diff --git a/R/iidda/DESCRIPTION b/R/iidda/DESCRIPTION
index 29da3828..938ef3cc 100644
--- a/R/iidda/DESCRIPTION
+++ b/R/iidda/DESCRIPTION
@@ -29,4 +29,4 @@ Imports:
     readr
 Encoding: UTF-8
 LazyData: true
-RoxygenNote: 7.2.3.9000
+RoxygenNote: 7.3.2
diff --git a/R/iidda/NAMESPACE b/R/iidda/NAMESPACE
index 1add868b..d69d5d43 100644
--- a/R/iidda/NAMESPACE
+++ b/R/iidda/NAMESPACE
@@ -30,10 +30,12 @@ export(extract_all_between_paren)
 export(extract_between_paren)
 export(extract_char_or_blank)
 export(extract_or_blank)
+export(factor_time_scale)
 export(failed_prep_script_outcomes)
 export(fill_and_wrap)
 export(fill_re_template)
 export(filter_out_time_scales)
+export(find_unaccounted_cases)
 export(fix_csv)
 export(flatten_disease_hierarchy)
 export(freq_to_by)
@@ -43,6 +45,7 @@ export(get_canmod_digitization_metadata)
 export(get_dataset_path)
 export(get_elements)
 export(get_firsts)
+export(get_implied_zeros)
 export(get_items)
 export(get_lookup_table)
 export(get_main_script)
diff --git a/R/iidda/R/data_prep_tools.R b/R/iidda/R/data_prep_tools.R
index a04e8cb7..1916327e 100644
--- a/R/iidda/R/data_prep_tools.R
+++ b/R/iidda/R/data_prep_tools.R
@@ -505,7 +505,7 @@ identify_time_scales = function(data){
    %>% mutate(time_scale = ifelse(period_end_date == as.Date(period_start_date) + 6, "wk", "2wk"))
    %>% mutate(time_scale = ifelse(as.Date(period_end_date)-as.Date(period_start_date) > 14, "mo", time_scale))
    %>% mutate(time_scale = ifelse(as.Date(period_end_date)-as.Date(period_start_date) > 40, "qr", time_scale))
-   %>% mutate(time_scale = ifelse(as.Date(period_end_date)-as.Date(period_start_date) > 100, "yr", time_scale))
+   %>% mutate(time_scale = ifelse(as.Date(period_end_date)-as.Date(period_start_date) > 300, "yr", time_scale))
   )
 }
 canada_province_scale_finder = function(data) {
@@ -803,7 +803,25 @@ time_scale_chooser = function(time_scale, which_fun) {
   r
 }
 
-#' Filter out Time Scales
+#' Factor Time Scale
+#'
+#' @param data A tidy data set with a `time_scale` column.
+#'
+#' @return A data set with a factored time_scale column. 
+#'
+#' @export
+factor_time_scale = function(data){
+  if (is.factor(data$time_scale)) {
+    return(data)
+  }
+  time_scale_map = c(wk = "wk", yr = "yr", mo = "mo", `2wk` = "2wk", mt = "mo", `two-wks` = "2wk", qrtr = "qr", qr = "qr")
+  data$time_scale = time_scale_map[as.character(data$time_scale)]
+  order = c("wk", "2wk", "mo", "qr", "yr")
+  
+  return(mutate(data, time_scale = factor(data$time_scale, levels = order, ordered = TRUE)))
+}
+
+#' Filter out Time Scales OLD
 #'
 #' Choose a single best `time_scale` for each year in a dataset, grouped by
 #' nesting disease. This best `time_scale` is defined as the longest
@@ -820,8 +838,8 @@ time_scale_chooser = function(time_scale, which_fun) {
 #' @return A data set only containing records with the best time scale.
 #'
 #' @importFrom lubridate year
-#' @export
-filter_out_time_scales = function(data
+#' @noRd
+filter_out_time_scales_old = function(data
       , initial_group = c("iso_3166", "iso_3166_2", "disease", "nesting_disease")
       , final_group = c("basal_disease")
       , cleanup = TRUE
@@ -849,3 +867,249 @@ filter_out_time_scales = function(data
   }
   new_data
 }
+
+
+#' Filter out Time Scales
+#'
+#' Choose a single best `time_scale` for each year in a dataset, grouped by
+#' nesting disease. This best `time_scale` is defined as the longest
+#' of the shortest time scales in each location and sub-disease.
+#'
+#' @param data A tidy data set with a `time_scale` column.
+#' @param initial_group Character vector naming columns for defining
+#' the initial grouping used to compute the shortest time scales.
+#' @param final_group Character vector naming columns for defining the final
+#' grouping used to compute the longest of the shortest time scales.
