def generate_zero_columns(data, config):
# generate 3 additional data column (0 is first column index_
# 4 hospitalized cumulative,
# 5 ICU
# 6
days = data[:, I_TIME]
tests = data[:, I_INF]
dead = data[:, I_DEAD]
#n = np.ndarray.size(days)
delay_hos = get_mean(config['delayHOS'])
delay_hos2 = delay_hos + 1 / get_mean(config['gamma'])
delay_hosd = get_mean(config['delayHOSD'])
icufrac = read_icufrac_data(config, days, 0)
icudfrac = get_mean(config['icudfrac'])
dfrac = get_mean(config['dfrac'])
delay_icud = get_mean(config['delayICUD'])
hoscum = days * 0.0
hoscumcreate = 'none'
try:
hoscumcreate = config['hoscumcreate']
except:
pass
if (hoscumcreate == 'dead'):
hoscum1 = (1 - icufrac) * gauss_smooth_shift(
dead, delay_hosd, 0, 1.0 / (dfrac - icufrac * icudfrac))
hoscum2 = (icufrac) * gauss_smooth_shift(dead, delay_icud, 0, 1.0 /
(icudfrac))
hoscum = hoscum1 + hoscum2
elif (hoscumcreate == 'confirmed'):
try:
rate_hoscumfromconfirmed = config['rate_hoscumfromconfirmed']
except:
pass
hoscum = gauss_smooth_shift(tests, delay_hos2, 0,
rate_hoscumfromconfirmed)
icu = days * 0.0
hosact = days * 0.0
datanew = np.concatenate(
(data[:, 0:4], hoscum[:, None], icu[:, None], hosact[:, None]),
axis=-1)
datanew = generate_hos_actual(datanew, config)
return datanew
def generate_hos_actual(data, config):
# generate an additional data column for actual hospitalized
# only do this when the mean of the actuals are zero
delay_rec = get_mean(config['delayREC'])
delay_hos = get_mean(config['delayHOS'])
delay_hosrec = get_mean(config['delayHOSREC'])
delay_hosd = get_mean(config['delayHOSD'])
delay_icu = get_mean(config['delayICUCAND'])
delay_icud = get_mean(config['delayICUD'])
delay_icurec = get_mean(config['delayICUREC'])
hosfrac = get_mean(config['hosfrac'])
dfrac = get_mean(config['dfrac'])
t_max = np.size(data[:, I_HOSCUM])
time = np.linspace(1, t_max, int(t_max))
icufrac = read_icufrac_data(config, time, 0)
# icufrac =config['ICufrac']
icudfrac = get_mean(config['icudfrac'])
hos = data[:, I_HOS]
if np.sum(np.cumsum(hos)) > 0.1:
return data
# else include the hospitalised column
hoscum = data[:, I_HOSCUM]
fracs = [hosfrac, dfrac, icufrac, icudfrac]
delays = [
delay_rec, delay_hos, delay_hosrec, delay_hosd, delay_icu, delay_icud,
delay_icurec
]
dataprocess = do_hospitalization_process(hoscum, delays, fracs)
hos = dataprocess[:, OP_HOS]
hos = hos[:, None]
icucum = dataprocess[:, OP_ICUCUM]
# print ('day icufrac icucum')
# for i, ic in enumerate(icucum):
# if (i<t_max):
# print (time[i], icufrac[i], ic)
datanew = np.concatenate((data[:, 0:6], hos), axis=-1)
return datanew
def parse_config(config, mode='prior'):
"""
Turns configuration file into parsed model parameters
:param config: The entire configuration dictionary
:return: parsed model parameters: params, init_values, alphas_t, m, time, time_delay
"""
nr_samples = config['nr_prior_samples'] if mode == 'prior' else config[
'nr_forecast_samples']
t_max = config['t_max']
time_delay = config['time_delay']
population = to_distr(config['population'], nr_samples)
n = to_distr(config['N'], nr_samples)
