def __init__(
self,
state,
reference_date=datetime(day=1, month=3, year=2020),
plot_compartments=("HICU", "HGen", "HVent"),
primary_suppression_policy="suppression_policy__0.5",
):
self.state = state
self.reference_date = reference_date
self.plot_compartments = plot_compartments
self.primary_suppression_policy = primary_suppression_policy
# Load the county metadata and extract names for the state.
county_metadata = load_data.load_county_metadata()
self.counties = county_metadata[county_metadata["state"].str.lower() ==
self.state.lower()]["fips"]
self.ensemble_data_by_county = {
fips: load_data.load_ensemble_results(fips)
for fips in self.counties
}
self.county_metadata = county_metadata.set_index("fips")
self.names = [
self.county_metadata.loc[fips, "county"].replace(" County", "")
for fips in self.counties
]
self.filename = os.path.join(
OUTPUT_DIR, self.state, "reports",
f"summary__{self.state}__state_report.pdf")
self.surge_filename = os.path.join(
OUTPUT_DIR, self.state, "reports",
f"summary__{self.state}__state_surge_report.xlsx")
def __init__(self,
state,
reference_date=datetime(day=1, month=3, year=2020),
plot_compartments=('HICU', 'HGen', 'HVent'),
primary_suppression_policy='suppression_policy__0.5'):
self.state = state
self.reference_date = reference_date
self.plot_compartments = plot_compartments
self.primary_suppression_policy = primary_suppression_policy
# Load the county metadata and extract names for the state.
county_metadata = load_data.load_county_metadata()
self.counties = county_metadata[county_metadata['state'].str.lower() ==
self.state.lower()]['fips']
self.ensemble_data_by_county = {
fips: load_data.load_ensemble_results(fips)
for fips in self.counties
}
self.county_metadata = county_metadata.set_index('fips')
self.names = [
self.county_metadata.loc[fips, 'county'].replace(' County', '')
for fips in self.counties
]
self.filename = os.path.join(
OUTPUT_DIR, self.state, 'reports',
f"summary__{self.state}__state_report.pdf")
self.surge_filename = os.path.join(
OUTPUT_DIR, self.state, 'reports',
f"summary__{self.state}__state_surge_report.xlsx")
def generate_surge_spreadsheet(self):
"""
Produce a spreadsheet summarizing peaks.
Parameters
----------
state: str
State to generate sheet for.
Returns
-------
"""
df = load_data.load_county_metadata()
all_fips = df[df['state'].str.lower() == self.state.lower()].fips
all_data = {fips: load_data.load_ensemble_results(fips) for fips in all_fips}
df = df.set_index('fips')
records = []
for fips, ensembles in all_data.items():
county_name = df.loc[fips]['county']
t0 = fit_results.load_t0(fips)
for suppression_policy, ensemble in ensembles.items():
county_record = dict(
county_name=county_name,
county_fips=fips,
mitigation_policy=policy_to_mitigation(suppression_policy)
)
for compartment in ['HGen', 'general_admissions_per_day', 'HICU', 'icu_admissions_per_day', 'total_new_infections',
'direct_deaths_per_day', 'total_deaths', 'D']:
compartment_name = compartment_to_name_map[compartment]
county_record[compartment_name + ' Peak Value Mean'] = '%.0f' % ensemble[compartment]['peak_value_mean']
county_record[compartment_name + ' Peak Value Median'] = '%.0f' % ensemble[compartment]['peak_value_ci50']
county_record[compartment_name + ' Peak Value CI25'] = '%.0f' % ensemble[compartment]['peak_value_ci25']
county_record[compartment_name + ' Peak Value CI75'] = '%.0f' % ensemble[compartment]['peak_value_ci75']
county_record[compartment_name + ' Peak Time Median'] = (t0 + timedelta(days=ensemble[compartment]['peak_time_ci50'])).date().isoformat()
# Leaving for now...
# if 'surge_start' in ensemble[compartment]:
# if not np.isnan(np.nanmean(ensemble[compartment]['surge_start'])):
# county_record[compartment_name + ' Surge Start Mean'] = (t0 + timedelta(days=np.nanmean(ensemble[compartment]['surge_start']))).date().isoformat()
# county_record[compartment_name + ' Surge End Mean'] = (t0 + timedelta(days=np.nanmean(ensemble[compartment]['surge_end']))).date().isoformat()
records.append(county_record)
df = pd.DataFrame(records)
writer = pd.ExcelWriter(self.surge_filename, engine='xlsxwriter')
for policy in df['mitigation_policy'].unique()[::-1]:
df[df['mitigation_policy'] == policy].drop(['mitigation_policy', 'county_fips'], axis=1)
df[df['mitigation_policy'] == policy].drop(['mitigation_policy', 'county_fips'], axis=1).to_excel(writer, sheet_name=policy)
writer.save()
def map_fips(self, fips):
"""
For a given county fips code, generate the CAN UI output format.
