def forecast_each_state(
country,
state,
timeseries,
beds_data,
population_data,
min_date,
max_date,
output_dir,
):
_logger.info(f"Generating data for state: {state}")
cases = timeseries.get_data(state=state)
try:
beds = beds_data.get_beds_by_country_state(country, state)
except IndexError:
# Old timeseries data throws an exception if the state does not exist in
# the dataset.
_logger.error(f"Failed to get beds data for {state}")
return
population = population_data.get_state_level(country, state)
if not population:
_logger.warning(f"Missing population for {state}")
return
for i, intervention in enumerate(get_interventions()):
_logger.info(f"Running intervention {i} for {state}")
results = model_state(cases, beds, population, intervention)
website_data = prepare_data_for_website(results,
cases,
population,
min_date,
max_date,
interval=4)
write_results(website_data, output_dir, f"{state}.{i}.json")
def forecast_each_county(
min_date,
max_date,
country,
state,
county,
fips,
timeseries,
beds_data,
population_data,
output_dir,
):
_logger.info(f"Running model for county: {county}, {state} - {fips}")
cases = timeseries.get_data(state=state, country=country, fips=fips)
beds = beds_data.get_county_level(state, fips=fips)
population = population_data.get_county_level(country, state, fips=fips)
total_cases = sum(cases.cases)
if not population or not beds or not total_cases:
_logger.debug(
f"Missing data, skipping: Beds: {beds} Pop: {population} Total Cases: {total_cases}"
)
return
_logger.info(
f"Running interventions for {county}, {state}: {fips} - "
f"total cases: {total_cases} beds: {beds} pop: {population}"
)
for i, intervention in enumerate(get_interventions()):
results = model_state(cases, beds, population, intervention)
website_data = prepare_data_for_website(
results, cases, population, min_date, max_date, interval=4
)
write_results(website_data, output_dir, f"{state}.{fips}.{i}.json")
def forecast_each_state(
country,
state,
timeseries,
beds_data,
population_data,
min_date,
max_date,
output_dir,
):
_logger.info(f"Generating data for state: {state}")
cases = timeseries.get_data(state=state)
beds = beds_data.get_state_level(state)
if not beds:
_logger.error(f"Failed to get beds data for {state}")
return
population = population_data.get_state_level(country, state)
if not population:
_logger.warning(f"Missing population for {state}")
return
for i, intervention in enumerate(build_params.get_interventions()):
_logger.info(f"Running intervention {i} for {state}")
results = model_state(cases, beds, population, intervention)
website_data = prepare_data_for_website(results,
cases,
population,
min_date,
max_date,
interval=4)
write_results(website_data, output_dir, f"{state}.{i}.json")
def validate_results(result_dir: str) -> None:
"""
For each state, check that we have a file for each intervention,
and that the file is non-empty
"""
per_state_expected = len(build_params.get_interventions())
missing_or_empty = []
for state in build_params.US_STATE_ABBREV.values():
if state in UNSUPPORTED_REGIONS:
continue
for i in range(per_state_expected):
fname = os.path.join(result_dir, ".".join([state, str(i), "json"]))
try:
result = os.stat(fname)
if result.st_size == 0:
missing_or_empty.append(fname)
except FileNotFoundError:
missing_or_empty.append(fname)
if len(missing_or_empty) > 0:
raise RuntimeError(
f'Missing or empty expected files: {", ".join(missing_or_empty)}')
# Assumes that anyone who needs ICU care and doesn't get it dies
'case_fatality_rate_hospitals_overwhelmed':
HOSPITALIZATION_RATE * HOSPITALIZED_CASES_REQUIRING_ICU_CARE,
'hospital_capacity_change_daily_rate':
1.05,
'max_hospital_capacity_factor':
2.07,
'initial_hospital_bed_utilization':
.6,
'model_interval':
4, # In days
'total_infected_period':
12, # In days
'rolling_intervals_for_current_infected':
int(round(TOTAL_INFECTED_PERIOD / MODEL_INTERVAL, 0)),
}
return CovidTimeseriesModel().forecast(model_parameters=MODEL_PARAMETERS)
if __name__ == '__main__':
logging.basicConfig(level=logging.INFO)
dataset = CDSDataset()
interventions = get_interventions()
for state in dataset.get_all_states_by_country('USA'):
for i, intervention in enumerate(interventions):
_logger.info(f"Running intervention {i} for {state}")
record_results(
model_state(dataset, 'USA', state, intervention),
OUTPUT_DIR, state, i,
dataset.get_population_by_country_state('USA', state))
