def vaccine_dgm(network,
restricted=False,
n_init_infect=7,
time_limit=10,
inf_duration=5):
"""
Parameters
----------
network:
input network
restricted:
whether to use the restricted treatment assignment
n_init_infect:
number of initial infections to start with
time_limit:
maximum time to let the outbreak go through
inf_duration:
duration of infection status in time-steps
"""
graph = network.copy()
data = network_to_df(graph)
adj_matrix = nx.adjacency_matrix(graph, weight=None)
data['A_sum'] = fast_exp_map(adj_matrix,
np.array(data['A']),
measure='sum')
data['A_mean'] = fast_exp_map(adj_matrix,
np.array(data['A']),
measure='mean')
data['H_mean'] = fast_exp_map(adj_matrix,
np.array(data['H']),
measure='mean')
# Running Data Generating Mechanism for A
pr_a = logistic.cdf(-1.9 + 1.75 * data['A'] + 0.95 * data['H'] +
1.2 * data['H_mean'])
vaccine = np.random.binomial(n=1, p=pr_a, size=nx.number_of_nodes(graph))
data['vaccine'] = vaccine
if restricted: # if we are in the restricted scenarios
attrs = exposure_restrictions(network=network.graph['label'],
exposure='vaccine')
data.update(
pd.DataFrame(list(attrs.values()),
index=list(attrs.keys()),
columns=['vaccine']))
# print("Pr(V):", np.mean(vaccine))
for n in graph.nodes():
graph.nodes[n]['vaccine'] = int(data.loc[data.index == n,
'vaccine'].values)
# Running outbreak simulation
graph = _outbreak_(graph,
n_init_infect,
duration=inf_duration,
limit=time_limit)
return graph
def naloxone_dgm_truth(network, pr_a, shift=False, restricted=False):
graph = network.copy()
data = network_to_df(graph)
adj_matrix = nx.adjacency_matrix(graph, weight=None)
data['O_sum'] = fast_exp_map(adj_matrix,
np.array(data['O']),
measure='sum')
data['O_mean'] = fast_exp_map(adj_matrix,
np.array(data['O']),
measure='mean')
data['G_sum'] = fast_exp_map(adj_matrix,
np.array(data['G']),
measure='sum')
data['G_mean'] = fast_exp_map(adj_matrix,
np.array(data['G']),
measure='mean')
# Running Data Generating Mechanism for A
if shift: # If a shift in the Odds distribution is instead specified
prob = logistic.cdf(-1.3 - 1.5 * data['P'] +
1.5 * data['P'] * data['G'] +
0.95 * data['O_mean'] + 0.95 * data['G_mean'])
odds = probability_to_odds(prob)
pr_a = odds_to_probability(np.exp(np.log(odds) + pr_a))
naloxone = np.random.binomial(n=1, p=pr_a, size=nx.number_of_nodes(graph))
data['naloxone'] = naloxone
if restricted: # if we are in the restricted scenarios
attrs = exposure_restrictions(network=network.graph['label'],
exposure='naloxone')
data.update(
pd.DataFrame(list(attrs.values()),
index=list(attrs.keys()),
columns=['naloxone']))
exclude = list(attrs.keys())
# Creating network summary variables
data['naloxone_sum'] = fast_exp_map(adj_matrix,
np.array(data['naloxone']),
measure='sum')
# Running Data Generating Mechanism for Y
pr_y = logistic.cdf(-1.1 - 0.2 * data['naloxone_sum'] + 1.7 * data['P'] -
0.9 * data['G'] + 0.75 * data['O_mean'] -
0.75 * data['G_mean'])
overdose = np.random.binomial(n=1, p=pr_y, size=nx.number_of_nodes(graph))
if restricted:
data['overdose'] = overdose
data = data.loc[~data.index.isin(exclude)].copy()
overdose = np.array(data['overdose'])
return np.mean(overdose)
def diet_dgm_truth(network, pr_a, restricted=False, shift=False):
