def get_data(country = "USA", level = 3, start = date(2020,1,1), pr_capita = 100000):
#Get data function
x, src = covid19(country, level = level, start = start, verbose = False)
##clean up
#Select columns:
x = x[[
'date',
'id',
'confirmed',
'deaths',
'population',
'administrative_area_level_1',
'administrative_area_level_2',
'administrative_area_level_3']]
#Add new infected columns and remove (very few NAs - one in the start of each state)
x["new_infected"] = x.groupby(["administrative_area_level_2"])["confirmed"].diff()
#Remove NAs
x = x[x["new_infected"].notna()]
#Remove peeked values with 3day rolling window
x['smoothed_new_infected'] = x.groupby('administrative_area_level_2')['new_infected'].rolling(3).mean().reset_index(0,drop=True)
#Normalize new infected per capita within state
x["new_infected_pr_capita"] = (x["smoothed_new_infected"]/x["population"])*pr_capita
return x
def _covid19(self, *args, **kw):
x, src = covid19dh.covid19(*args, **kw, vintage=True,
verbose=False) # fetch
# test
self.assertIsInstance(x, pd.DataFrame)
for col in [
"id", "date", "tests", "confirmed", "recovered", "deaths",
"hosp", "vent", "icu"
]:
self.assertIn(col, x.columns)
for col in ["population", "latitude", "longitude"]:
self.assertIn(col, x.columns)
for col in [
"school_closing", "workplace_closing", "cancel_events",
"gatherings_restrictions", "transport_closing",
"testing_policy", "stay_home_restrictions",
"internal_movement_restrictions",
"international_movement_restrictions", "information_campaigns",
"contact_tracing", "stringency_index", "key",
"key_apple_mobility", "key_google_mobility"
]:
self.assertIn(col, x.columns)
for col in [
"iso_alpha_3", "iso_alpha_2", "iso_numeric", "currency",
"administrative_area_level", "administrative_area_level_1",
"administrative_area_level_2", "administrative_area_level_3"
]:
self.assertIn(col, x.columns)
return x, src
def _covid19(self, *args, **kw):
x,src = covid19dh.covid19(*args, **kw, verbose = False) # fetch
# test
self.assertIsInstance(x, pd.DataFrame)
cols = set(self._numeric_attributes) | set(self._constant_attributes)
cols |= set(self._sourceless_attributes) | set(self._indicator_attributes)
for col in cols:
self.assertIn(col, x.columns)
return x,src
def data_item(item_id: str, q: str = None):
jsondataset = covid19(item_id, verbose=False)
data = {}
datapoints = json.loads(jsondataset.to_json())
data['authors'] = str(covid19datahub_authors)
data['goal'] = str(covid19datahub_goal)
data['data'] = datapoints
data['citations'] = cite(jsondataset)
#return json.dumps(data, sort_keys=True, indent=4)
return data
def _covid19dh_retrieve(self):
"""
Retrieve dataset and citation list from COVID-19 Data Hub.
Citation list will be saved to self.
Returns:
(tuple):
(pandas.DataFrame): dataset at country level
(pandas.DataFrame): dataset at province level
"""
c_df = covid19dh.covid19(country=None, level=1, verbose=False)
c_citations = covid19dh.cite(c_df)
# For some countries, province-level data is included
p_df = covid19dh.covid19(country=None, level=2, verbose=False)
p_citations = covid19dh.cite(p_df)
# Citation
citations = list(dict.fromkeys(c_citations + p_citations))
self._covid19dh_citation = "\n".join(citations)
return (c_df, p_df)
def _covid19dh_retrieve(self):
"""
Retrieve dataset and citation list from COVID-19 Data Hub.
Citation list will be saved to self.
Returns:
(tuple):
(pandas.DataFrame): dataset at country level
(pandas.DataFrame): dataset at province level
Notes:
For some countries, province-level data is included.
