Infection count edge weighting
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Analysis/SEIRDS.pdf
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Analysis/SEIRDS.pdf
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@ -1,6 +1,12 @@
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## Set Working Directory to file directory - RStudio approach
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setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
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weighted <- 'False'
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#conda_install(envname = "r-reticulate", packages="networkx")
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#conda_install(envname = "r-reticulate", packages="matplotlib")
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#conda_install(envname = "r-reticulate", packages="pydot")
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#conda_install(envname = "r-reticulate", packages="pygraphviz")
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seirds.f <- function(t, y, k) {
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S <- y[1]
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E <- y[2]
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@ -18,7 +24,7 @@ seirds.f <- function(t, y, k) {
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# Saying infec rate of S in contact with E same as contact with I
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dS <- epsilon - (beta*E + beta*I)*S + waning*R - gamma_d*S
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dE <- beta*S*E + beta*S*I - (delta+gamma_d)*E
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dE <- (beta*E + beta*I)*S - (delta+gamma_d)*E
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dI <- delta*E - (1+gamma_d)*I
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dR <- (1-mu)*I - (waning+gamma_d)*R
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dD <- mu*I
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@ -27,11 +33,22 @@ seirds.f <- function(t, y, k) {
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}
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library(reticulate)
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#conda_install(envname = "r-reticulate", packages="networkx")
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#conda_install(envname = "r-reticulate", packages="matplotlib")
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#conda_install(envname = "r-reticulate", packages="pydot")
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#conda_install(envname = "r-reticulate", packages="pygraphviz")
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source_python('prep_model.py')
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model_data <- prep_seirds(weighted)
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S <- unlist(model_data)[1]
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E <- unlist(model_data)[2]
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I <- unlist(model_data)[3]
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R <- unlist(model_data)[4]
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D <- unlist(model_data)[5]
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beta <- unlist(model_data)[6]
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delta <- unlist(model_data)[7]
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gamma_r <- unlist(model_data)[8]
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gamma_d <- unlist(model_data)[9]
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mu <- unlist(model_data)[10]
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epsilon <- unlist(model_data)[11]
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omega <- unlist(model_data)[12]
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# Obtained from prep_model.py
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seirds.params <- c(beta, # beta
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@ -79,6 +96,6 @@ plot.seirds <- function(sol, method){
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}
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plot.seirds(seirds.ode.sol, "ODE45")
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ggsave("SERIDS.pdf")
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ggsave("SEIRDS.pdf")
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# Sanity check: Make sure sums to ~1.0
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sum(tail(seirds.ode.sol$y,1))
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@ -11,104 +11,136 @@ print(os.getcwd())
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#os.chdir(os.path.dirname(sys.argv[0]))
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#print(os.getcwd())
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# AGraph preserves attributes, networkx Graph does not.
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# Many of the desired functions are in networkx.
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# So import AGraph to keep attributes, then convert to Networkx.
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A = nx.drawing.nx_agraph.to_agraph(nx.drawing.nx_pydot.read_dot("./1_mo_color_DOTFILE.dot"))
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A.layout('dot')
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#A.draw('tree.png')
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A.remove_node('\\n') # Remove "newline" node from newline end of dot file
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G=nx.DiGraph(A)
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color_map = []
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color_d = {}
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node_pos = {} # used for drawing/graphing
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# Compartments
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S = 0
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I_R = 0
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I_D = 0
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E = 0
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R = 0
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D = 0
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ep_tmp = 0 # counter for epsilon
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for node in A:
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color = A.get_node(node).attr.to_dict()['fillcolor']
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str_pos = A.get_node(node).attr.to_dict()['pos']
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coords = str_pos.split(',')
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x = coords[0] # layout for draw function
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y = coords[1]
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node_pos[node] = float(x), float(y)
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if color is None or color == '':
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color_map.append("white")
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color_d[node] = color
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in_edges = list(G.in_edges(node))
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tmp_S = 1
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for source in in_edges:
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tmp_S = 1
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# If previous node was infected, then we are recovered
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def prep_seirds(weighted):
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print("Prepping the SEIRDS model, using trivial weighting=", weighted)
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# AGraph preserves attributes, networkx Graph does not.
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# Many of the desired functions are in networkx.
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# So import AGraph to keep attributes, then convert to Networkx.
