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Infection count edge weighting

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This commit is contained in:
Noah L. Schrick 2023-05-01 22:18:47 -05:00
parent 622daed52b
commit 9c2f92b91c
4 changed files with 154 additions and 105 deletions
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Analysis/SEIRDS.pdf Normal file
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Analysis/SERIDS.pdf
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29
Analysis/ode.R
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@ -1,6 +1,12 @@
## Set Working Directory to file directory - RStudio approach ## Set Working Directory to file directory - RStudio approach
setwd(dirname(rstudioapi::getActiveDocumentContext()$path)) setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
weighted <- 'False'
#conda_install(envname = "r-reticulate", packages="networkx")
#conda_install(envname = "r-reticulate", packages="matplotlib")
#conda_install(envname = "r-reticulate", packages="pydot")
#conda_install(envname = "r-reticulate", packages="pygraphviz")
seirds.f <- function(t, y, k) { seirds.f <- function(t, y, k) {
S <- y[1] S <- y[1]
E <- y[2] E <- y[2]
@ -18,7 +24,7 @@ seirds.f <- function(t, y, k) {
# Saying infec rate of S in contact with E same as contact with I # Saying infec rate of S in contact with E same as contact with I
dS <- epsilon - (beta*E + beta*I)*S + waning*R - gamma_d*S dS <- epsilon - (beta*E + beta*I)*S + waning*R - gamma_d*S
dE <- beta*S*E + beta*S*I - (delta+gamma_d)*E dE <- (beta*E + beta*I)*S - (delta+gamma_d)*E
dI <- delta*E - (1+gamma_d)*I dI <- delta*E - (1+gamma_d)*I
dR <- (1-mu)*I - (waning+gamma_d)*R dR <- (1-mu)*I - (waning+gamma_d)*R
dD <- mu*I dD <- mu*I
@ -27,11 +33,22 @@ seirds.f <- function(t, y, k) {
} }
library(reticulate) library(reticulate)
#conda_install(envname = "r-reticulate", packages="networkx")
#conda_install(envname = "r-reticulate", packages="matplotlib")
#conda_install(envname = "r-reticulate", packages="pydot")
#conda_install(envname = "r-reticulate", packages="pygraphviz")
source_python('prep_model.py') source_python('prep_model.py')
model_data <- prep_seirds(weighted)
S <- unlist(model_data)[1]
E <- unlist(model_data)[2]
I <- unlist(model_data)[3]
R <- unlist(model_data)[4]
D <- unlist(model_data)[5]
beta <- unlist(model_data)[6]
delta <- unlist(model_data)[7]
gamma_r <- unlist(model_data)[8]
gamma_d <- unlist(model_data)[9]
mu <- unlist(model_data)[10]
epsilon <- unlist(model_data)[11]
omega <- unlist(model_data)[12]
# Obtained from prep_model.py # Obtained from prep_model.py
seirds.params <- c(beta, # beta seirds.params <- c(beta, # beta
@ -79,6 +96,6 @@ plot.seirds <- function(sol, method){
} }
plot.seirds(seirds.ode.sol, "ODE45") plot.seirds(seirds.ode.sol, "ODE45")
ggsave("SERIDS.pdf") ggsave("SEIRDS.pdf")
# Sanity check: Make sure sums to ~1.0 # Sanity check: Make sure sums to ~1.0
sum(tail(seirds.ode.sol$y,1)) sum(tail(seirds.ode.sol$y,1))

214
Analysis/prep_model.py
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@ -11,104 +11,136 @@ print(os.getcwd())
#os.chdir(os.path.dirname(sys.argv[0])) #os.chdir(os.path.dirname(sys.argv[0]))
#print(os.getcwd()) #print(os.getcwd())
# AGraph preserves attributes, networkx Graph does not. def prep_seirds(weighted):
# Many of the desired functions are in networkx. print("Prepping the SEIRDS model, using trivial weighting=", weighted)
# So import AGraph to keep attributes, then convert to Networkx. # AGraph preserves attributes, networkx Graph does not.
A = nx.drawing.nx_agraph.to_agraph(nx.drawing.nx_pydot.read_dot("./1_mo_color_DOTFILE.dot")) # Many of the desired functions are in networkx.
A.layout('dot') # So import AGraph to keep attributes, then convert to Networkx.