"""
warnings.simplefilter("ignore", ResourceWarning)
c_res = covid19dh.covid19(country=None,
level=1,
verbose=False,
raw=False)
p_res = covid19dh.covid19(country=None,
level=2,
verbose=False,
raw=False)
try:
c_df, c_cite = c_res
p_df, p_cite = p_res
except ValueError:
# covid19dh <= 1.14
c_df, c_cite = c_res.copy(), covid19dh.cite(c_res)
p_df, p_cite = p_res.copy(), covid19dh.cite(p_res)
citations = list(dict.fromkeys(c_cite + p_cite))
self._covid19dh_citation = "\n".join(citations)
return (c_df, p_df)
# Citation
cite = pd.concat([c_cite, p_cite], axis=0, ignore_index=True)
cite = cite.loc[:, ["title", "year", "url"]]
cite = cite.sort_values(["year", "url"], ascending=[False, True])
cite.drop_duplicates(subset="title", inplace=True)
series = cite.apply(lambda x: f"{x[0]} ({x[1]}), {x[2]}", axis=1)
self._covid19dh_citation = "\n".join(series.tolist())
return (c_df, p_df)
def _download(self):
"""
Retrieve dataset and citation list from COVID-19 Data Hub.
Returns:
tuple:
pandas.DataFrame: dataset at country level
pandas.DataFrame: dataset at province level
str: the list of primary sources
Note:
For some countries, province-level data is included.
"""
warnings.simplefilter("ignore", ResourceWarning)
levels = [f"administrative_area_level_{i}" for i in range(1, 4)]
# Level 1 (country/region)
c_raw, c_cite = covid19dh.covid19(country=None,
level=1,
verbose=False,
raw=True)
c_df = c_raw.groupby(levels[0]).ffill().fillna(0)
for num in range(3):
c_df.loc[:, levels[num]] = c_raw[levels[num]]
# Level 2 (province/state)
p_raw, p_cite = covid19dh.covid19(country=None,
level=2,
verbose=False,
raw=True)
p_df = p_raw.groupby(levels[:2]).ffill().fillna(0)
for num in range(3):
p_df.loc[:, levels[num]] = p_raw[levels[num]]
# Citation
cite = pd.concat([c_cite, p_cite], axis=0, ignore_index=True)
cite = cite.loc[:, ["title", "year", "url"]]
cite = cite.sort_values(["year", "url"], ascending=[False, True])
cite.drop_duplicates(subset="title", inplace=True)
series = cite.apply(lambda x: f"{x[0]} ({x[1]}), {x[2]}", axis=1)
return (c_df, p_df, "\n".join(series.tolist()))
def get_covid_data(filename='Covid'):
"""
Gets the daily records about the ongoing pandemic due by the proliferation of the coronavirus SARS-CoV-2.
:param filename: name of the pickle file that will host the data.
:return: address of the pickle file.
"""
# Get the data
dataframe, src = covid19(raw=True)
# Create the path where to save the data
dataframe_path = os.path.join(base_dir, f'{filename}.pkl')
# Save the dataset in a pickle file
to_pickle(dataframe, dataframe_path)
return dataframe_path
def test_register_total(self):
# Directly download province level data from COVID-19 Data Hub
raw_df, *_ = covid19dh.covid19("Italy", level=2, verbose=False)
filename = "input/italy_raw.csv"
raw_df.to_csv(filename)
# Create CountryData instance
country_data = CountryData(filename=filename, country="Italy")
country_data.set_variables(
date="date", confirmed="confirmed", recovered="recovered", fatal="deaths",
province="administrative_area_level_2"
)
# Register total value of all provinces as country level data
country_data.register_total()
provinces = country_data.cleaned()[Term.PROVINCE].unique()
assert Term.UNKNOWN in provinces
def mismatching_days():
# get data
cases = covid_death_cases()
x = cases.groupby(["date"]).size().reset_index(name='case_agg')
# reference
ref, _ = covid19dh.covid19("Poland", verbose=False)
ref = ref
ref['deaths'] = ref.deaths.diff()
# merge
x = x.merge(ref, on="date")
# parse not matching
x = x[x.case_agg != x.deaths]
return x
def test_twitter_deaths(self):
# get data
cases = PL.covid_death_cases()
