diff --git a/autumn/projects/sm_covid2/common_school/runner_tools.py b/autumn/projects/sm_covid2/common_school/runner_tools.py
index 01d16b746..38e17cd85 100644
--- a/autumn/projects/sm_covid2/common_school/runner_tools.py
+++ b/autumn/projects/sm_covid2/common_school/runner_tools.py
@@ -14,6 +14,8 @@
 from estival.wrappers import nevergrad as eng
 from estival.wrappers import pymc as epm
 
+import nevergrad as ng
+
 from estival.utils.parallel import map_parallel
 
 from autumn.core.runs import ManagedRun
@@ -55,10 +57,10 @@ def sample_with_lhs(n_samples, bcm):
     return sample_as_dicts
 
 
-def optimise_model_fit(bcm, num_workers: int = 8, warmup_iterations: int = 2000, search_iterations: int = 5000, suggested_start: dict = None):
+def optimise_model_fit(bcm, num_workers: int = 8, warmup_iterations: int = 2000, search_iterations: int = 5000, suggested_start: dict = None, opt_class=ng.optimizers.NGOpt):
 
     # Build optimizer
-    opt = eng.optimize_model(bcm, obj_function=bcm.loglikelihood, suggested=suggested_start, num_workers=num_workers)
+    opt = eng.optimize_model(bcm, obj_function=bcm.loglikelihood, suggested=suggested_start, num_workers=num_workers, opt_class=opt_class)
 
     # Run warm-up iterations and 
     if warmup_iterations > 0:
@@ -71,11 +73,11 @@ def optimise_model_fit(bcm, num_workers: int = 8, warmup_iterations: int = 2000,
     return best_params, opt
 
 
-def multi_country_optimise(iso3_list: list, analysis: str = "main", num_workers: int = 8, search_iterations: int = 7000, parallel_opti_jobs: int = 4, logger=None, out_path: str = None):
+def multi_country_optimise(iso3_list: list, analysis: str = "main", num_workers: int = 8, search_iterations: int = 7000, parallel_opti_jobs: int = 4, logger=None, out_path: str = None, opt_class=ng.optimizers.NGOpt):
 
     def country_opti_wrapper(iso3):
         bcm = get_bcm_object(iso3, analysis)
-        best_p, _ = optimise_model_fit(bcm, num_workers=num_workers, warmup_iterations=0, search_iterations=search_iterations)        
+        best_p, _ = optimise_model_fit(bcm, num_workers=num_workers, warmup_iterations=0, search_iterations=search_iterations, opt_class=opt_class)        
         return best_p
 
     if logger: