def fit_IDF(p_reported, ifr):
name = ['Île de France']
departements, size = build_patches.patches_from_regions(name)
data = gather_hosp_data(departements[0])
fitter = lockdown.LockdownFitter(data, name[0], size[0], '2020-03-16')
fitter.setup_fit('Before lockdown',
'2020-03-01',
'2020-03-27', [17],
columns=['Hospital deaths'])
fitter.setup_fit('Lockdown', '2020-03-16', '2020-05-11', [18, 28, 28])
fitter.setup_fit('Easing of lockdown', '2020-05-11', '2020-06-10',
[10, 15, 15])
fitter.setup_fit('Second wave', '2020-07-10', '2020-10-30', [18, 28, 28])
fitter.setup_fit('Second lockdown', '2020-10-30', '2020-12-01',
[13, 18, 18])
fitter.setup_fit('Easing of second lockdown', '2020-11-25', '2020-12-18',
[10, 15, 15])
fitter.compute_growth_rates(verbose=True)
try:
EI_dist = lockdown.EI_dist_covid(p_reported)
fitter.prepare_sir(EI_dist, ifr)
for fit in fitter.fits[2:]:
if fit.name == 'Second lockdown':
continue
fitter.adjust_date_of_change(fit.name, 'Hospital admissions')
fitter.compute_sir('2020-12-31')
except Exception as error:
print('Error !!!!!!')
print(error)
print(traceback.format_exc())
finally:
return fitter
def fit_PACA(p_reported, ifr, curfew=True):
name = ["Provence-Alpes-Côte d’Azur"]
departements, size = build_patches.patches_from_regions(name)
data = gather_hosp_data(departements[0])
fitter = lockdown.LockdownFitter(data, name[0], size[0], '2020-03-16')
fitter.setup_fit('Before lockdown',
'2020-03-01',
'2020-03-27', [17],
columns=['Hospital deaths'])
fitter.setup_fit('Lockdown', '2020-03-16', '2020-05-11', [18, 28, 28])
fitter.setup_fit('Easing of lockdown', '2020-05-01', '2020-06-20',
[10, 15], ['Hospital admissions', 'Hospital deaths'])
fitter.setup_fit('Second wave (1/3)', '2020-06-28', '2020-09-07',
[18, 48, 32])
fitter.setup_fit('Second wave (2/3)', '2020-09-01', '2020-10-10',
[15, 18, 18])
fitter.setup_fit('Second wave (3/3)', '2020-09-25', '2020-10-20',
[15, 18, 18])
if curfew:
fitter.setup_fit('Curfew', '2020-10-16', '2020-11-10', [8],
['Hospital admissions'])
fitter.setup_fit('Second lockdown', '2020-10-30', '2020-12-01',
[13, 15, 15])
fitter.setup_fit('Easing of second lockdown', '2020-11-29', '2020-12-18',
[10, 15, 15])
fitter.compute_growth_rates(verbose=True)
try:
EI_dist = lockdown.EI_dist_covid(p_reported)
fitter.prepare_sir(EI_dist, ifr)
for fit in fitter.fits[2:]:
if fit.name in [
'Second lockdown', 'Curfew', 'Easing of second lockdown'
]:
continue
fitter.adjust_date_of_change(fit.name, 'Hospital admissions')
fitter.compute_sir('2020-12-31')
except Exception as error:
print('Error !!!!!!')
print(error)
print(traceback.format_exc())
finally:
return fitter
param_values = np.loadtxt("param_values_delays.txt", float)
except (OSError, Change):
param_values = saltelli.sample(inputs, 1000, calc_second_order = False)
np.savetxt("param_values_delays.txt", param_values)
try:
if COMPUTE_AGAIN or CHANGE_INPUT:
raise Change('output')
output_values = np.loadtxt('output_values_delays.txt', float)
except (OSError, Change):
output_values = np.zeros((2, np.size(param_values, 0)))
## prepare lockdown fitter
patches, sizes = build_patches.patches_from_region_list(region_list)
data_patches = gather_data(patches, SOS_medecins=False, include_early=False)
fitter = lockdown.LockdownFitter(data_patches[0], names[0], sizes[0], '2020-03-16')
fitter.setup_fit('Before lockdown', '2020-03-01', '2020-03-26', [17], columns = ['Hospital deaths'])
fitter.setup_fit('Lockdown', '2020-03-16', '2020-05-11', [18, 28, 28])
fitter.compute_growth_rates()
for (i, param) in enumerate(param_values):
print(100.*i/np.size(param_values, 0))
E_dist, I_dist = lockdown.EI_dist(param[0], param[1], 10, n=10)
EI_dist = lockdown.product_dist(E_dist, I_dist)
lockdown_datetime = fitter.datetime_lockdown + date.timedelta(days = param[2])
fitter.datetime_lockdown = lockdown_datetime
try:
fitter.prepare_sir(EI_dist, param[3], verbose = False)
except AssertionError:
continue
fitter.dates_of_change['Lockdown'] = lockdown_datetime.strftime(fitter.date_format)
data_patches = gather_data(patches, SOS_medecins=False, include_early=False)
deces = 'Décès hospitaliers dus au Covid-19'
admissions = 'Admissions à l\'hôpital liées au Covid-19'
# data_patches[0] = data_patches[0].drop(['ICU admissions'], axis = 1)
for i in range(len(names)):
data_patches[i].columns = [admissions, deces, 'Admissions en réanimation']
patches = []
for (i, name) in enumerate(names):
data_patches[i] = data_patches[i].drop(
data_patches[i]['2020-11-01':].index)
patches.append(
lockdown.LockdownFitter(data_patches[i], name, sizes[i], '2020-03-16'))
for patch in patches:
patch.setup_fit('avant le confinement',
'2020-03-01',
'2020-03-25', [17],
columns=[deces])
patch.setup_fit('confinement', '2020-03-16', '2020-06-02', [18, 28, 28])
## Setup fits in Ile de France
patches[idf].setup_fit('été', '2020-06-02', '2020-09-10', [18, 25, 25])
patches[idf].setup_fit('deuxième vague', '2020-09-01', '2020-10-30',
[10, 15, 15])
## Setup fits in Grand Est
patches[gdest].setup_fit('déconfinement', '2020-05-15', '2020-07-01',