graph = network.copy()
data = network_to_df(graph)
adj_matrix = nx.adjacency_matrix(graph, weight=None)
data['B_mean_dist'] = fast_exp_map(adj_matrix,
np.array(data['B']),
measure='mean_dist')
data['E_sum'] = fast_exp_map(adj_matrix,
np.array(data['E']),
measure='sum')
data['E_mean'] = fast_exp_map(adj_matrix,
np.array(data['E']),
measure='mean')
if shift: # If a shift in the Odds distribution is instead specified
prob = logistic.cdf(-0.5 + 0.05 * (data['B'] - 30) +
0.25 * data['G'] * data['E'] +
0.05 * data['E_mean'])
odds = probability_to_odds(prob)
pr_a = odds_to_probability(np.exp(np.log(odds) + pr_a))
diet = np.random.binomial(n=1, p=pr_a, size=nx.number_of_nodes(graph))
data['diet'] = diet
if restricted: # if we are in the restricted scenarios
attrs = exposure_restrictions(network=network.graph['label'],
exposure='diet')
data.update(
pd.DataFrame(list(attrs.values()),
index=list(attrs.keys()),
columns=['diet']))
exclude = list(attrs.keys())
# Running Data Generating Mechanism for Y
data['diet_sum'] = fast_exp_map(adj_matrix,
np.asarray(data['diet']),
measure='sum')
data['diet_t3'] = np.where(data['diet_sum'] > 3, 1, 0)
bmi = (3 + data['B'] - 5 * data['diet'] - 5 * data['diet_t3'] +
3 * data['G'] - 3 * data['E'] - 0.5 * data['E_sum'] +
data['B_mean_dist'] +
np.random.normal(0, scale=1, size=nx.number_of_nodes(graph)))
if restricted:
data['bmi'] = bmi
data = data.loc[~data.index.isin(exclude)].copy()
bmi = np.array(data['bmi'])
return np.mean(bmi)
def vaccine_dgm_truth(network,
pr_a,
shift=False,
restricted=False,
n_init_infect=7,
time_limit=10,
inf_duration=5):
graph = network.copy()
data = network_to_df(graph)
# Running Data Generating Mechanism for A
if shift: # If a shift in the Odds distribution is instead specified
adj_matrix = nx.adjacency_matrix(graph, weight=None)
data['A_sum'] = fast_exp_map(adj_matrix,
np.array(data['A']),
measure='sum')
data['H_mean'] = fast_exp_map(adj_matrix,
np.array(data['H']),
measure='mean')
prob = logistic.cdf(-1.9 + 1.75 * data['A'] + 0.95 * data['H'] +
1.2 * data['H_mean'])
odds = probability_to_odds(prob)
pr_a = odds_to_probability(np.exp(np.log(odds) + pr_a))
vaccine = np.random.binomial(n=1, p=pr_a, size=nx.number_of_nodes(graph))
data['vaccine'] = vaccine
if restricted: # if we are in the restricted scenarios
attrs = exposure_restrictions(network=network.graph['label'],
exposure='vaccine')
data.update(
pd.DataFrame(list(attrs.values()),
index=list(attrs.keys()),
columns=['vaccine']))
for n in graph.nodes():
graph.nodes[n]['vaccine'] = int(data.loc[data.index == n,
'vaccine'].values)
# Running Data Generating Mechanism for Y
graph = _outbreak_(graph,
n_init_infect,
duration=inf_duration,
limit=time_limit)
dis = []
for nod, d in graph.nodes(data=True):
dis.append(d['D'])
return np.mean(dis)
def diet_dgm(network, restricted=False):
"""
Parameters
----------
network:
input network
restricted:
whether to use the restricted treatment assignment
"""
graph = network.copy()
data = network_to_df(graph)
adj_matrix = nx.adjacency_matrix(graph, weight=None)
data['G_mean'] = fast_exp_map(adj_matrix,
np.array(data['G']),
measure='mean')
data['E_mean'] = fast_exp_map(adj_matrix,
np.array(data['E']),
measure='mean')