x = cases.groupby(["date"]).size().reset_index(name='case_agg')
# reference
ref,_ = covid19dh.covid19("Poland", verbose = False)
ref = ref[["date","deaths"]]
ref['deaths'] = ref.deaths.diff()
# merge
x = x.merge(ref, on = "date")
# parse not matching
x = x[x.case_agg != x.deaths]
def restrictions(country, level = 3):
data,_ = covid19dh.covid19(country, level = level, verbose = False) # load data
if level >= 1: level_cols = ['iso_alpha_2']
if level >= 2: level_cols = [*level_cols,'administrative_area_level_2']
if level == 3: level_cols = [*level_cols,'administrative_area_level_3']
x = data[['date','iso_alpha_3',*level_cols,*restriction_cols]]\
.reset_index(drop = True)
# restrictions imposed
x['week'] = x.date.apply( lambda dt: dt.isocalendar()[1] )
x = x[["week",*level_cols,*restriction_cols]]\
.groupby(["week",*level_cols])\
.aggregate({c:np.nanmean for c in restriction_cols})\
.reset_index()
if level > 1:
cities = x\
.drop_duplicates(subset = ["iso_alpha_2","administrative_area_level_3"])
mapcities = tools.nuts(city = cities.administrative_area_level_3,
country = cities.iso_alpha_2)
return x
def _IT_data(level=1):
# read cache
it = cache.read(country='IT', level=level)
if it is not None: return it
# fetch
x, _ = covid19dh.covid19('ITA', level=level, verbose=False)
# country level
if level == 1:
x = x[['date','confirmed','tests','deaths','recovered']]\
.sort_values('date')\
.reset_index(drop = True)
# to diff
x['confirmed'] = x.confirmed.diff().fillna(x.loc[0, 'confirmed'])
x['tests'] = x.tests.diff().fillna(x.loc[0, 'tests'])
x['deaths'] = x.deaths.diff().fillna(x.loc[0, 'deaths'])
x['recovered'] = x.recovered.diff().fillna(x.loc[0, 'recovered'])
x = x[~x.confirmed.isna() & ~x.tests.isna() & ~x.deaths.isna()
& ~x.recovered.isna()]
if level == 1:
x['region'] = 'IT'
# region level
else:
name_mapper = _name_to_nuts()
x['region'] = x.administrative_area_level_2.apply(name_mapper.get)
x = x[['date','region','confirmed','tests','deaths','recovered']]\
.sort_values('date')\
.reset_index(drop = True)
# to diff
for reg, df in x.groupby('region'):
x.loc[x.region == reg,'confirmed'] = df.confirmed\
.diff().fillna(df.reset_index(drop=True).loc[0,'confirmed'])
x.loc[x.region == reg,'tests'] = df.tests\
.diff().fillna(df.reset_index(drop=True).loc[0,"tests"])
x.loc[x.region == reg,'deaths'] = df.deaths\
.diff().fillna(df.reset_index(drop=True).loc[0,"deaths"])
x.loc[x.region == reg,'recovered'] = df.recovered\
.diff().fillna(df.reset_index(drop=True).loc[0,"recovered"])
cache.write(x, country='IT', level=level)
return x
def test_cite_verbose(self):
x,src = covid19dh.covid19("CZE", verbose = False)
# cite
src2 = covid19dh._cite.cite(x, verbose = False)
df = df.rename(columns={'date': 'date_key'})
copy_to_sql(df = df, table_name = "STG_Google_Mobility", schema_name=params.SchemaName, primary_index = ['date_key'], if_exists = 'replace')
from datetime import datetime
datetime.utcnow()
dateTimeObj = pytz.utc.localize(datetime.utcnow()).astimezone(pytz.timezone('US/Pacific'))
timestampStr = dateTimeObj.strftime("%d-%b-%Y (%H:%M:%S.%f)")
print("Google Mobility Finished! " + timestampStr)
#############################################################
# 6) COVID Datahub Level 3
#############################################################
import datetime
df = covid19("USA", level = 3, start = date(2020,1,1), verbose = False)
df['current_dttm'] = datetime.datetime.today()
df = df.rename(columns={'date': 'date_key'})
copy_to_sql(df = df, table_name = "STG_COVID19_Datahub_LVL3", schema_name=params.SchemaName, primary_index = ['date_key'], if_exists = 'replace')
from datetime import datetime
