demographics

Author: bmtgoncalves

Epidemiology101.ipynb

@@ -40,4 +40,4 @@ figure.subplot.left: 0.08
 figure.subplot.right: 0.95
 figure.subplot.bottom: 0.07
 figure.figsize: 12.8, 8.8
-figure.dpi: 300
+figure.dpi: 70

Epidemiology102.ipynb

@@ -76,6 +76,14 @@ def add_spontaneous(self, source, target, rate):
         """
         self.transitions.add_edge(source, target, rate=rate)
 
+    def add_birth_rate(self, rate):
+        for comp in self.transitions.nodes():
+            self.transitions.nodes[comp]['birth']=rate
+
+    def add_death_rate(self, rate):
+        for comp in self.transitions.nodes():
+            self.transitions.nodes[comp]['death']=rate
+
     def add_vaccination(self, source, target, rate, start):
         """
         Add a vaccination transition between two compartments
@@ -187,7 +195,19 @@ def _new_cases(self, population, time, pos):
 
             diff[pos[source]] -= rate
             diff[pos[target]] += rate
-            
+        
+        # Population dynamics
+        for comp, data in self.transitions.nodes(data=True):
+            comp_id = pos[comp]
+
+            if "birth" in data:
+                births = population[comp_id]*data["birth"]
+                diff[comp_id] += births
+
+            if "death" in data:
+                deaths = population[comp_id]*data["death"]
+                diff[comp_id] -= deaths
+
         return diff
 
     def plot(self, title=None, normed=True, **kwargs):
@@ -277,6 +297,7 @@ def simulate(self, timesteps, t_min=1, seasonality=None, **kwargs):
             N = np.sum(pop)
 
 
+            # Disease dynamics
             for comp in comps:
                 trans = list(self.transitions.edges(comp, data=True))             
 
@@ -286,6 +307,10 @@ def simulate(self, timesteps, t_min=1, seasonality=None, **kwargs):
                     source = pos[comp]
                     target = pos[node_j]
 
+
+                    if pop[source] == 0:
+                        continue
+
                     rate = data['rate']
 
                     if 'start' in data and data['start'] >= t:
@@ -317,6 +342,18 @@ def simulate(self, timesteps, t_min=1, seasonality=None, **kwargs):
                 for i in range(len(delta)):
                     new_pop[i] += delta[i]
 
+            # Population dynamics
+            for comp, data in self.transitions.nodes(data=True):
+                comp_id = pos[comp]
+
+                if "birth" in data:
+                    births = np.random.binomial(pop[comp_id], data["birth"])
+                    new_pop[comp_id] += births
+
+                if "death" in data:
+                    deaths = np.random.binomial(pop[comp_id], data["death"])
+                    new_pop[comp_id] -= deaths
+
             values.append(new_pop)
 
         values = np.array(values)