diff --git a/coordinator/ProcessorEpidemiology.py b/coordinator/ProcessorEpidemiology.py
index 59e7cf9bf72f3aade5fa9f57f7597fa2833f9d56..31656accdae4bccc738ed7c9174ebab011f65988 100644
--- a/coordinator/ProcessorEpidemiology.py
+++ b/coordinator/ProcessorEpidemiology.py
@@ -312,6 +312,20 @@ class ProcessorEpidemiology(Processor):
         # TODO: Some of this is modifying config before epi model is run. Determine
         # how to account for that
 
+        
+        # If this job is continuing the results of a past epi run, there are
+        # several additional requirements:
+        
+        # - The epi model will be expected to run from the reference date to the
+        #   end date, and the start date to reference date defines the range of the
+        #   job that is being continued.
+
+        # - Each model named under config['Epidemiology']['Epi'] will also need a
+        #   corresponding set of arguments for reading in as for depo and env, e.g.
+        #   config['Epidemiology']['model_name'].
+
+        is_continue = config['Epidemiology'].get('continue',False)
+
         # initialise any needed variables
 
         reference_date_str = config['StartString']
@@ -329,6 +343,7 @@ class ProcessorEpidemiology(Processor):
         config['StartTimeShort'] = start_string_short
         config['EndTime'] = end_string
 
+        # Used to get the last jobs to continue or update from
         yesterday_date = datetime.datetime.strptime(reference_date_str,'%Y%m%d') - datetime.timedelta(days=1)
         yesterday_string = yesterday_date.strftime('%Y%m%d')
 
@@ -472,10 +487,10 @@ class ProcessorEpidemiology(Processor):
             config_epi['Host']['FileNamePrepared'] = dst_host_csv
 
         # Preparing any continue-run files
-        for ci in config['Epidemiology']['Epi']:
-            
-            if ci.get('continue',False) is True:
+        if is_continue is True:
 
+            for ci in config_epi['Epi']:
+            
                 model_name = ci['model']
 
                 # Get results of last day and prepare as input
@@ -503,7 +518,12 @@ class ProcessorEpidemiology(Processor):
                 if k not in short_name.keys():
                     config_epi[k]=v
 
-            self.logger.debug('Incremental configuration looks like:')
+            if is_continue:
+                # This will be used in the epi model and not the data preparation
+                continue_start_date = reference_date+datetime.timedelta(hours=3)
+                config_epi['StartTime'] = continue_start_date.strftime('%Y-%m-%d-%H%M')
+                config_epi['StartTimeShort'] = continue_start_date.strftime('%Y%m%d%H%M')
+
             def print_item(item):
                 self.logger.debug(f"Item {item}")
                 self.logger.debug(json.dumps(item,indent=2))
@@ -514,6 +534,9 @@ class ProcessorEpidemiology(Processor):
                         iterate(item)
                     else:
                         print_item(item)
+
+            self.logger.debug('Incremental configuration looks like:')
+
             iterate(config_epi)
 
             self.logger.debug('Complete configuration looks like:')
@@ -542,7 +565,7 @@ class ProcessorEpidemiology(Processor):
             def calc_mean(arr):
                 return 'mean', arr.mean()
 
-            for epiconf in config['Epidemiology']['Epi']:
+            for epiconf in config_epi['Epi']:
 
                 outfile = epiconf["infectionRasterFileName"]
 
@@ -585,25 +608,25 @@ class ProcessorEpidemiology(Processor):
 
             # slice the epi results into before forecast and in forecast
 
-            for epiconf in config['Epidemiology']['Epi']:
+            for epiconf in config_epi['Epi']:
 
                 outfile = epiconf["infectionRasterFileName"]+'_progression.csv'
 
                 # load the full epi results
                 df_full = read_csv(outfile,header=[0],index_col=[0,1])
-                column_date_fmt = f"X{config['StartTimeShort']}_X%Y%m%d%H%M"
+                column_date_fmt = f"X{config_epi['StartTimeShort']}_X%Y%m%d%H%M"
                 df_full_dates = to_datetime(df_full.columns.astype('str'),format=column_date_fmt)
 
                 unit_description = ''
 
+                # convert units from ha_infected/ha_cell to ha_infected/ha_wheat
+
                 if epiconf['rescale_output_by_host_raster'] is True:
 
                     unit_description = '_per_ha_wheat'
 
                     model_colns = df_full.columns
 
-                    # convert units from ha_infected/ha_cell to ha_infected/ha_wheat
-
                     df_full = self.get_model_divided_by_host_fraction(
                             df_full,
                             config_epi['Host']['HostCSV'],
@@ -618,7 +641,7 @@ class ProcessorEpidemiology(Processor):
 
                 # determine date to cut with
                 # plus 1 minute so midnight is associated with preceding day
-                date_to_cut = datetime.datetime.strptime(config['StartString']+'0001','%Y%m%d%H%M')
+                date_to_cut = datetime.datetime.strptime(config_epi['StartString']+'0001','%Y%m%d%H%M')
                 dates_after_cut = df_full_dates >= date_to_cut
                 idx = argmax(dates_after_cut)-1