+#' @param aggregate_if_unavailable If a location is not reporting for the determined
+#' 'best timescale', but is reporting at a finer timescale, aggregate this finer 
+#' timescale to the 'best timescale'
+#'
+#' @return A data set only containing records with the best time scale.
+#'
+#' @importFrom lubridate year
+#' @export
+filter_out_time_scales = function(data
+    , initial_group = c("iso_3166", "iso_3166_2", "disease", "nesting_disease", "basal_disease")
+    , final_group = c("basal_disease")
+    , aggregate_if_unavailable = TRUE
+) {
+  
+  if (length(unique(data$time_scale)) == 1L) return(data)
+  
+  new_data = (data
+    # remove '_unaccounted' cases when deciding best time_scale
+    |> factor_time_scale()
+    |> filter(!grepl("_unaccounted$", disease))
+    |> mutate(year = year(period_end_date))
+    |> group_by(across(all_of(c("year", initial_group))))
+    |> mutate(shortest_time_scale = time_scale_chooser(time_scale, which.min))
+    |> ungroup()
+    |> group_by(across(all_of(c("year", final_group))))
+    |> mutate(best_time_scale = time_scale_chooser(shortest_time_scale, which.max))
+    |> ungroup()
+    |> filter(as.character(time_scale) == best_time_scale)
+    |> select(-best_time_scale, -shortest_time_scale)
+  )
+  
+  # adding "unaccounted" data back, at the best_time_scale
+  all_new_data = (data
+    |> filter(grepl("_unaccounted$", disease))
+    |> mutate(year = year(period_end_date))
+    |> semi_join(select(new_data, "year", "time_scale", "disease", "nesting_disease", "basal_disease") |> unique(),
+                 by = c("year", "time_scale", final_group))
+    |> rbind(new_data)
+  )
+  
+  if (isTRUE(aggregate_if_unavailable)) {
+    # coarse scales to aggregate to
+    scales = (all_new_data
+    |> select(period_start_date, period_end_date, disease, nesting_disease, basal_disease)
+    |> unique()
+    |> rename(coarser_start_date = period_start_date,
+              coarser_end_date = period_end_date)
+    )
+    
+    # data which isn't available at 'best_time_scale' for the year, but is
+    # available at a finer timescale
+    data_to_aggregate = (data 
+     |> factor_time_scale()
+     |> mutate(year = year(period_end_date))
+     |> left_join(select(all_new_data, "year","disease", "nesting_disease", "basal_disease", "time_scale") |> unique(),
+                  by = c("year", "disease", "nesting_disease", "basal_disease"),
+                  suffix = c('_old', '_new'))
+     |> filter(time_scale_old < time_scale_new)
+     |> mutate(period_start_date = as.Date(period_start_date),
+               period_end_date = as.Date(period_end_date))
+     
+     # keep only data which isn't available at the 'best time scale' (which is now the timescale in all_new_data)
+     |> anti_join(select(all_new_data,"iso_3166_2", "year","disease", "nesting_disease", "basal_disease", "time_scale") |> unique()
+                  , by = c('time_scale_new' = 'time_scale', 'disease', 'year', 'nesting_disease','basal_disease', 'iso_3166_2'))
+    )
+    
+    aggregated_unavailable_data = (scales 
+     |> inner_join(data_to_aggregate, by = c("disease", "nesting_disease", "basal_disease"), relationship = 'many-to-many') 
+     |> filter(period_end_date > coarser_start_date & period_end_date <= coarser_end_date) 
+     |> select(names(data_to_aggregate), coarser_start_date, coarser_end_date)
+     
+     |> group_by(iso_3166, iso_3166_2, disease, nesting_disease, basal_disease, coarser_start_date, coarser_end_date)
+     |> mutate(cases_coarse_period = sum(as.numeric(cases_this_period)))
+     |> mutate(population = round(mean(as.numeric(population))),
+               population_reporting = round(mean(as.numeric(population_reporting))))
+     |> ungroup() 
+     
+     |> select(-cases_this_period, -period_start_date, -period_end_date,
+               -days_this_period, -period_mid_date)
+     |> rename(time_scale = time_scale_new,
+               cases_this_period = cases_coarse_period,
+               period_start_date = coarser_start_date,
+               period_end_date = coarser_end_date)
+     
+     |> distinct(iso_3166, iso_3166_2, disease, nesting_disease, basal_disease, 
+                 period_start_date, period_end_date, .keep_all = TRUE)
+     
+     # add back days_this_period and period_mid_date for the coarser start and end dates
+     |> mutate(days_this_period = iidda.analysis::num_days(period_start_date, period_end_date))
+     |> mutate(period_mid_date = iidda.analysis::mid_dates(period_start_date, period_end_date, days_this_period))
+     |> select(-time_scale_old)
+     
+     |> mutate(record_origin = 'derived')
+    )
+    
+    final = (all_new_data
+     |> mutate(record_origin = ifelse("record_origin" %in% names(all_new_data), record_origin, 'historical'))
+     |> rbind(aggregated_unavailable_data)
+    )
+    
+    return(final)
+  } else{
+    return(all_new_data)
+  }
+}
+
+#' Get Implied Zeros
+#'
+#' Add zeros to data set that are implied by a '0' reported at a coarser timescale.
