item{data}{A \verb{<data.frame>} of confirmed cases (confirm) by date (date).
+\code{confirm} must be numeric and \code{date} must be in date format. Optionally
+this can also have a logical \code{accumulate} column which indicates whether
+data should be added to the next data point. This is useful when modelling
+e.g. weekly incidence data. See also the \code{\link[=fill_missing]{fill_missing()}} function which
+helps add the \code{accumulate} column with the desired properties when dealing
+with non-daily data. If any accumulation is done this happens after
+truncation as specified by the \code{truncation} argument.}
+
+\item{threshold}{Numeric, defaults to \code{Inf}. Indicates if detected zero
+cases are meaningful by using a threshold number of cases based on the
+7-day average. If the average is above this threshold at the time of a
+zero observation count then the zero is replaced with a missing (\code{NA})
+count and thus ignored in the likelihood.}
+
+\item{obs_column}{Character (default: "confirm"). If given, only the column
+specified here will be used for checking missingness. This is useful if
+using a data set that has multiple columns of hwich one of them
+corresponds to observations that are to be processed here.}
+}
+\value{
+A data.table with the zero threshold applied.
+}
+\description{
+This function aims to detect spurious zeroes by comparing the 7-day average
+of the case counts to a threshold. If the 7-day average is above the
+threshold, the zero case count is replaced with \code{NA}.
+}
diff --git a/man/create_clean_reported_cases.Rd b/man/create_clean_reported_cases.Rd
index 949880c81..d973aef40 100644
--- a/man/create_clean_reported_cases.Rd
+++ b/man/create_clean_reported_cases.Rd
@@ -34,11 +34,8 @@ to Inf. Indicates if detected zero cases are meaningful by using a threshold
 number of cases based on the 7-day average. If the average is above this
 threshold then the zero is replaced using \code{fill}.}
 
-\item{fill}{Numeric, defaults to NA. Value to use to replace NA values or
-zeroes that are flagged because the 7-day average is above the
-\code{zero_threshold}. If the default NA is used then dates with NA values or with
-7-day averages above the \code{zero_threshold} will be skipped in model fitting.
-If this is set to 0 then the only effect is to replace NA values with 0.}
+\item{fill}{Deprecated; zero dates with  7-day averages above the
+\code{zero_threshold} will be skipped in model fitting.}
 
 \item{add_breakpoints}{Logical, defaults to TRUE. Should a breakpoint column
 be added to the data frame if it does not exist.}
@@ -47,7 +44,7 @@ be added to the data frame if it does not exist.}
 A cleaned data frame of reported cases
 }
 \description{
-\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#stable}{\figure{lifecycle-stable.svg}{options: alt='[Stable]'}}}{\strong{[Stable]}}
+\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#deprecated}{\figure{lifecycle-deprecated.svg}{options: alt='[Deprecated]'}}}{\strong{[Deprecated]}}
 Filters leading zeros, completes dates, and applies an optional threshold at
 which point 0 cases are replaced with a user supplied value (defaults to
 \code{NA}).
diff --git a/man/fill_missing.Rd b/man/fill_missing.Rd
index 2120c6ddf..9e37e0b02 100644
--- a/man/fill_missing.Rd
+++ b/man/fill_missing.Rd
@@ -52,7 +52,7 @@ using a data set that has multiple columns of hwich one of them
 corresponds to observations that are to be processed here.}
 