#A.draw('tree.png') A = nx.drawing.nx_agraph.to_agraph(nx.drawing.nx_pydot.read_dot("./1_mo_color_DOTFILE.dot"))
A.remove_node('\\n') # Remove "newline" node from newline end of dot file A.layout('dot')
G=nx.DiGraph(A) #A.draw('tree.png')
A.remove_node('\\n') # Remove "newline" node from newline end of dot file
G=nx.DiGraph(A)
color_map = [] color_map = []
color_d = {} color_d = {}
node_pos = {} # used for drawing/graphing node_pos = {} # used for drawing/graphing
# Compartments # Compartments
S = 0 S = 0
I_R = 0 I_R = 0
I_D = 0 I_D = 0
E = 0 E = 0
R = 0 R = 0
D = 0 D = 0
ep_tmp = 0 # counter for epsilon ep_tmp = 0 # counter for epsilon
inf_ct = 0 # infection rate counter
for node in A: for node in A:
color = A.get_node(node).attr.to_dict()['fillcolor'] color = A.get_node(node).attr.to_dict()['fillcolor']
str_pos = A.get_node(node).attr.to_dict()['pos'] str_pos = A.get_node(node).attr.to_dict()['pos']
coords = str_pos.split(',') coords = str_pos.split(',')
x = coords[0] # layout for draw function x = coords[0] # layout for draw function
y = coords[1] y = coords[1]
node_pos[node] = float(x), float(y) node_pos[node] = float(x), float(y)
if color is None or color == '': if color is None or color == '':
color_map.append("white") color_map.append("white")
color_d[node] = color color_d[node] = color
in_edges = list(G.in_edges(node)) in_edges = list(G.in_edges(node))
tmp_S = 1 out_edges = list(G.out_edges(node))
for source in in_edges:
tmp_S = 1
# If previous node was infected, then we are recovered tmp_S = 1
tmp_inf = 0
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
for source in out_edges:
if (color_d[source[0]] == 'red'): if (color_d[source[0]] == 'red'):
R = R + 1 if(weighted == 'False' or not out_edges):
tmp_S = 0 tmp_inf = 1
break # No need to check the other nodes else:
S = S + tmp_S inf_ct = inf_ct + 1/len(out_edges) # trivial weighting
#G[source[0]][node]['weight'] = 3 inf_ct = inf_ct + tmp_inf
elif color == 'yellow': S = S + tmp_S
color_map.append(color) #G[source[0]][node]['weight'] = 3
color_d[node] = color elif color == 'yellow':
in_edges = list(G.in_edges(node)) color_map.append(color)
tmp_E = 1 color_d[node] = color
for source in in_edges: in_edges = list(G.in_edges(node))
tmp_E = 1 tmp_E = 1
# If previous node was infected, then we are recovered tmp_R = 0
if (color_d[source[0]] == 'red'): tmp_inf = 0
R = R + 1 for source in in_edges:
tmp_E = 0 # If previous node was infected, then we are recovered
break # No need to check the other nodes if (color_d[source[0]] == 'red'):
E = E + tmp_E tmp_R = 1
else: tmp_E = 0
color_map.append(color) if (color_d[source[0]] == '' or color_d[source[0]] == 'white'):
color_d[node] = color if(weighted == 'False' or not in_edges):
# Check if node dies tmp_inf = 1 # add 1 for the inf counter
out_edges = list(G.out_edges(node)) else:
if not out_edges: inf_ct = inf_ct + 1/len(in_edges) # trivial weighting
D = D + 1 E = E + tmp_E
I_D = I_D + 1 R = R + tmp_R
else: inf_ct = inf_ct + tmp_inf
I_R = I_R + 1 else:
# Check if imported color_map.append(color)
in_edges = list(G.in_edges(node)) color_d[node] = color
if not in_edges: # Check if node dies
ep_tmp = ep_tmp + 1 out_edges = list(G.out_edges(node))
if not out_edges:
D = D + 1
I_D = I_D + 1
else:
I_R = I_R + 1
# Check if imported
in_edges = list(G.in_edges(node))
if not in_edges:
ep_tmp = ep_tmp + 1
else:
tmp_inf = 0
for source in in_edges:
if (color_d[source[0]] == '' or color_d[source[0]] == 'white'):
if(weighted == 'False' or not in_edges):
tmp_inf = 1
else:
inf_ct = inf_ct + 1/len(in_edges) # trivial weightin
inf_ct = inf_ct + tmp_inf
# Params # Params
beta = (I_R+I_D)/len(A) # rate of infec (I/total?) beta = (inf_ct)/len(A) # rate of infec
#delta = E/len(A) # symptom appearance rate (E/total?) delta = 1 # incubation period
delta = 1 # incubation period gamma_r = R/len(A) # recov rate
gamma_r = R/len(A) # recov rate (R/total?) gamma_d = D/len(A) # death rate
gamma_d = D/len(A) # death rate (D/total?) mu = D/(I_R+I_D) # fatality ratio
mu = D/(I_R+I_D) # fatality ratio (D/I) epsilon = ep_tmp/len(A) # infected import rate
epsilon = ep_tmp/len(A) # infected import rate omega = 1 # waning immunity rate
omega = 1 # waning immunity rate
print("Model Compartments:") print("Model Compartments:")
print("S:", str(S)) print("S:", str(S))
print("E:", str(E)) print("E:", str(E))
print("I_R:", str(I_R)) print("I_R:", str(I_R))
print("I_D:", str(I_D)) print("I_D:", str(I_D))
print("R:", str(R)) print("R:", str(R))
print("D:", str(D)) print("D:", str(D))
print("\n") print("\n")
print("Model Parameters:") print("Model Parameters:")
print("beta:", str(beta)) print("infect counter:", str(inf_ct))
print("delta:", str(delta)) print("beta:", str(beta))
print("gamma_r:", str(gamma_r)) print("delta:", str(delta))
print("gamma_d:", str(gamma_d)) print("gamma_r:", str(gamma_r))
print("mu:", str(mu)) print("gamma_d:", str(gamma_d))
print("epsilon:", str(epsilon)) print("mu:", str(mu))
print("omega:", str(omega)) print("epsilon:", str(epsilon))
print("omega:", str(omega))
return (S, E, I_R+I_D, R, D, beta, delta, gamma_r, gamma_d, mu, epsilon, omega)