sigma = to_distr(config['sigma'], nr_samples)
gamma = to_distr(config['gamma'], nr_samples)
r0 = to_distr(config['R0'], nr_samples)
alpha = np.array(config['alpha'])
dayalpha1 = config['dayalpha']
dayalpha = np.array(dayalpha1)
dayalpha = (dayalpha + time_delay)
delay_rec = to_distr(config['delayREC'], nr_samples)
delay_hos = to_distr(config['delayHOS'], nr_samples)
delay_hosrec = to_distr(config['delayHOSREC'], nr_samples)
delay_hosd = to_distr(config['delayHOSD'], nr_samples)
delay_icu = to_distr(config['delayICUCAND'], nr_samples)
delay_icud = to_distr(config['delayICUD'], nr_samples)
delay_icurec = to_distr(config['delayICUREC'], nr_samples)
hosfrac = to_distr(config['hosfrac'], nr_samples)
dfrac = to_distr(config['dfrac'], nr_samples)
# add standard dev for gaussian smoothing
rec_sd = to_gauss_smooth_dist(config['delayREC'], nr_samples)
hos_sd = to_gauss_smooth_dist(config['delayHOS'], nr_samples)
hosrec_sd = to_gauss_smooth_dist(config['delayHOSREC'], nr_samples)
hosd_sd = to_gauss_smooth_dist(config['delayHOSD'], nr_samples)
icu_sd = to_gauss_smooth_dist(config['delayICUCAND'], nr_samples)
icud_sd = to_gauss_smooth_dist(config['delayICUD'], nr_samples)
icurec_sd = to_gauss_smooth_dist(config['delayICUREC'], nr_samples)
icudfrac = to_distr(config['icudfrac'], nr_samples)
m = to_distr(config['m'], nr_samples)
time = np.linspace(0, t_max, int(t_max) + 1)
icufrac = read_icufrac_data(config, time, time_delay)
# indep_scaling = [to_distr(config['alphascaling'], nr_samples) for _ in alpha]
# Make scaling fully correlated
# alpha_n = [a * indep_scaling[0] for i, a in enumerate(alpha)]
# Make scalling fully uncorrelated
# alpha_n = [a * indep_scaling[i] for i, a in enumerate(alpha)]
#
# alpha_out = np.array(alpha_n).T.clip(max=0.99)
# alpha_out = [list(a) for a in alpha_n]
alpha_dict = [{'type': 'uniform', 'min': a[0], 'max': a[1]} for a in alpha]
alpha_n = [to_distr(a, nr_samples) for a in alpha_dict]
return_dict = {'free_param': [], 'm_prior': [], 'locked': {}}
if np.size(icufrac) == np.size(time):
return_dict['locked']['icufrac'] = icufrac
params = [
n, r0, sigma, gamma, alpha_n, delay_hos, delay_rec, delay_hosrec,
delay_hosd, delay_icu, delay_icud, delay_icurec, hosfrac, dfrac,
icudfrac, m, population, icurec_sd, icud_sd, icu_sd, rec_sd,
hos_sd, hosrec_sd, hosd_sd
]
names = [
'n', 'r0', 'sigma', 'gamma', 'alpha', 'delay_hos', 'delay_rec',
'delay_hosrec', 'delay_hosd', 'delay_icu', 'delay_icud',
'delay_icurec', 'hosfrac', 'dfrac', 'icudfrac', 'm', 'population',
'icured_sd', 'icud_sd', 'icu_sd', 'rec_sd', 'hos_sd', 'hosrec_sd',
'hosd_sd'
]
else:
params = [
n, r0, sigma, gamma, alpha_n, delay_hos, delay_rec, delay_hosrec,
delay_hosd, delay_icu, delay_icud, delay_icurec, hosfrac, icufrac,
dfrac, icudfrac, m, population, icurec_sd, icud_sd, icu_sd, rec_sd,
hos_sd, hosrec_sd, hosd_sd
]
names = [
'n', 'r0', 'sigma', 'gamma', 'alpha', 'delay_hos', 'delay_rec',
'delay_hosrec', 'delay_hosd', 'delay_icu', 'delay_icud',
'delay_icurec', 'hosfrac', 'icufrac', 'dfrac', 'icudfrac', 'm',