Parameters
----------
fips: str
County FIPS code to map.
"""
logging.info(f"Mapping output to WebUI for {self.state}, {fips}")
pyseir_outputs = load_data.load_ensemble_results(fips)
if len(fips) == 5 and fips not in self.df_whitelist.fips.values:
logging.info(f"Excluding {fips} due to white list...")
return
try:
fit_results = load_inference_result(fips)
t0_simulation = datetime.fromisoformat(fit_results["t0_date"])
except (KeyError, ValueError):
logging.error(f"Fit result not found for {fips}. Skipping...")
return
# ---------------------------------------------------------------------
# Rescale hosps based on the population ratio... Could swap this to
# infection ratio later?
# ---------------------------------------------------------------------
hosp_times, current_hosp, _ = load_data.load_hospitalization_data_by_state(
state=self.state_abbreviation,
t0=t0_simulation,
convert_cumulative_to_current=True,
category="hospitalized",
)
_, current_icu, _ = load_data.load_hospitalization_data_by_state(
state=self.state_abbreviation,
t0=t0_simulation,
convert_cumulative_to_current=True,
category="icu",
)
if len(fips) == 5:
population = self.population_data.get_record_for_fips(fips)[CommonFields.POPULATION]
else:
population = self.population_data.get_record_for_state(self.state_abbreviation)[
CommonFields.POPULATION
]
# logging.info(f'Mapping output to WebUI for {self.state}, {fips}')
# pyseir_outputs = load_data.load_ensemble_results(fips)
# if pyseir_outputs is None:
# logging.warning(f'Fit result not found for {fips}: Skipping county')
# return None
policies = [key for key in pyseir_outputs.keys() if key.startswith("suppression_policy")]
if current_hosp is not None:
t_latest_hosp_data, current_hosp = hosp_times[-1], current_hosp[-1]
t_latest_hosp_data_date = t0_simulation + timedelta(days=int(t_latest_hosp_data))
state_hosp_gen = load_data.get_compartment_value_on_date(
fips=fips[:2], compartment="HGen", date=t_latest_hosp_data_date
)
state_hosp_icu = load_data.get_compartment_value_on_date(
fips=fips[:2], compartment="HICU", date=t_latest_hosp_data_date
)
if len(fips) == 5:
# Rescale the county level hospitalizations by the expected
# ratio of county / state hospitalizations from simulations.
# We use ICU data if available too.
county_hosp = load_data.get_compartment_value_on_date(
fips=fips,
compartment="HGen",
date=t_latest_hosp_data_date,
ensemble_results=pyseir_outputs,
)
county_icu = load_data.get_compartment_value_on_date(
fips=fips,
compartment="HICU",
date=t_latest_hosp_data_date,
ensemble_results=pyseir_outputs,
)
current_hosp *= (county_hosp + county_icu) / (state_hosp_gen + state_hosp_icu)
hosp_rescaling_factor = current_hosp / (state_hosp_gen + state_hosp_icu)
# Some states have covidtracking issues. We shouldn't ground ICU cases
# to zero since so far these have all been bad reporting.
if current_icu is not None and current_icu[-1] > 0:
icu_rescaling_factor = current_icu[-1] / state_hosp_icu
else:
icu_rescaling_factor = current_hosp / (state_hosp_gen + state_hosp_icu)
else:
hosp_rescaling_factor = 1.0
icu_rescaling_factor = 1.0
# Iterate through each suppression policy.
# Model output is interpolated to the dates desired for the API.
for i_policy, suppression_policy in enumerate(
[key for key in pyseir_outputs.keys() if key.startswith("suppression_policy")]
):
output_for_policy = pyseir_outputs[suppression_policy]
output_model = pd.DataFrame()
t_list = output_for_policy["t_list"]
t_list_downsampled = range(0, int(max(t_list)), self.output_interval_days)
output_model[schema.DAY_NUM] = t_list_downsampled
output_model[schema.DATE] = [
(t0_simulation + timedelta(days=t)).date().strftime("%m/%d/%y")
for t in t_list_downsampled
]
output_model[schema.TOTAL] = population
output_model[schema.TOTAL_SUSCEPTIBLE] = np.interp(
t_list_downsampled, t_list, output_for_policy["S"]["ci_50"]
)
output_model[schema.EXPOSED] = np.interp(
t_list_downsampled, t_list, output_for_policy["E"]["ci_50"]
)
output_model[schema.INFECTED] = np.interp(
t_list_downsampled,
t_list,
np.add(output_for_policy["I"]["ci_50"], output_for_policy["A"]["ci_50"]),
) # Infected + Asympt.