data['E_sum'] = fast_exp_map(adj_matrix,
np.array(data['E']),
measure='sum')
data['B_mean_dist'] = fast_exp_map(adj_matrix,
np.array(data['B']),
measure='mean_dist')
data['B_mean'] = fast_exp_map(adj_matrix,
np.array(data['B']),
measure='mean')
# Running Data Generating Mechanism for A
pr_a = logistic.cdf(-0.5 + 0.05 * (data['B'] - 30) +
0.25 * data['G'] * data['E'] + 0.05 * data['E_mean'])
diet = np.random.binomial(n=1, p=pr_a, size=nx.number_of_nodes(graph))
data['diet'] = diet
if restricted: # if we are in the restricted scenarios
attrs = exposure_restrictions(network=network.graph['label'],
exposure='diet')
data.update(
pd.DataFrame(list(attrs.values()),
index=list(attrs.keys()),
columns=['diet']))
data['diet_sum'] = fast_exp_map(adj_matrix,
np.array(data['diet']),
measure='sum')
data['diet_t3'] = np.where(data['diet_sum'] > 3, 1, 0)
# Running Data Generating Mechanism for Y
bmi = (3 + data['B'] - 5 * data['diet'] - 5 * data['diet_t3'] +
3 * data['G'] - 3 * data['E'] - 0.5 * data['E_sum'] +
data['B_mean_dist'] +
np.random.normal(0, scale=1, size=nx.number_of_nodes(graph)))
data['bmi'] = bmi
# Adding node information back to graph
for n in graph.nodes():
graph.nodes[n]['diet'] = int(data.loc[data.index == n, 'diet'].values)
graph.nodes[n]['bmi'] = float(data.loc[data.index == n, 'bmi'].values)
return graph
def naloxone_dgm(network, restricted=False):
"""
Parameters
----------
network:
input network
restricted:
whether to use the restricted treatment assignment
"""
graph = network.copy()
data = network_to_df(graph)
adj_matrix = nx.adjacency_matrix(graph, weight=None)
data['O_sum'] = fast_exp_map(adj_matrix,
np.array(data['O']),
measure='sum')
data['O_mean'] = fast_exp_map(adj_matrix,
np.array(data['O']),
measure='mean')
data['G_sum'] = fast_exp_map(adj_matrix,
np.array(data['G']),
measure='sum')
data['G_mean'] = fast_exp_map(adj_matrix,
np.array(data['G']),
measure='mean')
# Running Data Generating Mechanism for A
pr_a = logistic.cdf(-1.3 - 1.5 * data['P'] + 1.5 * data['P'] * data['G'] +
0.95 * data['O_mean'] + 0.95 * data['G_mean'])
naloxone = np.random.binomial(n=1, p=pr_a, size=nx.number_of_nodes(graph))
data[
'naloxone'] = naloxone # https://www.sciencedirect.com/science/article/pii/S074054721730301X (30%)
if restricted: # if we are in the restricted scenarios
attrs = exposure_restrictions(network=network.graph['label'],
exposure='naloxone')
data.update(
pd.DataFrame(list(attrs.values()),
index=list(attrs.keys()),
columns=['naloxone']))
data['naloxone_sum'] = fast_exp_map(adj_matrix,
np.array(data['naloxone']),
measure='sum')
# Running Data Generating Mechanism for Y
pr_y = logistic.cdf(-1.1 - 0.2 * data['naloxone_sum'] + 1.7 * data['P'] -
0.9 * data['G'] + 0.75 * data['O_mean'] -
0.75 * data['G_mean'])
overdose = np.random.binomial(n=1, p=pr_y, size=nx.number_of_nodes(graph))
data[
'overdose'] = overdose # https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5832501/?report=classic (20%)
# print(data[['naloxone', 'overdose']].describe())
# Adding node information back to graph
for n in graph.nodes():
graph.nodes[n]['naloxone'] = int(data.loc[data.index == n,
'naloxone'].values)
graph.nodes[n]['overdose'] = int(data.loc[data.index == n,
'overdose'].values)
return graph