'population', 'icured_sd', 'icud_sd', 'icu_sd', 'rec_sd', 'hos_sd',
'hosrec_sd', 'hosd_sd'
]
return_dict['locked']['dayalpha'] = dayalpha
# return_dict['locked']['icu_sd'] = icu_sd
# return_dict['locked']['icud_sd'] = icud_sd
# return_dict['locked']['icurec_sd'] = icurec_sd
# return_dict['locked']['rec_sd'] = rec_sd
# return_dict['locked']['hos_sd'] = hos_sd
# return_dict['locked']['hosrec_sd'] = hosrec_sd
# return_dict['locked']['hosd_sd'] = hosd_sd
for i, param in enumerate(params):
name = names[i]
return_dict = add_to_dict(return_dict, param, name)
return_dict['time'] = time
return_dict['time_delay'] = time_delay
return return_dict['m_prior'], return_dict
def parse_config(config, ndata, mode='prior'):
"""
Turns configuration file into parsed model parameters
:param config: The entire configuration dictionary
:param ndata: the number of data days (affecting icufracscale (applied only beyond ndata+timedelay) )
:param mode 'prior' will give nr_prior_samples, 'seek_N' only one with mean, else nr_forecast_samples
:param n: n=0 will taken n from the argument (range 3 times lower- 3 times higher) otherwise from the json
:return: parsed model parameters: params, init_values, alphas_t, m, time, time_delay
"""
nr_samples = 1
if mode == 'prior':
nr_samples = config['nr_prior_samples']
elif mode == 'mean':
nr_samples = 1
else:
nr_samples = config['nr_forecast_samples']
t_max = config['t_max']
time_delay = config['time_delay']
population = to_distr(config['population'], nr_samples)
n = to_distr(config['N'], nr_samples)
sigma = to_distr(config['sigma'], nr_samples)
gamma = to_distr(config['gamma'], nr_samples)
r0 = to_distr(config['R0'], nr_samples)
alpha = np.array(config['alpha'])
dayalpha1 = config['dayalpha']
dayalpha = np.array(dayalpha1)
dayalpha = (dayalpha + time_delay)
delay_rec = to_distr(config['delayREC'], nr_samples)
delay_hos = to_distr(config['delayHOS'], nr_samples)
delay_hosrec = to_distr(config['delayHOSREC'], nr_samples)
delay_hosd = to_distr(config['delayHOSD'], nr_samples)
delay_icu = to_distr(config['delayICUCAND'], nr_samples)
delay_icud = to_distr(config['delayICUD'], nr_samples)
delay_icurec = to_distr(config['delayICUREC'], nr_samples)
hosfrac = to_distr(config['hosfrac'], nr_samples)
dfrac = to_distr(config['dfrac'], nr_samples)
# add standard dev for gaussian smoothing
rec_sd = to_gauss_smooth_dist(config['delayREC'], nr_samples)
hos_sd = to_gauss_smooth_dist(config['delayHOS'], nr_samples)
hosrec_sd = to_gauss_smooth_dist(config['delayHOSREC'], nr_samples)
hosd_sd = to_gauss_smooth_dist(config['delayHOSD'], nr_samples)
icu_sd = to_gauss_smooth_dist(config['delayICUCAND'], nr_samples)
icud_sd = to_gauss_smooth_dist(config['delayICUD'], nr_samples)
icurec_sd = to_gauss_smooth_dist(config['delayICUREC'], nr_samples)
r0_scaleseason = -1
r0_daymaxseason = -1
try:
r0_scaleseason = to_distr(config['R0_scaleseason'], nr_samples)
r0_daymaxseason = to_distr(config['R0_daymaxseason'], nr_samples)
except:
pass
icudfrac = to_distr(config['icudfrac'], nr_samples)
m = to_distr(config['m'], nr_samples)
time = np.linspace(0, t_max, int(t_max) + 1)