+#' 
+#' @param data A tidy data set
+#'
+#' @return A tidy data set with inferred 0s
+#'
+#' @export
+get_implied_zeros = function(data){
+  
+  starting_data = (data
+                   |> mutate(year = year(as.Date(period_end_date)))
+                   |> factor_time_scale()
+                   
+                   |> group_by(iso_3166_2, disease, year, original_dataset_id)
+                   |> mutate(all_zero = ifelse(sum(as.numeric(cases_this_period)) == 0, TRUE, FALSE))
+                   |> ungroup()
+                   
+                   |> group_by(disease, year, original_dataset_id)
+                   |> mutate(finest_timescale = min(time_scale))
+                   |> ungroup()
+  )
+  
+  scales = (starting_data
+            |> filter(time_scale == finest_timescale)
+            |> distinct(disease, nesting_disease, basal_disease,
+                        year, time_scale, period_start_date, period_end_date, 
+                        period_mid_date, days_this_period, original_dataset_id)
+  )
+  
+  # records for which all_zero = true and finest_timescale isn't available
+  get_new_zeros = (starting_data                
+                   |> filter(time_scale > finest_timescale, all_zero)
+                   
+                   # filter for timescales that are not in the original data
+                   |> anti_join(starting_data
+                                , by = c('iso_3166_2', 'year', 'finest_timescale' = 'time_scale',
+                                         'disease', 'nesting_disease', 'basal_disease', 'dataset_id'
+                                )) # nesting/basal too? see if that changes result!
+                   
+                   |> select(-period_start_date, -period_end_date, -period_mid_date,
+                             -days_this_period)
+  ) 
+  
+  # for rows in get_new_records, find the periods (i.e. start and end dates)
+  # for the finest_timescale for a given year, disease, and original_dataset_id
+  new_zeros = 
+    (get_new_zeros
+     |> left_join(scales, by = c('disease', 'year', 'finest_timescale' = 'time_scale',
+                                 'nesting_disease', 'basal_disease', 'original_dataset_id'
+     ), relationship = "many-to-many")
+     |> select(-time_scale, -year, -all_zero)
+     |> rename(time_scale = finest_timescale)
+     
+     |> mutate(record_origin = 'derived')
+    )
+  
+  # join back to original data
+  (data
+    |> mutate(record_origin = ifelse("record_origin" %in% names(data), record_origin, 'historical'))
+    |> rbind(new_zeros)
+  )
+}
+
+
+#' Find Unaccounted Cases
+#'
+#' Make new records for instances when the sum of leaf diseases is less than
+#' the reported total for their basal disease. The difference between these 
+#' counts gets disease name 'basal_disease'_unaccounted'. 
+#' 
+#'
+#' @param data A tidy data set with a `basal_disease` column.