output_model[schema.INFECTED_A] = output_model[schema.INFECTED]
output_model[schema.INFECTED_B] = hosp_rescaling_factor * np.interp(
t_list_downsampled, t_list, output_for_policy["HGen"]["ci_50"]
) # Hosp General
output_model[schema.INFECTED_C] = icu_rescaling_factor * np.interp(
t_list_downsampled, t_list, output_for_policy["HICU"]["ci_50"]
) # Hosp ICU
# General + ICU beds. don't include vent here because they are also counted in ICU
output_model[schema.ALL_HOSPITALIZED] = np.add(
output_model[schema.INFECTED_B], output_model[schema.INFECTED_C]
)
output_model[schema.ALL_INFECTED] = output_model[schema.INFECTED]
output_model[schema.DEAD] = np.interp(
t_list_downsampled, t_list, output_for_policy["total_deaths"]["ci_50"]
)
final_beds = np.mean(output_for_policy["HGen"]["capacity"])
output_model[schema.BEDS] = final_beds
output_model[schema.CUMULATIVE_INFECTED] = np.interp(
t_list_downsampled,
t_list,
np.cumsum(output_for_policy["total_new_infections"]["ci_50"]),
)
if fit_results:
output_model[schema.Rt] = np.interp(
t_list_downsampled,
t_list,
fit_results["eps"] * fit_results["R0"] * np.ones(len(t_list)),
)
output_model[schema.Rt_ci90] = np.interp(
t_list_downsampled,
t_list,
2 * fit_results["eps_error"] * fit_results["R0"] * np.ones(len(t_list)),
)
else:
output_model[schema.Rt] = 0
output_model[schema.Rt_ci90] = 0
output_model[schema.CURRENT_VENTILATED] = icu_rescaling_factor * np.interp(
t_list_downsampled, t_list, output_for_policy["HVent"]["ci_50"]
)
output_model[schema.POPULATION] = population
# Average capacity.
output_model[schema.ICU_BED_CAPACITY] = np.mean(output_for_policy["HICU"]["capacity"])
output_model[schema.VENTILATOR_CAPACITY] = np.mean(
output_for_policy["HVent"]["capacity"]
)
# Truncate date range of output.
output_dates = pd.to_datetime(output_model["date"])
output_model = output_model[
(output_dates >= datetime(month=3, day=3, year=2020))
& (output_dates < datetime.today() + timedelta(days=90))
]
output_model = output_model.fillna(0)
# Fill in results for the Rt indicator.
try:
rt_results = load_Rt_result(fips)
rt_results.index = rt_results["Rt_MAP_composite"].index.strftime("%m/%d/%y")
merged = output_model.merge(
rt_results[["Rt_MAP_composite", "Rt_ci95_composite"]],
right_index=True,
left_on="date",
how="left",
)
output_model[schema.RT_INDICATOR] = merged["Rt_MAP_composite"]
# With 90% probability the value is between rt_indicator - ci90 to rt_indicator + ci90
output_model[schema.RT_INDICATOR_CI90] = (
merged["Rt_ci95_composite"] - merged["Rt_MAP_composite"]
)
except (ValueError, KeyError) as e:
output_model[schema.RT_INDICATOR] = "NaN"
output_model[schema.RT_INDICATOR_CI90] = "NaN"
output_model[[schema.RT_INDICATOR, schema.RT_INDICATOR_CI90]] = output_model[
[schema.RT_INDICATOR, schema.RT_INDICATOR_CI90]
].fillna("NaN")
# Truncate floats and cast as strings to match data model.
int_columns = [
col
for col in output_model.columns
if col
not in (
schema.DATE,
schema.Rt,
schema.Rt_ci90,
schema.RT_INDICATOR,
schema.RT_INDICATOR_CI90,
)
]
output_model.loc[:, int_columns] = (
output_model[int_columns].fillna(0).astype(int).astype(str)
)
output_model.loc[
:, [schema.Rt, schema.Rt_ci90, schema.RT_INDICATOR, schema.RT_INDICATOR_CI90]
] = (
output_model[
[schema.Rt, schema.Rt_ci90, schema.RT_INDICATOR, schema.RT_INDICATOR_CI90]
]
.fillna(0)
.round(decimals=4)
.astype(str)
)
# Convert the records format to just list(list(values))
output_model = [
[val for val in timestep.values()]
for timestep in output_model.to_dict(orient="records")
]
output_path = get_run_artifact_path(
fips, RunArtifact.WEB_UI_RESULT, output_dir=self.output_dir
)
policy_enum = Intervention.from_webui_data_adaptor(suppression_policy)
output_path = output_path.replace("__INTERVENTION_IDX__", str(policy_enum.value))
with open(output_path, "w") as f:
json.dump(output_model, f)
def generate_surge_spreadsheet(self):
"""
Produce a spreadsheet summarizing peaks.
