def iterate_simulation(current_state, seir_parameters, phase, initial):
"""
Iterate over simulation phase, returning evolution and new current state.
"""
res = entrypoint(current_state, seir_parameters, phase, initial)
current_state = (res.drop("scenario", axis=1).iloc[-1].to_dict()
) # new initial = last date
return res, current_state
def run_simulation(user_input, config):
"""
Run simulation phases and return hospital demand.
Parameters
----------
user_input["population_params"]: str
Explicit population parameters:
- N: population
- I: infected
- R: recovered
- D: deaths
user_input["strategy"]: dict
Simulation parameters for each phase (from user input):
- scenario: date
Return
------
pd.Dataframe
States evolution over phases
"""
dfs = {"worst": np.nan, "best": np.nan}
# Run worst scenario
for bound in dfs.keys():
phase = {"scenario": "projection_current_rt", "n_days": 90}
# Get Rts
if not user_input["state_id"] and not user_input["city_id"]:
phase["R0"] = 3
else:
phase["R0"] = get_rt(user_input["place_type"], user_input, config,
bound)
res = entrypoint(
user_input["population_params"],
config["br"]["seir_parameters"],
phase=phase,
initial=True,
)
res = res.reset_index(drop=True)
res.index += 1
res.index.name = "dias"
dfs[bound] = res
return dfs