icufrac = read_icufrac_data(config, time, time_delay)
# scaling uncertainty for future icufraction
icufracscale_input = 1.0
try:
icufracscale_input = config['icufracscale']
except:
pass
icufracscale = to_distr(icufracscale_input, nr_samples)
# indep_scaling = [to_distr(config['alphascaling'], nr_samples) for _ in alpha]
# Make scaling fully correlated
# alpha_n = [a * indep_scaling[0] for i, a in enumerate(alpha)]
# Make scalling fully uncorrelated
# alpha_n = [a * indep_scaling[i] for i, a in enumerate(alpha)]
#
# alpha_out = np.array(alpha_n).T.clip(max=0.99)
# alpha_out = [list(a) for a in alpha_n]
#alpha_dict = [{'type': 'uniform', 'min': a[0], 'max': a[1]} for a in alpha]
alpha_normal = False
try:
alpha_normal = config['alpha_normal']
except:
pass
if (alpha_normal):
alpha_dict = [{
'type': 'normal',
'mean': a[0],
'stddev': a[1]
} for a in alpha]
else:
alpha_dict = [{
'type': 'normal',
'mean': (0.5 * (a[0] + a[1])),
'stddev': ((1.0 / np.sqrt(12)) * (a[1] - a[0]))
} for a in alpha]
alpha_n = [to_distr(a, nr_samples) for a in alpha_dict]
return_dict = {'free_param': [], 'm_prior': [], 'locked': {}}
if np.size(icufrac) == np.size(time):
return_dict['locked']['icufrac'] = icufrac
params = [
n, r0, sigma, gamma, alpha_n, delay_hos, delay_rec, delay_hosrec,
delay_hosd, delay_icu, delay_icud, delay_icurec, hosfrac, dfrac,
icudfrac, m, population, icurec_sd, icud_sd, icu_sd, rec_sd,
hos_sd, hosrec_sd, hosd_sd, icufracscale, r0_scaleseason,
r0_daymaxseason
]
names = [
'n', 'r0', 'sigma', 'gamma', 'alpha', 'delay_hos', 'delay_rec',
'delay_hosrec', 'delay_hosd', 'delay_icu', 'delay_icud',
'delay_icurec', 'hosfrac', 'dfrac', 'icudfrac', 'm', 'population',
'icurec_sd', 'icud_sd', 'icu_sd', 'rec_sd', 'hos_sd', 'hosrec_sd',
'hosd_sd', 'icufracscale', 'r0_scaleseason', 'r0_daymaxseason'
]
else:
params = [
n, r0, sigma, gamma, alpha_n, delay_hos, delay_rec, delay_hosrec,
delay_hosd, delay_icu, delay_icud, delay_icurec, hosfrac, icufrac,
dfrac, icudfrac, m, population, icurec_sd, icud_sd, icu_sd, rec_sd,
hos_sd, hosrec_sd, hosd_sd, icufracscale, r0_scaleseason,
r0_daymaxseason
]
names = [
'n', 'r0', 'sigma', 'gamma', 'alpha', 'delay_hos', 'delay_rec',
'delay_hosrec', 'delay_hosd', 'delay_icu', 'delay_icud',
'delay_icurec', 'hosfrac', 'icufrac', 'dfrac', 'icudfrac', 'm',
'population', 'icurec_sd', 'icud_sd', 'icu_sd', 'rec_sd', 'hos_sd',
'hosrec_sd', 'hosd_sd', 'icufracscale', 'r0_scaleseason',
'r0_daymaxseason'
]
return_dict['locked']['dayalpha'] = dayalpha
return_dict['locked']['ndataend'] = ndata + time_delay
# return_dict['locked']['icu_sd'] = icu_sd
# return_dict['locked']['icud_sd'] = icud_sd
# return_dict['locked']['icurec_sd'] = icurec_sd
# return_dict['locked']['rec_sd'] = rec_sd
# return_dict['locked']['hos_sd'] = hos_sd
# return_dict['locked']['hosrec_sd'] = hosrec_sd
# return_dict['locked']['hosd_sd'] = hosd_sd
for i, param in enumerate(params):
name = names[i]
return_dict = add_to_dict(return_dict, param, name)
return_dict['time'] = time
return_dict['time_delay'] = time_delay
return return_dict['m_prior'], return_dict