Model Parameters

Analysis/1_mo_color_DOTFILE.dot

@@ -393,6 +393,7 @@ strict digraph G {
 391;
 392;
 393;
+394 [fillcolor=red,style=filled];
 0->1 [label=5];
 1->2 [label=5];
 2->3 [label=2];
@@ -1771,4 +1772,5 @@ strict digraph G {
 390->393 [label=1];
 391->393 [label=2];
 392->393 [label=2];
+393->394 [label=99];
 }

Analysis/prep_model.py

@@ -4,20 +4,23 @@ import networkx as nx
 import matplotlib.pyplot as plt
 from collections import OrderedDict
 from operator import getitem
-import itertools, os
+import itertools, os, sys
+
+# Change dir to location of this python file
+os.chdir(os.path.dirname(sys.argv[0]))
 
 # AGraph preserves attributes, networkx Graph does not.
 # Many of the desired functions are in networkx.
 # So import AGraph to keep attributes, then convert to Networkx.
 A = nx.drawing.nx_agraph.to_agraph(nx.drawing.nx_pydot.read_dot("./1_mo_color_DOTFILE.dot"))
 A.layout('dot')
-A.draw('tree.png')
+#A.draw('tree.png')
 A.remove_node('\\n') # Remove "newline" node from newline end of dot file
 G=nx.DiGraph(A)
 
 color_map = []
 color_d = {}
-node_pos = {}
+node_pos = {} # used for drawing/graphing
 
 # Compartments
 S = 0
@@ -26,14 +29,7 @@ E = 0
 R = 0
 D = 0
 
-# Params
+ep_tmp = 0 # counter for epsilon
-beta = 0
-delta = 0
-gamma_r = 0
-gamma_d = 0
-mu = 0
-epsilon = 0
-omega = 0
 
 for node in A:
     color = A.get_node(node).attr.to_dict()['fillcolor']
@@ -46,11 +42,64 @@ for node in A:
         color_map.append("white")
         color_d[node] = color
         in_edges = list(G.in_edges(node))
+        tmp_S = 1
+        for source in in_edges:
+            tmp_S = 1
+            # If previous node was infected, then we are recovered
+            if (color_d[source[0]] == 'red'):
+                R = R + 1
+                tmp_S = 0
+                break # No need to check the other nodes
+        S = S + tmp_S
+            #G[source[0]][node]['weight'] = 3 
     elif color == 'yellow':
         color_map.append(color)
         color_d[node] = color
         in_edges = list(G.in_edges(node))
+        tmp_E = 1
+        for source in in_edges:
+            tmp_E = 1
+            # If previous node was infected, then we are recovered
+            if (color_d[source[0]] == 'red'):
+                R = R + 1
+                tmp_E = 0
+                break # No need to check the other nodes
+        E = E + tmp_E
     else:
         color_map.append(color)
         color_d[node] = color
+        I = I + 1
+        # Check if node dies
+        out_edges = list(G.out_edges(node))
+        if not out_edges:
+            D = D + 1
+        # Check if imported
         in_edges = list(G.in_edges(node))
+        if not in_edges:
+            ep_tmp = ep_tmp + 1
+
+# Params
+beta = I/len(A) # rate of infec (I/total?)
+delta = E/len(A) # symptom appearance rate (E/total?)
+gamma_r = R/len(A) # recov rate (R/total?)
+gamma_d = D/len(A) # death rate (D/total?)
+mu = D/I # fatality ratio (D/I)
+epsilon = ep_tmp/len(A) # infected import rate
+omega = 0 # waning immunity rate
+
+
+print("Model Compartments:")
+print("S:", str(S))
+print("I:", str(I))
+print("E:", str(E))
+print("R:", str(R))
+print("D:", str(D))
+print("\n")
+
+print("Model Parameters:")
+print("beta:", str(beta))
+print("delta:", str(delta))
+print("gamma_r:", str(gamma_r))
+print("gamma_d:", str(gamma_d))
+print("mu:", str(mu))
+print("epsilon:", str(epsilon))