Parameters
----------
state: str
State to generate sheet for.
Returns
-------
"""
df = load_data.load_county_metadata()
all_fips = load_data.get_all_fips_codes_for_a_state(self.state)
all_data = {
fips: load_data.load_ensemble_results(fips)
for fips in all_fips
}
df = df.set_index("fips")
records = []
for fips, ensembles in all_data.items():
county_name = df.loc[fips]["county"]
t0 = fit_results.load_t0(fips)
for suppression_policy, ensemble in ensembles.items():
county_record = dict(
county_name=county_name,
county_fips=fips,
mitigation_policy=policy_to_mitigation(suppression_policy),
)
for compartment in [
"HGen",
"general_admissions_per_day",
"HICU",
"icu_admissions_per_day",
"total_new_infections",
"direct_deaths_per_day",
"total_deaths",
"D",
]:
compartment_name = compartment_to_name_map[compartment]
county_record[compartment_name + " Peak Value Mean"] = (
"%.0f" % ensemble[compartment]["peak_value_mean"])
county_record[compartment_name + " Peak Value Median"] = (
"%.0f" % ensemble[compartment]["peak_value_ci50"])
county_record[compartment_name + " Peak Value CI25"] = (
"%.0f" % ensemble[compartment]["peak_value_ci25"])
county_record[compartment_name + " Peak Value CI75"] = (
"%.0f" % ensemble[compartment]["peak_value_ci75"])
county_record[compartment_name + " Peak Time Median"] = ((
t0 +
timedelta(days=ensemble[compartment]["peak_time_ci50"])
).date().isoformat())
# Leaving for now...
# if 'surge_start' in ensemble[compartment]:
# if not np.isnan(np.nanmean(ensemble[compartment]['surge_start'])):
# county_record[compartment_name + ' Surge Start Mean'] = (t0 + timedelta(days=np.nanmean(ensemble[compartment]['surge_start']))).date().isoformat()
# county_record[compartment_name + ' Surge End Mean'] = (t0 + timedelta(days=np.nanmean(ensemble[compartment]['surge_end']))).date().isoformat()
records.append(county_record)
df = pd.DataFrame(records)
df.write_json(self.surge_filename)
def map_fips(self, fips):
"""
For a given county fips code, generate the CAN UI output format.
Parameters
----------
fips: str
County FIPS code to map.
"""
if len(fips) == 5:
population = self.population_data.get_county_level(
'USA', state=self.state_abbreviation, fips=fips)
else:
population = self.population_data.get_state_level(
'USA', state=self.state_abbreviation)
logging.info(f'Mapping output to WebUI for {self.state}, {fips}')
pyseir_outputs = load_data.load_ensemble_results(fips)
policies = [
key for key in pyseir_outputs.keys()
if key.startswith('suppression_policy')
]
all_hospitalized_today = None
try:
fit_results = load_inference_result(fips)
except ValueError:
fit_results = None
logging.error(
f'Fit result not found for {fips}: Skipping inference elements'
)
for i_policy, suppression_policy in enumerate(policies):
if suppression_policy == 'suppression_policy__full_containment': # No longer shipping this.
continue
output_for_policy = pyseir_outputs[suppression_policy]
output_model = pd.DataFrame()
if suppression_policy == 'suppression_policy__inferred' and fit_results:
if len(fips) == 5 and fips not in self.df_whitelist.fips:
continue
t0 = datetime.fromisoformat(fit_results['t0_date'])
# Hospitalizations need to be rescaled by the inferred factor to match observations for display.
now_idx = int(
(datetime.today() -
datetime.fromisoformat(fit_results['t0_date'])).days)
total_hosps = output_for_policy['HGen']['ci_50'][
now_idx] + output_for_policy['HICU']['ci_50'][now_idx]
hosp_fraction = all_hospitalized_today / total_hosps
else:
t0 = datetime.today()
hosp_fraction = 1.
t_list = output_for_policy['t_list']
t_list_downsampled = range(0, int(max(t_list)),
self.output_interval_days)
# Col 0
output_model['days'] = t_list_downsampled
# Col 1
output_model['date'] = [
(t0 + timedelta(days=t)).date().strftime('%m/%d/%y')
for t in t_list_downsampled
]
# Col 2
output_model['t'] = population
# Col 3
output_model['b'] = np.interp(t_list_downsampled, t_list,
output_for_policy['S']['ci_50'])
# Col 4
output_model['c'] = np.interp(t_list_downsampled, t_list,
output_for_policy['E']['ci_50'])
# Col 5
output_model['d'] = np.interp(
t_list_downsampled, t_list,
np.add(output_for_policy['I']['ci_50'],
output_for_policy['A']['ci_50'])) # Infected + Asympt.