+#'
+#' @return A data set containing records that are the difference between a 
+#' reported total for a basal_disease and the sum of their leaf diseases 
+#'
+#' @export
+find_unaccounted_cases = function(data){
+  
+  # check if sum of leaf diseases = reported sum of basal disease
+  sum_of_leaf = (
+    data
+    %>% filter(!disease %in% unique(nesting_disease))
+    %>% filter(disease != basal_disease)
+    %>% group_by(iso_3166, iso_3166_2, period_start_date, period_end_date, nesting_disease)
+    %>% summarise(cases_this_period = sum(as.numeric(cases_this_period)))
+  )
+  
+  reported_totals = (
+    data
+    %>% filter(nesting_disease == '')
+    %>% filter(disease %in% sum_of_leaf$nesting_disease)
+    %>% select(-nesting_disease)
+    %>% rename(nesting_disease = disease)
+  )
+  
+  # if sum of leaf diseases is < reported sum of basal disease, 
+  # make new sub-disease called 'disease-name'_unaccounted, which contains
+  # the difference between sum of leaf diseases and the reported sum of the disease
+  unaccounted_data = 
+    (inner_join(sum_of_leaf, reported_totals, by = 
+                  c('iso_3166', 'iso_3166_2', 'period_start_date', 'period_end_date', 'nesting_disease'), 
+                suffix = c('_sum', '_reported')) 
+     
+     %>% mutate(cases_this_period_reported = as.numeric(cases_this_period_reported),
+                cases_this_period_sum = as.numeric(cases_this_period_sum))
+     
+     %>% filter(cases_this_period_sum < cases_this_period_reported)
+     %>% mutate(diff = cases_this_period_reported - cases_this_period_sum)
+     %>% rename(cases_this_period = diff)
+     %>% mutate(disease = paste(nesting_disease, 'unaccounted', sep = '_'))
+     
+     %>% select(-cases_this_period_reported, -cases_this_period_sum)
+     
+     %>% mutate(original_dataset_id = '')
+     %>% mutate(historical_disease = '')
+     %>% mutate(record_origin = 'derived')
+    ) 
+  
+  (data
+    |> mutate(record_origin = ifelse("record_origin" %in% names(data), record_origin, 'historical'))
+    |> rbind(unaccounted_data)
+  )
+}
+
diff --git a/R/iidda/man/factor_time_scale.Rd b/R/iidda/man/factor_time_scale.Rd
new file mode 100644
index 00000000..22e7784f
--- /dev/null
+++ b/R/iidda/man/factor_time_scale.Rd
@@ -0,0 +1,17 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data_prep_tools.R
+\name{factor_time_scale}
+\alias{factor_time_scale}
+\title{Factor Time Scale}
+\usage{
+factor_time_scale(data)
+}
+\arguments{
+\item{data}{A tidy data set with a `time_scale` column.}
+}
+\value{
+A data set with a factored time_scale column.
+}
+\description{
+Factor Time Scale
+}
diff --git a/R/iidda/man/filter_out_time_scales.Rd b/R/iidda/man/filter_out_time_scales.Rd
index 20583649..5b444de4 100644
--- a/R/iidda/man/filter_out_time_scales.Rd
+++ b/R/iidda/man/filter_out_time_scales.Rd
@@ -6,9 +6,10 @@
 \usage{
 filter_out_time_scales(
   data,
-  initial_group = c("iso_3166", "iso_3166_2", "disease", "nesting_disease"),
+  initial_group = c("iso_3166", "iso_3166_2", "disease", "nesting_disease",
+    "basal_disease"),
   final_group = c("basal_disease"),
-  cleanup = TRUE
+  aggregate_if_unavailable = TRUE
 )
 }
 \arguments{
@@ -20,8 +21,9 @@ the initial grouping used to compute the shortest time scales.}
 \item{final_group}{Character vector naming columns for defining the final
 grouping used to compute the longest of the shortest time scales.}
 
-\item{cleanup}{Should intermediate columns be removed before returning the
-output}
+\item{aggregate_if_unavailable}{If a location is not reporting for the determined
+'best timescale', but is reporting at a finer timescale, aggregate this finer 
+timescale to the 'best timescale'}
 }
 \value{
 A data set only containing records with the best time scale.
diff --git a/R/iidda/man/find_unaccounted_cases.Rd b/R/iidda/man/find_unaccounted_cases.Rd
new file mode 100644
index 00000000..d7ca81e5
--- /dev/null
+++ b/R/iidda/man/find_unaccounted_cases.Rd
@@ -0,0 +1,20 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data_prep_tools.R
+\name{find_unaccounted_cases}
+\alias{find_unaccounted_cases}
+\title{Find Unaccounted Cases}
+\usage{
+find_unaccounted_cases(data)
+}
+\arguments{
+\item{data}{A tidy data set with a `basal_disease` column.}
+}
+\value{
+A data set containing records that are the difference between a 
+reported total for a basal_disease and the sum of their leaf diseases
+}
+\description{
+Make new records for instances when the sum of leaf diseases is less than
+the reported total for their basal disease. The difference between these 
+counts gets disease name 'basal_disease'_unaccounted'.
+}
diff --git a/R/iidda/man/get_implied_zeros.Rd b/R/iidda/man/get_implied_zeros.Rd
new file mode 100644
index 00000000..d6452474
--- /dev/null
+++ b/R/iidda/man/get_implied_zeros.Rd
@@ -0,0 +1,17 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data_prep_tools.R
+\name{get_implied_zeros}
+\alias{get_implied_zeros}
+\title{Get Implied Zeros}
+\usage{
+get_implied_zeros(data)
+}
+\arguments{
+\item{data}{A tidy data set}
+}
+\value{
+A tidy data set with inferred 0s
+}
+\description{
+Add zeros to data set that are implied by a '0' reported at a coarser timescale.
+}