def test_create_cdf():
p = [0, 0.2, 0.7, 0.1]
cdf = create_cdf(p)
# ----- The first value of CDF should equal the first value of p
assert p[0] == cdf[0]
# ----- The final value of the distribution should be 1
assert cdf[-1] == 1
# ----- Length of cdf should equal length of p
assert len(cdf) == len(p)
def create_antibiotics_type_dictionary(self) -> dict:
""" Create the antibiotics distributions lists for each location
"""
td = dict()
for location in self.model.location.locations.enum:
category = self.model.location.locations.convert_int(
location.value, "category")
dist = self.params["distributions"][category]
td[location.value] = create_cdf(dist)
return td
def test_random_selection():
p = [0.1, 0.1, 0.7, 0.1]
options = [0, 1, 2, 3]
cdf = create_cdf(p)
rs = random_selection(0.95, cdf, options)
# ----- .95 is between .9 and 1.0, so 2 is selected
assert rs == 3
rs = random_selection(0.5, cdf, options)
# ----- .5 is between .2 and .7, so 2 is selected
assert rs == 2
rs = random_selection(0.11, cdf, options)
# ----- .1 is between 0.1 and .2, so 1 is selected
assert rs == 1
rs = random_selection(0.09, cdf, options)
# ----- .09 is between 0 and .1, so 0 is selected
assert rs == 0
def simulate_cre_transitions(self):
""" Simulate all agents CRE Transitions
1: SUSCEPTIBLE TO CRE: Community & Facility
2: CRE RECOVERY
"""
living = self.model.life.values == LifeState.ALIVE
susceptible_agents = (living) & (self.cre.values
== CREState.SUSCEPTIBLE)
cre_agents = (living) & (self.cre.values == CREState.CRE)
# ----- 1a: Community
community_id = self.model.location.locations.community
in_community = self.model.unique_ids[
(self.model.location.location.values == community_id)
& susceptible_agents]
probabilities = np.zeros(len(in_community))
probabilities.fill(self.params["cre"]["betas"]["base"]) # TODO
selected_agents = probabilities > self.model.rng.rand(
len(probabilities))
for unique_id in in_community[selected_agents]:
self.cre_update(unique_id, CREState.CRE)
# ----- 1b: Facility
in_facility = self.model.unique_ids[
(self.model.location.location.values != community_id)
& susceptible_agents]
probabilities = self.find_cre_probability(in_facility)
selected_agents = probabilities > self.model.rng.rand(
len(probabilities))
for unique_id in in_facility[selected_agents]:
self.cre_update(unique_id, CREState.CRE)
# ----- 2: CRE Recovery
probabilities = np.array([self.params["recovery"]] * len(cre_agents))
selected_agents = probabilities > self.model.rng.rand(
len(probabilities))
for unique_id in cre_agents[selected_agents]:
# --- Create recovery options (Susceptible, Colonized, Death)
to_death = self.params["death"]
to_susceptible = 1 - to_death
dist = [to_susceptible, to_death]
options = [CREState.SUSCEPTIBLE, CREState.DEAD]
end_state = random_selection(self.model.rng.rand(1),
create_cdf(dist), options)
self.cre_update(unique_id, end_state)
def select_facility(hospital: dict,
discharge_data: pd.DataFrame,
county_age_dict: dict,
fill_percent=.7):
""" Append the number of hospital patients required for a given county/age dictionary.
This will help fill the beds with agents
"""
hospital_id = hospital['ID']
rows = discharge_data[discharge_data[hospital_id] > 0].copy()
rows.loc[:, 'Percentage'] = rows[hospital_id] / rows[hospital_id].sum()
for bed in range(0, int(hospital['beds'] * fill_percent)):
p = create_cdf(rows.Percentage.values)
# --- Randomly select a county based on the percentage
county = random_selection(
np.random.rand(1, 1)[0], p, rows.County_Code.values)
# --- Randomly select an age based on 40% <50, 20% 50-65, and 40% 65+
age = random_selection(
np.random.rand(1, 1)[0], [.4, .6, 1.0], np.array([0, 1, 2]))
county_age_dict[(county, age)].append(hospital['int'])
def simulate_cdi_transitions(self):
""" Simulate all agents CDI Transitions
Find which patients are susceptible, colonized, or currently have CDI
1: Susecptible to Colonized (a: those in facilities, b: those in community)
2: Colonization Recovery
3: Colonized to CDI
4: CDI Recovery
"""
living = self.model.life.values == LifeState.ALIVE
susceptible_agents = (living) & (self.cdi.values == CDIState.SUSCEPTIBLE)
colonized_agents = self.model.unique_ids[
(living) & (self.cdi.values == CDIState.COLONIZED)
]
cdi_agents = self.model.unique_ids[(living) & (self.cdi.values == CDIState.CDI)]
# ----- 1a:
community = self.model.location.locations.community
in_facility = self.model.unique_ids[
(self.model.location.location.values != community) & susceptible_agents
]
probabilities = self.find_colonization_probability(in_facility)
selected_agents = probabilities > self.model.rng.rand(len(probabilities))
for unique_id in in_facility[selected_agents]:
self.cdi_update(unique_id, CDIState.COLONIZED)
# ----- 1b:
in_community = self.model.unique_ids[
(self.model.location.location.values == community) & susceptible_agents
]
probabilities = np.zeros(len(in_community))
probabilities.fill(self.colonization_dict[community])
selected_agents = probabilities > self.model.rng.rand(len(probabilities))
for unique_id in in_community[selected_agents]:
self.cdi_update(unique_id, CDIState.COLONIZED)
# ----- 2:
probabilities = np.array(
[self.params["colonization"]["clearance"]] * len(colonized_agents)
)
selected_agents = probabilities > self.model.rng.rand(len(probabilities))
for unique_id in colonized_agents[selected_agents]:
self.cdi_update(unique_id, CDIState.SUSCEPTIBLE)
# ----- 3:
probabilities = self.find_cdi_probability(colonized_agents)
selected_agents = probabilities > self.model.rng.rand(len(probabilities))
for unique_id in colonized_agents[selected_agents]:
self.cdi_update(unique_id, CDIState.CDI)
# ----- 4:
probabilities = np.array([self.params["cdi"]["recovery"]] * len(cdi_agents))
selected_agents = probabilities > self.model.rng.rand(len(probabilities))
for unique_id in cdi_agents[selected_agents]:
# --- Create recovery options (Susceptible, Colonized, Death)
to_death = self.params["cdi"]["death"]["age"][
str(int(self.model.age_groups[unique_id]))
]
to_col = self.params["cdi"]["recurrence"]["probability_with_recent_CDI"][
str(self.recent_cdi_count.get(unique_id, 0))
]
dist = [1 - to_death - to_col, to_col, to_death]
options = [CDIState.SUSCEPTIBLE, CDIState.COLONIZED, CDIState.DEAD]
end_state = random_selection(
self.model.rng.rand(1), create_cdf(dist), options
)
self.cdi_update(unique_id, end_state)
self.update_cdi_variables()
def create_cdf_dict(self):
for k, v in self.transition_dict.items():
self.cdf_dict[k] = create_cdf(v)