# Col 6
output_model['e'] = output_model['d']
# Col 7
output_model['f'] = np.interp(
t_list_downsampled, t_list,
output_for_policy['HGen']['ci_50']) # Hosp General
# Col 8
output_model['g'] = np.interp(
t_list_downsampled, t_list,
output_for_policy['HICU']['ci_50']) # Hosp ICU
# Col 9
output_model['all_hospitalized'] = hosp_fraction * np.add(
output_model['f'], output_model['g'])
# Col 10
output_model['all_infected'] = output_model['d']
# Col 11
output_model['dead'] = np.interp(
t_list_downsampled, t_list,
output_for_policy['total_deaths']['ci_50'])
# Col 12
final_beds = np.mean(
output_for_policy['HGen']['capacity']) + np.mean(
output_for_policy['HICU']['capacity'])
output_model['beds'] = final_beds
output_model['cumulative_infected'] = np.interp(
t_list_downsampled, t_list,
np.cumsum(output_for_policy['total_new_infections']['ci_50']))
if fit_results:
output_model['R_t'] = np.interp(
t_list_downsampled, t_list, fit_results['eps'] *
fit_results['R0'] * np.ones(len(t_list)))
output_model['R_t_stdev'] = np.interp(
t_list_downsampled, t_list, fit_results['eps_error'] *
fit_results['R0'] * np.ones(len(t_list)))
else:
output_model['R_t'] = 0
output_model['R_t_stdev'] = 0
# Record the current number of hospitalizations in order to rescale the inference results.
all_hospitalized_today = output_model['all_hospitalized'][0]
# Don't backfill inferences
if suppression_policy != 'suppression_policy__inferred':
backfill = self.backfill_output_model_fips(
fips, t0, final_beds, output_model)
output_model = pd.concat([
backfill, output_model
])[output_model.columns].reset_index(drop=True)
# Truncate date range of output.
output_dates = pd.to_datetime(output_model['date'])
output_model = output_model[
(output_dates > datetime(month=3, day=3, year=2020))
& (output_dates < datetime.today() + timedelta(days=90))]
output_model = output_model.fillna(0)
for col in ['l']:
output_model[col] = 0
output_model['population'] = population
for col in ['m', 'n']:
output_model[col] = 0
# Truncate floats and cast as strings to match data model.
int_columns = [
col for col in output_model.columns
if col not in ('date', 'R_t', 'R_t_stdev')
]
output_model.loc[:,
int_columns] = output_model[int_columns].fillna(
0).astype(int).astype(str)
output_model.loc[:, ['R_t', 'R_t_stdev']] = output_model[[
'R_t', 'R_t_stdev'
]].fillna(0).round(decimals=4).astype(str)
# Convert the records format to just list(list(values))
output_model = [[
val for val in timestep.values()
] for timestep in output_model.to_dict(orient='records')]
output_path = get_run_artifact_path(fips,
RunArtifact.WEB_UI_RESULT,
output_dir=self.output_dir)
policy_enum = Intervention.from_webui_data_adaptor(
suppression_policy)
output_path = output_path.replace('__INTERVENTION_IDX__',
str(policy_enum.value))
with open(output_path, 'w') as f:
json.dump(output_model, f)
def map_fips(self, fips: str) -> None:
"""
For a given fips code, for either a county or state, generate the CAN UI output format.
Parameters
----------
fips: str
FIPS code to map.
"""
log.info("Mapping output to WebUI.", state=self.state, fips=fips)
shim_log = structlog.getLogger(fips=fips)
pyseir_outputs = load_data.load_ensemble_results(fips)
try:
fit_results = load_inference_result(fips)
t0_simulation = datetime.fromisoformat(fit_results["t0_date"])
except (KeyError, ValueError):
log.error("Fit result not found for fips. Skipping...", fips=fips)
return
population = self._get_population(fips)
# We will shim all suppression policies by the same amount (since historical tracking error
# for all policies is the same).
baseline_policy = "suppression_policy__inferred" # This could be any valid policy