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A = nx.drawing.nx_agraph.to_agraph(nx.drawing.nx_pydot.read_dot("./1_mo_color_DOTFILE.dot"))
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A.layout('dot')
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#A.draw('tree.png')
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A.remove_node('\\n') # Remove "newline" node from newline end of dot file
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G=nx.DiGraph(A)
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color_map = []
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color_d = {}
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node_pos = {} # used for drawing/graphing
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# Compartments
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S = 0
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I_R = 0
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I_D = 0
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E = 0
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R = 0
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D = 0
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ep_tmp = 0 # counter for epsilon
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inf_ct = 0 # infection rate counter
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for node in A:
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color = A.get_node(node).attr.to_dict()['fillcolor']
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str_pos = A.get_node(node).attr.to_dict()['pos']
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coords = str_pos.split(',')
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x = coords[0] # layout for draw function
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y = coords[1]
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node_pos[node] = float(x), float(y)
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if color is None or color == '':
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color_map.append("white")
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color_d[node] = color
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in_edges = list(G.in_edges(node))
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out_edges = list(G.out_edges(node))
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tmp_S = 1
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tmp_inf = 0
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for source in in_edges:
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tmp_S = 1
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# If previous node was infected, then we are recovered
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if (color_d[source[0]] == 'red'):
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R = R + 1
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tmp_S = 0
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break # No need to check the other nodes
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for source in out_edges:
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if (color_d[source[0]] == 'red'):
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R = R + 1
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tmp_S = 0
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break # No need to check the other nodes
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S = S + tmp_S
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#G[source[0]][node]['weight'] = 3
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elif color == 'yellow':
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color_map.append(color)
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color_d[node] = color
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in_edges = list(G.in_edges(node))
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tmp_E = 1
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for source in in_edges:
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tmp_E = 1
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# If previous node was infected, then we are recovered
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if (color_d[source[0]] == 'red'):
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R = R + 1
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tmp_E = 0
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break # No need to check the other nodes
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E = E + tmp_E
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else:
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color_map.append(color)
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color_d[node] = color
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# Check if node dies
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out_edges = list(G.out_edges(node))
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if not out_edges:
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D = D + 1
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I_D = I_D + 1
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else:
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I_R = I_R + 1
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# Check if imported
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in_edges = list(G.in_edges(node))
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if not in_edges:
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ep_tmp = ep_tmp + 1
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# Params
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beta = (I_R+I_D)/len(A) # rate of infec (I/total?)
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#delta = E/len(A) # symptom appearance rate (E/total?)
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delta = 1 # incubation period
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gamma_r = R/len(A) # recov rate (R/total?)
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gamma_d = D/len(A) # death rate (D/total?)
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mu = D/(I_R+I_D) # fatality ratio (D/I)
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epsilon = ep_tmp/len(A) # infected import rate
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omega = 1 # waning immunity rate
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print("Model Compartments:")
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print("S:", str(S))
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print("E:", str(E))
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print("I_R:", str(I_R))
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print("I_D:", str(I_D))
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print("R:", str(R))
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print("D:", str(D))
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print("\n")
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print("Model Parameters:")
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print("beta:", str(beta))
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print("delta:", str(delta))
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print("gamma_r:", str(gamma_r))
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print("gamma_d:", str(gamma_d))
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print("mu:", str(mu))
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print("epsilon:", str(epsilon))
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print("omega:", str(omega))
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if(weighted == 'False' or not out_edges):
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tmp_inf = 1
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else:
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inf_ct = inf_ct + 1/len(out_edges) # trivial weighting
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inf_ct = inf_ct + tmp_inf
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S = S + tmp_S
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#G[source[0]][node]['weight'] = 3
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elif color == 'yellow':
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color_map.append(color)
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color_d[node] = color
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in_edges = list(G.in_edges(node))
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tmp_E = 1
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tmp_R = 0
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tmp_inf = 0
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for source in in_edges:
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# If previous node was infected, then we are recovered
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if (color_d[source[0]] == 'red'):
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tmp_R = 1
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tmp_E = 0
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if (color_d[source[0]] == '' or color_d[source[0]] == 'white'):
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if(weighted == 'False' or not in_edges):
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tmp_inf = 1 # add 1 for the inf counter
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else:
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inf_ct = inf_ct + 1/len(in_edges) # trivial weighting
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E = E + tmp_E
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R = R + tmp_R
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inf_ct = inf_ct + tmp_inf
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else:
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color_map.append(color)
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color_d[node] = color
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# Check if node dies
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out_edges = list(G.out_edges(node))
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if not out_edges:
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D = D + 1
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I_D = I_D + 1
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else:
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I_R = I_R + 1
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# Check if imported
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in_edges = list(G.in_edges(node))
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if not in_edges:
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ep_tmp = ep_tmp + 1
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else:
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tmp_inf = 0
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for source in in_edges:
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if (color_d[source[0]] == '' or color_d[source[0]] == 'white'):
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if(weighted == 'False' or not in_edges):
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tmp_inf = 1
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else:
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inf_ct = inf_ct + 1/len(in_edges) # trivial weightin
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inf_ct = inf_ct + tmp_inf
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# Params
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beta = (inf_ct)/len(A) # rate of infec
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delta = 1 # incubation period
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gamma_r = R/len(A) # recov rate
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gamma_d = D/len(A) # death rate
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mu = D/(I_R+I_D) # fatality ratio
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epsilon = ep_tmp/len(A) # infected import rate
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omega = 1 # waning immunity rate
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print("Model Compartments:")
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print("S:", str(S))
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print("E:", str(E))
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print("I_R:", str(I_R))
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print("I_D:", str(I_D))
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print("R:", str(R))
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print("D:", str(D))
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print("\n")
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print("Model Parameters:")
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print("infect counter:", str(inf_ct))
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print("beta:", str(beta))
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print("delta:", str(delta))
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print("gamma_r:", str(gamma_r))
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print("gamma_d:", str(gamma_d))
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print("mu:", str(mu))
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print("epsilon:", str(epsilon))
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print("omega:", str(omega))
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return (S, E, I_R+I_D, R, D, beta, delta, gamma_r, gamma_d, mu, epsilon, omega)
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