Changed P(I) formula and added host immunity for exposed Population

Fixed values for viable_to_RNA_ratio and transmissibility_factor of each Virus variant

Updated probability of infection formula

cara/apps/calculator/model_generator.py

@@ -449,6 +449,7 @@ class FormData:
             presence=self.exposed_present_interval(),
             activity=activity,
             mask=self.mask(),
+            host_immunity=0.,
         )
         return exposed
 

cara/apps/expert.py

@@ -507,6 +507,7 @@ baseline_model = models.ExposureModel(
         presence=models.SpecificInterval(((8., 12.), (13., 17.))),
         activity=models.Activity.types['Seated'],
         mask=models.Mask.types['No mask'],
+        host_immunity=0.,
     ),
 )
 

cara/models.py

@@ -50,6 +50,7 @@ from .utils import method_cache
 
 from .dataclass_utils import nested_replace
 
+oneoverln2 = 1 / np.log(2)
 
 # Define types for items supporting vectorisation. In the future this may be replaced
 # by ``np.ndarray[<type>]`` once/if that syntax is supported. Note that vectorization
@@ -433,7 +434,7 @@ class Virus:
     viable_to_RNA_ratio: _VectorisedFloat
 
     #: Reported increase of transmissibility of a VOC
-    transmissibility_VOC: float
+    transmissibility_factor: float
 
     #: Pre-populated examples of Viruses.
     types: typing.ClassVar[typing.Dict[str, "Virus"]]
@@ -472,26 +473,26 @@ Virus.types = {
         # 50 comes from Buonanno et al.
         infectious_dose=50.,
         viable_to_RNA_ratio = 0.5,
-        transmissibility_VOC=1.0,
+        transmissibility_factor=1.0,
     ),
     'SARS_CoV_2_B117': SARSCoV2(
         # also called VOC-202012/01
         viral_load_in_sputum=1e9,
-        infectious_dose=30.,
+        infectious_dose=50.,
         viable_to_RNA_ratio = 0.5,
-        transmissibility_VOC=0.6,
+        transmissibility_factor=0.6,
     ),
     'SARS_CoV_2_P1': SARSCoV2(
         viral_load_in_sputum=1e9,
-        infectious_dose=1/0.045,
+        infectious_dose=50.,
         viable_to_RNA_ratio = 0.5,
-        transmissibility_VOC=0.45,
+        transmissibility_factor=0.45,
     ),
     'SARS_CoV_2_B16172': SARSCoV2(
         viral_load_in_sputum=1e9,
-        infectious_dose=30/1.6,
+        infectious_dose=50.,
         viable_to_RNA_ratio = 0.5,
-        transmissibility_VOC=0.38,
+        transmissibility_factor=0.38,
     ),
 }
 
@@ -667,6 +668,11 @@ class Population:
     #: The physical activity being carried out by the people.
     activity: Activity
 
+    #: The ratio of virions that are inactivated by the person's immunity.
+    # This parameter considers the potential antibodies in the person, 
+    # which might render inactive some RNA copies (virions).
+    host_immunity: float
+
     def person_present(self, time):
         return self.presence.triggered(time)
 
@@ -728,11 +734,6 @@ class InfectedPopulation(_PopulationWithVirus):
     #: The type of expiration that is being emitted whilst doing the activity.
     expiration: _ExpirationBase 
 
-    #: The ratio of virions that are inactivated by the infected person's immunity.
-    # This parameter considers the potential antibodies in the infected person, 
-    # which might render inactive some RNA copies (virions). 
-    host_immunity: _VectorisedFloat
-
     @method_cache
     def fraction_of_infectious_virus(self) -> _VectorisedFloat:
         """
@@ -1002,10 +1003,13 @@ class ExposureModel:
             exposure * self.fraction_deposited * f_inf
         )
         
-        infectious_dose = 1.44 * self.concentration_model.virus.infectious_dose * self.concentration_model.virus.transmissibility_VOC * (1 / (1 - self.concentration_model.infected.host_immunity))
+        # oneoverln2 multiplied by ID_50 corresponds to ID_63.
+        infectious_dose = oneoverln2 * self.concentration_model.virus.infectious_dose
 
         # Probability of infection.        
-        return (1 - np.exp(-(inf_aero/infectious_dose))) * 100
+        return (1 - np.exp(-((inf_aero * (1 - self.exposed.host_immunity))/(infectious_dose * 
+                self.concentration_model.virus.transmissibility_factor)))) * 100
+
 
     def expected_new_cases(self) -> _VectorisedFloat:
         prob = self.infection_probability()

cara/monte_carlo/data.py

@@ -111,25 +111,25 @@ virus_distributions = {
                 viral_load_in_sputum=symptomatic_vl_frequencies,
                 infectious_dose=infectious_dose_distribution,
                 viable_to_RNA_ratio=viable_to_RNA_ratio_distribution,
-                transmissibility_VOC=1.,
+                transmissibility_factor=1.,
                 ),
     'SARS_CoV_2_B117': mc.SARSCoV2(
                 viral_load_in_sputum=symptomatic_vl_frequencies,
                 infectious_dose=infectious_dose_distribution,
                 viable_to_RNA_ratio=viable_to_RNA_ratio_distribution,
-                transmissibility_VOC=0.6,
+                transmissibility_factor=0.6,
                 ),
     'SARS_CoV_2_P1': mc.SARSCoV2(
                 viral_load_in_sputum=symptomatic_vl_frequencies,
                 infectious_dose=infectious_dose_distribution,
                 viable_to_RNA_ratio=viable_to_RNA_ratio_distribution,
-                transmissibility_VOC=0.45,
+                transmissibility_factor=0.45,
                 ),
     'SARS_CoV_2_B16172': mc.SARSCoV2(
                 viral_load_in_sputum=symptomatic_vl_frequencies,
                 infectious_dose=infectious_dose_distribution,
                 viable_to_RNA_ratio=viable_to_RNA_ratio_distribution,
-                transmissibility_VOC=0.38,
+                transmissibility_factor=0.38,
                 ),
 }