# We need the index in the model's temporal frame.
idx_offset = int(fit_results["t_today"] - fit_results["t0"])
# Get the latest observed values to use in calculating shims
observed_latest_dict = combined_datasets.get_us_latest_for_fips(fips)
observed_death_latest = observed_latest_dict[CommonFields.DEATHS]
observed_total_hosps_latest = observed_latest_dict[
CommonFields.CURRENT_HOSPITALIZED]
observed_icu_latest = observed_latest_dict[CommonFields.CURRENT_ICU]
# For Deaths
model_death_latest = pyseir_outputs[baseline_policy]["total_deaths"][
"ci_50"][idx_offset]
model_acute_latest = pyseir_outputs[baseline_policy]["HGen"]["ci_50"][
idx_offset]
model_icu_latest = pyseir_outputs[baseline_policy]["HICU"]["ci_50"][
idx_offset]
model_total_hosps_latest = model_acute_latest + model_icu_latest
death_shim = shim.calculate_strict_shim(
model=model_death_latest,
observed=observed_death_latest,
log=shim_log.bind(type=CommonFields.DEATHS),
)
total_hosp_shim = shim.calculate_strict_shim(
model=model_total_hosps_latest,
observed=observed_total_hosps_latest,
log=shim_log.bind(type=CommonFields.CURRENT_HOSPITALIZED),
)
# For ICU This one is a little more interesting since we often don't have ICU. In this case
# we use information from the same aggregation level (intralevel) to keep the ratios
# between general hospitalization and icu hospitalization
icu_shim = shim.calculate_intralevel_icu_shim(
model_acute=model_acute_latest,
model_icu=model_icu_latest,
observed_icu=observed_icu_latest,
observed_total_hosps=observed_total_hosps_latest,
log=shim_log.bind(type=CommonFields.CURRENT_ICU),
)
# Iterate through each suppression policy.
# Model output is interpolated to the dates desired for the API.
suppression_policies = [
key for key in pyseir_outputs.keys()
if key.startswith("suppression_policy")
]
for suppression_policy in suppression_policies:
output_for_policy = pyseir_outputs[suppression_policy]
output_model = pd.DataFrame()
t_list = output_for_policy["t_list"]
t_list_downsampled = range(0, int(max(t_list)),
self.output_interval_days)
output_model[schema.DAY_NUM] = t_list_downsampled
output_model[schema.DATE] = [
(t0_simulation + timedelta(days=t)).date().strftime("%Y-%m-%d")
for t in t_list_downsampled
]
output_model[schema.TOTAL] = population
output_model[schema.TOTAL_SUSCEPTIBLE] = np.interp(
t_list_downsampled, t_list, output_for_policy["S"]["ci_50"])
output_model[schema.EXPOSED] = np.interp(
t_list_downsampled, t_list, output_for_policy["E"]["ci_50"])
output_model[schema.INFECTED] = np.interp(
t_list_downsampled,
t_list,
np.add(output_for_policy["I"]["ci_50"],
output_for_policy["A"]["ci_50"]),
) # Infected + Asympt.
output_model[schema.INFECTED_A] = output_model[schema.INFECTED]
interpolated_model_acute_values = np.interp(
t_list_downsampled, t_list, output_for_policy["HGen"]["ci_50"])
output_model[schema.INFECTED_B] = interpolated_model_acute_values
raw_model_icu_values = output_for_policy["HICU"]["ci_50"]
interpolated_model_icu_values = np.interp(t_list_downsampled,
t_list,
raw_model_icu_values)
output_model[schema.INFECTED_C] = (
icu_shim + interpolated_model_icu_values).clip(min=0)
# General + ICU beds. don't include vent here because they are also counted in ICU
output_model[schema.ALL_HOSPITALIZED] = (
interpolated_model_acute_values +
interpolated_model_icu_values + total_hosp_shim).clip(min=0)
output_model[schema.ALL_INFECTED] = output_model[schema.INFECTED]
# Shim Deaths to Match Observed
raw_model_deaths_values = output_for_policy["total_deaths"][
"ci_50"]
interp_model_deaths_values = np.interp(t_list_downsampled, t_list,
raw_model_deaths_values)
output_model[schema.DEAD] = (interp_model_deaths_values +
death_shim).clip(min=0)
# Continue mapping
final_beds = np.mean(output_for_policy["HGen"]["capacity"])
output_model[schema.BEDS] = final_beds
output_model[schema.CUMULATIVE_INFECTED] = np.interp(
t_list_downsampled,
t_list,
np.cumsum(output_for_policy["total_new_infections"]["ci_50"]),
)
if fit_results:
output_model[schema.Rt] = np.interp(
t_list_downsampled,
t_list,
fit_results["eps2"] * fit_results["R0"] *
np.ones(len(t_list)),
)
output_model[schema.Rt_ci90] = np.interp(
t_list_downsampled,
t_list,
2 * fit_results["eps2_error"] * fit_results["R0"] *
np.ones(len(t_list)),
)
else:
output_model[schema.Rt] = 0
output_model[schema.Rt_ci90] = 0
output_model[schema.CURRENT_VENTILATED] = (
icu_shim +
np.interp(t_list_downsampled, t_list,
output_for_policy["HVent"]["ci_50"])).clip(min=0)
output_model[schema.POPULATION] = population
# Average capacity.
output_model[schema.ICU_BED_CAPACITY] = np.mean(
output_for_policy["HICU"]["capacity"])
output_model[schema.VENTILATOR_CAPACITY] = np.mean(
output_for_policy["HVent"]["capacity"])
