else:
# get dates for previous days predictions/forecast
DATES = DATES_O[0:j]
obs_y_trunc = obs_y[0:j]
#if len(obs_y_trunc) == 0 or max(obs_y_trunc) == 1:
# continue
# declare x as a list of integers from 0 to len(y)
x = list(range(len(obs_y_trunc)))
iterations = 50000
SEIR_fit = 0
obs_pred_r2, obs_x, pred_y, forecasted_x, forecasted_y, params = fxns.fit_curve(x, obs_y_trunc,
model, ForecastDays, PopSize, ArrivalDate, j, iterations, SEIR_fit)
print(obs_pred_r2)
if obs_pred_r2 < 0:
obs_pred_r2 = 0.0
# convert any y-values (observed, predicted, or forecasted)
# that are less than 0 (nonsensical values) to 0.
obs_y_trunc[obs_y_trunc < 0] = 0
pred_y = np.array(pred_y)
pred_y[pred_y < 0] = 0
forecasted_y = np.array(forecasted_y)
forecasted_y[forecasted_y < 0] = 0
def _get_fit(self, focal_loc, ForecastDays, StatePops, model,
seir_fits_df):
PopSize = StatePops[StatePops['Province/State'] ==
focal_loc]['PopSize'].tolist()
PopSize = PopSize[0]
ArrivalDate = StatePops[StatePops['Province/State'] == focal_loc][
'Date_of_first_reported_infection'].tolist()
ArrivalDate = ArrivalDate[0]
SEIR_Fit = seir_fits_df[seir_fits_df['focal_loc'] == focal_loc]
# A function to generate all figures and tables
# variables:
# obs_x: observed x values
# obs_y: observed y values
# model: the model to fit
# T0: likely date of first infection
# ForecastDays: number of days ahead to extend predictions
# N: population size of interest
# ArrivalDate: likely data of first infection (used by SEIR-SD model)
# incubation_period: disease-specific epidemilogical parameter
# average number of days until an exposed person becomes
# begins to exhibit symptoms of infection
# infectious_period: disease-specific epidemilogical parameter
# average number of days an infected person is infected
# rho: disease-specific epidemilogical parameter
# aka basic reproductive number
# average number of secondary infections produced by a typical case
# of an infection in a population where everyone is susceptible
# socdist: population-specific social-distancing parameter
# declare the following as global variables so their changes can be
# seen/used by outside functions
# add 1 to number of forecast days for indexing purposes
ForecastDays = int(ForecastDays + 1)
# filter main dataframe to include only the chosen location
df_sub = self._df[self._df['Province/State'] == focal_loc]
# get column labels, will filter below to extract dates
yi = list(df_sub)
obs_y_trunc = []
fore_clrs = [
'purple', 'mediumorchid', 'plum', 'blue', 'deepskyblue',
'darkturquoise', 'green', 'limegreen', 'gold', 'orange', 'red'
]
pred_clrs = [
'0.0', '0.1', '0.2', '0.25', '0.3', '0.35', '0.4', '0.5', '0.6',
'0.7', '0.8'
]
for i, j in enumerate(list(range(-10, 1))):
pred_clr = pred_clrs[i]
fore_clr = fore_clrs[i]
if j == 0:
# get dates for today's predictions/forecast
DATES = yi[4:]
obs_y_trunc = df_sub.iloc[0, 4:].values
else:
# get dates for previous days predictions/forecast
DATES = yi[4:j]
obs_y_trunc = df_sub.iloc[0, 4:j].values
ii = 0
while obs_y_trunc[ii] == 0:
ii += 1
y = obs_y_trunc[ii:]
dates = DATES[ii:]
# declare x as a list of integers from 0 to len(y)
x = list(range(len(y)))
# Call function to use chosen model to obtain:
# r-square for observed vs. predicted
# predicted y-values
# forecasted x and y values
iterations = 2
obs_pred_r2, obs_x, pred_y, forecasted_x, forecasted_y, params = fxns.fit_curve(
x, y, model, ForecastDays, PopSize, ArrivalDate, j, iterations,
SEIR_Fit)
# convert y values to numpy array
y = np.array(y)
# because it isn't based on a best fit line,
# and the y-intercept is forced through [0,0]
# a model can perform so poorly that the
# observed vs predicted r-square is negative (a nonsensical value)
# if this happens, report the r-square as 0.0
if obs_pred_r2 < 0:
obs_pred_r2 = 0.0
# convert any y-values (observed, predicted, or forecasted)
# that are less than 0 (nonsensical values) to 0.
y[y < 0] = 0
pred_y = np.array(pred_y)
pred_y[pred_y < 0] = 0
forecasted_y = np.array(forecasted_y)
forecasted_y[forecasted_y < 0] = 0
# number of from ArrivalDate to end of forecast window
#numdays = len(forecasted_x)
latest_date = pd.to_datetime(dates[-1])
first_date = pd.to_datetime(dates[0])
# get the date of the last day in the forecast window
future_date = latest_date + datetime.timedelta(days=ForecastDays -
1)
# get all dates from ArrivalDate to the last day in the forecast window
fdates = pd.date_range(start=first_date, end=future_date)
fdates = fdates.strftime('%m/%d')
# designature plot label for legend
if j == 0:
label = 'Current forecast'
else:
label = str(-j) + ' day old forecast'
if label == 'Current forecast':
for i, val in enumerate(forecasted_y):
if i > 0:
if forecasted_y[i] - forecasted_y[i - 1] > 0:
self.new_cases.append(forecasted_y[i] -
forecasted_y[i - 1])
else:
self.new_cases.append(0)
if i == 0:
self.new_cases.append(forecasted_y[i])
# get dates from ArrivalDate to the current day
dates = pd.date_range(start=first_date, end=latest_date)
dates = dates.strftime('%m/%d')
output_list = [
y, pred_y, forecasted_y, dates, fdates, label, obs_pred_r2,
model, focal_loc, PopSize, ArrivalDate, pred_clr, fore_clr
]
self._model_fits_df.loc[len(self._model_fits_df)] = output_list