diff --git a/cara/apps/templates/base/calculator.form.html.j2 b/cara/apps/templates/base/calculator.form.html.j2
index 023ecb33..3b0a8cf7 100644
--- a/cara/apps/templates/base/calculator.form.html.j2
+++ b/cara/apps/templates/base/calculator.form.html.j2
@@ -557,6 +557,8 @@
         <li>Level 3 = 95 m<sup>3</sup>/h (silent).</li>
     </ul>
   </ul>
+  <b>Specific event:</b><br>
+  If the simulation refers to a Specific event at a given time (e.g. meeting or conference) indicate the population of the given location and the 7-day average of new reported cases.</p>
   <b>Activity types:</b><br>
   The type of activity applies to both the infected and exposed persons:
   <ul>
diff --git a/cara/apps/templates/base/calculator.report.html.j2 b/cara/apps/templates/base/calculator.report.html.j2
index 3dc13c61..bc7e988a 100644
--- a/cara/apps/templates/base/calculator.report.html.j2
+++ b/cara/apps/templates/base/calculator.report.html.j2
@@ -86,11 +86,8 @@
 														<br>
 														<div class="align-self-center alert alert-dark mb-0" role="alert">
 														The above result assumes that <b>{{ form.infected_people }} 
-														{% if form.infected_people == 1 %}
-														occupant is infected
-														{% else %} 
-														occupants are infected
-														{% endif %}</b> in the room. 
+														{{ "occupant is infected" if form.infected_people == 1 else "occupants are infected" }}
+														</b> in the room. 
 														By taking into account the estimate of cases currently circulating in <b>{{ form.location_name }}</b>, 
 														the probability of on-site transmission, having at least 1 new infection in an <b>event 
 														with {{ form.total_people }} occupants, is {{ prob_specific_event | non_zero_percentage }}</b>. 
@@ -304,8 +301,8 @@
 							{{ "is" if form.infected_people == 1 else "are" }}
 							infected.</p></li>
 							{% if form.p_recurrent_option == "p_specific_event" %}
-							<li><p class="data_text">Geographic population: {{ form.geographic_population }}</p></li>
-							<li><p class="data_text">Geographic cumulative reported cases: {{ form.geographic_cases }}</p></li>
+							<li><p class="data_text">Population in {{ form.location_name }}: {{ form.geographic_population }}</p></li>
+							<li><p class="data_text">New reported cases in {{ form.location_name }} (7-day average): {{ form.geographic_cases }}</p></li>
 							{% endif %}
 							<li><p class="data_text">
 							Activity type:
diff --git a/cara/apps/templates/base/userguide.html.j2 b/cara/apps/templates/base/userguide.html.j2
index 9de64992..399ffbb4 100644
--- a/cara/apps/templates/base/userguide.html.j2
+++ b/cara/apps/templates/base/userguide.html.j2
@@ -147,7 +147,10 @@ The recommended airflow rate for the HEPA filter should correspond to a total ai
 <p>Here we capture the information about the event being simulated.
 First enter the number of occupants in the space, if you have a (small) variation in the number of people, please input the average or consider using the expert tool.
 Within the number of people occupying the space, you should specify how many are infected.</p>
-<p>As an example, for a shared office with 4 people, where one person is infected, we enter 4 occupants and 1 infected person.</p>
+<p>As an example, for a shared office with 4 people, where one person is infected, we enter 4 occupants and 1 infected person.</p><br/>
+<p>If the simulation refers to a Specific event at a given time (e.g. meeting or conference) the tool calculates the probability of on-site transmission, having at least 1 new infection.
+Indicate the population of the given location and the 7-day average of new reported cases.</p>
+<p>Otherwise select the Recurrent exposure option.</p>
 <br>
 <h4>Activity type</h4>
 <br>
diff --git a/cara/models.py b/cara/models.py
index e3d4d9b5..e995e5d8 100644
--- a/cara/models.py
+++ b/cara/models.py
@@ -1161,6 +1161,8 @@ class ExposureModel:
 
     def probability_meet_infected_person(self, population, cases, event, x) -> _VectorisedFloat:
         """Probability to meet x infected persons in an event."""
+        
+        # Ascertainment bias
         AB = 5
         return sct.binom.pmf(x, event, self.probability_random_individual(cases, population, AB))