 \item{by}{Character vector. Name(s) of any additional column(s) where
-missing data should be processed separately for each value in the column.
+data processing should be done separately for each value in the column.
 This is useful when using data representing e.g. multiple geographies. If
 NULL (default) no such grouping is done.}
 }
diff --git a/man/filter_leading_zeros.Rd b/man/filter_leading_zeros.Rd
new file mode 100644
index 000000000..527f21e6c
--- /dev/null
+++ b/man/filter_leading_zeros.Rd
@@ -0,0 +1,41 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/preprocessing.R
+\name{filter_leading_zeros}
+\alias{filter_leading_zeros}
+\title{Filter leading zeros from a data set.}
+\usage{
+filter_leading_zeros(data, obs_column = "confirm", by = NULL)
+}
+\arguments{
+\item{data}{A \verb{<data.frame>} of confirmed cases (confirm) by date (date).
+\code{confirm} must be numeric and \code{date} must be in date format. Optionally
+this can also have a logical \code{accumulate} column which indicates whether
+data should be added to the next data point. This is useful when modelling
+e.g. weekly incidence data. See also the \code{\link[=fill_missing]{fill_missing()}} function which
+helps add the \code{accumulate} column with the desired properties when dealing
+with non-daily data. If any accumulation is done this happens after
+truncation as specified by the \code{truncation} argument.}
+
+\item{obs_column}{Character (default: "confirm"). If given, only the column
+specified here will be used for checking missingness. This is useful if
+using a data set that has multiple columns of hwich one of them
+corresponds to observations that are to be processed here.}
+
+\item{by}{Character vector. Name(s) of any additional column(s) where
+data processing should be done separately for each value in the column.
+This is useful when using data representing e.g. multiple geographies. If
+NULL (default) no such grouping is done.}
+}
+\value{
+A data.table with leading zeros removed.
+}
+\description{
+Filter leading zeros from a data set.
+}
+\examples{
+cases <- data.frame(
+  date = as.Date("2020-01-01") + 0:10,
+  confirm = c(0, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
+)
+filter_leading_zeros(cases)
+}
diff --git a/tests/testthat/test-create_clean_reported_cases.R b/tests/testthat/test-create_clean_reported_cases.R
index 44bb0ef78..2314a2adc 100644
--- a/tests/testthat/test-create_clean_reported_cases.R
+++ b/tests/testthat/test-create_clean_reported_cases.R
@@ -1,8 +1,3 @@
-
-test_that("create_clean_reported_cases runs without errors", {
-  expect_no_error(create_clean_reported_cases(example_confirmed, 7))
-})
-
 test_that("create_clean_reported_cases returns a data table", {
   result <- create_clean_reported_cases(example_confirmed, 7)
   expect_s3_class(result, "data.table")
diff --git a/tests/testthat/test-estimate_infections.R b/tests/testthat/test-estimate_infections.R
index 549cfeef0..598e5fca0 100644
--- a/tests/testthat/test-estimate_infections.R
+++ b/tests/testthat/test-estimate_infections.R
@@ -164,3 +164,12 @@ test_that("estimate_infections works as expected with failing chains", {
     )
   ))
 })
+
+test_that("a warning is thrown when using deprecated functionality", {
+  suppressWarnings(expect_deprecated(estimate_infections(
+    reported_cases, filter_leading_zeros = TRUE, verbose = FALSE
+  ), "filter_leading_zeros"))
+  suppressWarnings(expect_deprecated(estimate_infections(
+    reported_cases, zero_threshold = 50, verbose = FALSE
+  ), "zero_threshold"))
+})
diff --git a/tests/testthat/test-estimate_secondary.R b/tests/testthat/test-estimate_secondary.R
index 669679507..3d70b714c 100644
--- a/tests/testthat/test-estimate_secondary.R
+++ b/tests/testthat/test-estimate_secondary.R
@@ -224,6 +224,8 @@ test_that("estimate_secondary works with weigh_delay_priors = TRUE", {
 })
 
 test_that("estimate_secondary works with filter_leading_zeros set", {
+  ## testing deprecated functionality
+  withr::local_options(lifecycle_verbosity = "quiet")
   modified_data <- inc_cases[1:10, secondary := 0]
   out <- estimate_secondary(
     modified_data,
@@ -238,6 +240,8 @@ test_that("estimate_secondary works with filter_leading_zeros set", {
 })
 
 test_that("estimate_secondary works with zero_threshold set", {
+  ## testing deprecated functionality
+  withr::local_options(lifecycle_verbosity = "quiet")
   modified_data <- inc_cases[sample(1:30, 10), primary := 0]
   out <- estimate_secondary(
     modified_data,
@@ -249,3 +253,12 @@ test_that("estimate_secondary works with zero_threshold set", {
   expect_s3_class(out, "estimate_secondary")
   expect_named(out, c("predictions", "posterior", "data", "fit"))
 })
+
+test_that("a warning is thrown when using deprecated functionality", {
+  suppressWarnings(expect_deprecated(estimate_secondary(
+    inc_cases, filter_leading_zeros = TRUE, verbose = FALSE
+  ), "filter_leading_zeros"))
+  suppressWarnings(expect_deprecated(estimate_secondary(
+    inc_cases, zero_threshold = 50, verbose = FALSE
+  ), "zero_threshold"))
+})