# Truncate date range of output.
output_dates = pd.to_datetime(output_model["date"])
output_model = output_model[
(output_dates >= datetime(month=3, day=3, year=2020))
& (output_dates < datetime.today() + timedelta(days=90))]
output_model = output_model.fillna(0)
# Fill in results for the Rt indicator.
rt_results = load_Rt_result(fips)
if rt_results is not None:
rt_results.index = rt_results[
"Rt_MAP_composite"].index.strftime("%Y-%m-%d")
merged = output_model.merge(
rt_results[["Rt_MAP_composite", "Rt_ci95_composite"]],
right_index=True,
left_on="date",
how="left",
)
output_model[schema.RT_INDICATOR] = merged["Rt_MAP_composite"]
# With 90% probability the value is between rt_indicator - ci90
# to rt_indicator + ci90
output_model[schema.RT_INDICATOR_CI90] = (
merged["Rt_ci95_composite"] - merged["Rt_MAP_composite"])
else:
log.warning(
"No Rt Results found, clearing Rt in output.",
fips=fips,
suppression_policy=suppression_policy,
)
output_model[schema.RT_INDICATOR] = "NaN"
output_model[schema.RT_INDICATOR_CI90] = "NaN"
output_model[[schema.RT_INDICATOR,
schema.RT_INDICATOR_CI90]] = output_model[[
schema.RT_INDICATOR, schema.RT_INDICATOR_CI90
]].fillna("NaN")
int_columns = [
col for col in output_model.columns if col not in (
schema.DATE,
schema.Rt,
schema.Rt_ci90,
schema.RT_INDICATOR,
schema.RT_INDICATOR_CI90,
schema.FIPS,
)
]
output_model.loc[:,
int_columns] = output_model[int_columns].fillna(
0).astype(int)
output_model.loc[:, [
schema.Rt, schema.Rt_ci90, schema.RT_INDICATOR, schema.
RT_INDICATOR_CI90
]] = output_model[[
schema.Rt, schema.Rt_ci90, schema.RT_INDICATOR,
schema.RT_INDICATOR_CI90
]].fillna(0)
output_model[schema.FIPS] = fips
intervention = Intervention.from_webui_data_adaptor(
suppression_policy)
output_model[schema.INTERVENTION] = intervention.value
output_path = get_run_artifact_path(fips,
RunArtifact.WEB_UI_RESULT,
output_dir=self.output_dir)
output_path = output_path.replace("__INTERVENTION_IDX__",
str(intervention.value))
output_model.to_json(output_path, orient=OUTPUT_JSON_ORIENT)
def map_fips(self, fips: str) -> None:
"""
For a given fips code, for either a county or state, generate the CAN UI output format.
Parameters
----------
fips: str
FIPS code to map.
"""
log.info("Mapping output to WebUI.", state=self.state, fips=fips)
pyseir_outputs = load_data.load_ensemble_results(fips)
try:
fit_results = load_inference_result(fips)
t0_simulation = datetime.fromisoformat(fit_results["t0_date"])
except (KeyError, ValueError):
log.error("Fit result not found for fips. Skipping...", fips=fips)
return
population = self._get_population(fips)
# Get multiplicative conversion factors to scale model output to fit dataset current values
hosp_rescaling_factor, icu_rescaling_factor = self._get_model_to_dataset_conversion_factors(
t0_simulation=t0_simulation,
fips=fips,
pyseir_outputs=pyseir_outputs,
)
# Iterate through each suppression policy.
# Model output is interpolated to the dates desired for the API.
suppression_policies = [
key for key in pyseir_outputs.keys()
if key.startswith("suppression_policy")
]
for suppression_policy in suppression_policies:
output_for_policy = pyseir_outputs[suppression_policy]
output_model = pd.DataFrame()
t_list = output_for_policy["t_list"]
t_list_downsampled = range(0, int(max(t_list)),
self.output_interval_days)
output_model[schema.DAY_NUM] = t_list_downsampled
output_model[schema.DATE] = [
(t0_simulation + timedelta(days=t)).date().strftime("%m/%d/%y")
for t in t_list_downsampled
]
output_model[schema.TOTAL] = population
output_model[schema.TOTAL_SUSCEPTIBLE] = np.interp(
t_list_downsampled, t_list, output_for_policy["S"]["ci_50"])
output_model[schema.EXPOSED] = np.interp(
t_list_downsampled, t_list, output_for_policy["E"]["ci_50"])
output_model[schema.INFECTED] = np.interp(
t_list_downsampled,
t_list,
np.add(output_for_policy["I"]["ci_50"],
output_for_policy["A"]["ci_50"]),
) # Infected + Asympt.
