Reflect Talk Code Changes

interactives/GettingStarted.qmd

@@ -1,6 +1,5 @@
 ---
 title: Overview of `epiworldpy`
-jupyter: epiworldpy
 ---
 
 `epiworldpy` is an Python package that provides a fast (C++ backend) and highly-customizable framework for building network-based transmission/diffusion agent-based models. Some key features of `epiworldpy` are the ability to construct multi-virus models (e.g. models of competing multi-pathogens/multi-rumor), design mutating pathogens, architect population-level interventions, and build models with an arbitrary number of compartments/states (beyond SIR/SEIR).
@@ -158,14 +157,14 @@ plt.figure(figsize=(10, 6))
 
 for virus_id, virus_data in enumerate(reproductive_data):
     average_rts = list()
-    
+
     for date_data in virus_data:
         if not date_data:
             continue
 
         keys_array = np.array(list(date_data.values()), dtype=np.float64)
         average_rts.append(np.mean(keys_array))
-        
+
     plt.plot(range(0, len(average_rts)), average_rts, label=f"Virus {virus_id}")
 
 plt.xlabel('Date')
@@ -189,8 +188,16 @@ print(transition_matrix)
 By itself, this data is hard to parse (like previous results). Let's graph it:
 
 ```{python}
-grouped = transition_matrix.groupby(['dates', 'states_to'])['counts'].sum().unstack().fillna(0)
-daily_incidence = grouped.diff().fillna(0)
+# Subsetting rows where states_from != states_to.
+transition_matrix = transition_matrix[
+  transition_matrix['states_from'] != transition_matrix['states_to']
+]
+# Selecting only those where counts > 0
+transition_matrix = transition_matrix[
+  transition_matrix['counts'] > 0
+]
+
+daily_incidence = transition_matrix.groupby(['dates', 'states_to'])['counts'].sum().unstack()
 
 # Plot!
 plt.figure(figsize=(10, 6))
@@ -213,6 +220,13 @@ We can also look at generation time, here's an example on how to do that:
 from collections import defaultdict
 
 generation_time = covid19.get_db().get_generation_time()
+generation_time = pd.DataFrame(generation_time)
+
+# Filtering where gentimes is not -1
+generation_time = generation_time[
+  generation_time['gentimes'] != -1
+]
+
 agents = generation_time['agents']
 viruses = generation_time['viruses']
 times = generation_time['times']
@@ -268,10 +282,10 @@ fig, ax = plt.subplots(figsize=(6,4))
 to_track = { source[0] }
 def update(frame):
     ax.clear()
-    
+
     agents_involved_today = set()
     agents_relationships_we_care_about = []
-    
+
     # Get only the agents involved in the current frame.
     for i in range(len(start)):
         if start[i] <= frame <= end[i]:
@@ -385,7 +399,7 @@ vaccine = epiworld.Tool(
     as_proportion            = True,
     susceptibility_reduction = 0.9,
     transmission_reduction   = 0.5,
-    recovery_enhancer        = 0.5, 
+    recovery_enhancer        = 0.5,
     death_reduction          = 0.9
 )
 
@@ -408,7 +422,7 @@ Let's bring back our daily incidence graph to see how well the vaccine worked.
 transition_matrix = pd.DataFrame(covid19.get_db().get_hist_transition_matrix(False))
 
 grouped = transition_matrix.groupby(['dates', 'states_to'])['counts'].sum().unstack().fillna(0)
-daily_incidence = grouped.diff().fillna(0)
+daily_incidence = transition_matrix.groupby(['dates', 'states_to'])['counts'].sum().unstack().fillna(0)
 
 # Plot!
 plt.figure(figsize=(10, 6))
@@ -433,14 +447,14 @@ plt.figure(figsize=(10, 6))
 
 for virus_id, virus_data in enumerate(reproductive_data):
     average_rts = list()
-    
+
     for date_data in virus_data:
         if not date_data:
             continue
 
         keys_array = np.array(list(date_data.values()), dtype=np.float64)
         average_rts.append(np.mean(keys_array))
-        
+
     plt.plot(range(0, len(average_rts)), average_rts, label=f"Virus {virus_id}")
 
 plt.xlabel('Date')
@@ -452,4 +466,3 @@ plt.show()
 ```
 
 The numbers start out lower, and stay lower.
-