output_model[schema.INFECTED_A] = output_model[schema.INFECTED]
output_model[
schema.INFECTED_B] = hosp_rescaling_factor * np.interp(
t_list_downsampled, t_list,
output_for_policy["HGen"]["ci_50"]) # Hosp General
raw_model_icu_values = output_for_policy["HICU"]["ci_50"]
interpolated_model_icu_values = np.interp(t_list_downsampled,
t_list,
raw_model_icu_values)
final_derived_model_value = icu_rescaling_factor * interpolated_model_icu_values
output_model[schema.INFECTED_C] = final_derived_model_value
# General + ICU beds. don't include vent here because they are also counted in ICU
output_model[schema.ALL_HOSPITALIZED] = np.add(
output_model[schema.INFECTED_B],
output_model[schema.INFECTED_C])
output_model[schema.ALL_INFECTED] = output_model[schema.INFECTED]
output_model[schema.DEAD] = np.interp(
t_list_downsampled, t_list,
output_for_policy["total_deaths"]["ci_50"])
final_beds = np.mean(output_for_policy["HGen"]["capacity"])
output_model[schema.BEDS] = final_beds
output_model[schema.CUMULATIVE_INFECTED] = np.interp(
t_list_downsampled,
t_list,
np.cumsum(output_for_policy["total_new_infections"]["ci_50"]),
)
if fit_results:
output_model[schema.Rt] = np.interp(
t_list_downsampled,
t_list,
fit_results["eps"] * fit_results["R0"] *
np.ones(len(t_list)),
)
output_model[schema.Rt_ci90] = np.interp(
t_list_downsampled,
t_list,
2 * fit_results["eps_error"] * fit_results["R0"] *
np.ones(len(t_list)),
)
else:
output_model[schema.Rt] = 0
output_model[schema.Rt_ci90] = 0
output_model[
schema.CURRENT_VENTILATED] = icu_rescaling_factor * np.interp(
t_list_downsampled, t_list,
output_for_policy["HVent"]["ci_50"])
output_model[schema.POPULATION] = population
# Average capacity.
output_model[schema.ICU_BED_CAPACITY] = np.mean(
output_for_policy["HICU"]["capacity"])
output_model[schema.VENTILATOR_CAPACITY] = np.mean(
output_for_policy["HVent"]["capacity"])
# Truncate date range of output.
output_dates = pd.to_datetime(output_model["date"])
output_model = output_model[
(output_dates >= datetime(month=3, day=3, year=2020))
& (output_dates < datetime.today() + timedelta(days=90))]
output_model = output_model.fillna(0)
# Fill in results for the Rt indicator.
try:
rt_results = load_Rt_result(fips)
rt_results.index = rt_results[
"Rt_MAP_composite"].index.strftime("%m/%d/%y")
merged = output_model.merge(
rt_results[["Rt_MAP_composite", "Rt_ci95_composite"]],
right_index=True,
left_on="date",
how="left",
)
output_model[schema.RT_INDICATOR] = merged["Rt_MAP_composite"]
# With 90% probability the value is between rt_indicator - ci90
# to rt_indicator + ci90
output_model[schema.RT_INDICATOR_CI90] = (
merged["Rt_ci95_composite"] - merged["Rt_MAP_composite"])
except (ValueError, KeyError) as e:
log.warning("Clearing Rt in output for fips.",
fips=fips,
exc_info=e)
output_model[schema.RT_INDICATOR] = "NaN"
output_model[schema.RT_INDICATOR_CI90] = "NaN"
output_model[[schema.RT_INDICATOR,
schema.RT_INDICATOR_CI90]] = output_model[[
schema.RT_INDICATOR, schema.RT_INDICATOR_CI90
]].fillna("NaN")
# Truncate floats and cast as strings to match data model.
int_columns = [
col for col in output_model.columns if col not in (
schema.DATE,
schema.Rt,
schema.Rt_ci90,
schema.RT_INDICATOR,
schema.RT_INDICATOR_CI90,
)
]
output_model.loc[:, int_columns] = (
output_model[int_columns].fillna(0).astype(int).astype(str))
output_model.loc[:, [
schema.Rt, schema.Rt_ci90, schema.RT_INDICATOR, schema.
RT_INDICATOR_CI90
]] = (output_model[[
schema.Rt, schema.Rt_ci90, schema.RT_INDICATOR,
schema.RT_INDICATOR_CI90
]].fillna(0).round(decimals=4).astype(str))
# Convert the records format to just list(list(values))
output_model = [[
val for val in timestep.values()
] for timestep in output_model.to_dict(orient="records")]
output_path = get_run_artifact_path(fips,
RunArtifact.WEB_UI_RESULT,
output_dir=self.output_dir)
policy_enum = Intervention.from_webui_data_adaptor(
suppression_policy)
output_path = output_path.replace("__INTERVENTION_IDX__",
str(policy_enum.value))
with open(output_path, "w") as f:
json.dump(output_model, f)