datetime.utcnow()
dateTimeObj = pytz.utc.localize(datetime.utcnow()).astimezone(pytz.timezone('US/Pacific'))
timestampStr = dateTimeObj.strftime("%d-%b-%Y (%H:%M:%S.%f)")
print("COVID Datahub Level 3 Finished! " + timestampStr)
#############################################################
# 7) COVID Datahub Level 2
#############################################################
import datetime
// AUTHOR: Dev Gupta
// Python3 Concept: using covid19dh library to get data
// GITHUB: https://github.com/guptadev21
//Add your python3 concept below
from covid19dh import covid19
import datetime
a, data = covid19(raw = True, verbose = False)
country = input("For which country you want the data: \n")
a, data = covid19(country, cache = False, verbose = False, start = datetime.date(2021,10,1))
#print(data.columns) #shows no. of total columns that can be accessed
#print(data) #to show all the data
print(data.iso_alpha_3) # to show only country and no. of cases
print("Getting latest data from covid19dh")
from covid19dh import covid19
import datetime
countries = [
"Italy", "Austria", "Germany", "Belgium", "France", "United Kingdom",
"Switzerland", "Portugal"
]
yesterday = datetime.date.today() - datetime.timedelta(days=1)
x, src = covid19(countries,
raw=True,
verbose=False,
end=yesterday,
cache=False)
print("Preprocessing european data..")
x_small = x.loc[:, [
'administrative_area_level_1', 'date', 'vaccines', 'confirmed', 'tests',
'recovered', 'deaths', 'population'
]]
x_small.rename(columns={'administrative_area_level_1': 'id'}, inplace=True)
x_small[
'confirmed_per'] = 100000 * x_small['confirmed'] / x_small['population']
x_small['deaths_per'] = 100000 * x_small['deaths'] / x_small['population']
x_small['ratio'] = 100 * (x_small['deaths']) / (x_small['confirmed'])
x_small['tests_per'] = 100000 * (x_small['tests']) / (x_small['population'])
x_small['vaccines_per'] = x_small['vaccines'] / x_small['population']
def doFit(args):
args.output_dir = os.path.join(args.output_dir,
time.strftime("%Y%m%d%H%M"))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
global mainVars
#if args.country=="ESP":
#df=pd.read_csv('/home/enrico/iper-social-simulations/examples/SAR-COV2/0dim/data/dfESP.csv')
#else:
df, _ = covid19(args.country)
#df, _ = covid19(args.country)
#import ipdb; ipdb.set_trace()
df = df[df["date"] > args.start]
df = df[df["date"] < args.stop]
df = df[df["confirmed"] > 0] #Starts from first infections
#dati_regione1=dati_regione_[dati_regione_['totale_positivi']>25] #per stabilire il t0
N = df["population"].mean()
icu_beds = pd.read_csv(args.data_icu, header=0)
icu_beds = dict(zip(icu_beds["Country"], icu_beds["ICU_Beds"]))
icu_beds = icu_beds[
"Italy"] * N / 100000 # Emergency life support beds per 100k citizens
print("Maximum icy beds", icu_beds)
#dati_regione1=pd.read_csv('dpc-covid19-ita-andamento-nazionale.csv')
#regione='ITA'
age_effect = 1
demographic = {"0-29": 0.2, "30-59": 0.4, "60-89": 0.35, "89+": 0.05}
#I=np.array(dati_regione1['totale_positivi'])
#D=np.array(dati_regione1['deceduti'])
#R=np.array(dati_regione1['dimessi_guariti'])
#H=np.array(dati_regione1['totale_ospedalizzati'])
#S=N-I-D-H-R
df.plot(x="date", y=["tests"])
plt.savefig(os.path.join(args.output_dir, "tests-%s.png" % args.country))
df.plot(x="date", y=["confirmed", "recovered", "deaths"])
plt.savefig(
os.path.join(args.output_dir,
"confirmed-recovered-%s.png" % args.country))
df.plot(x="date", y=["deaths", "hosp", "vent", "icu"])
plt.savefig(os.path.join(args.output_dir,
"severity-%s.png" % args.country))
print("Maximum people in vent", df["vent"].max())
print("Maximum people in icu", df["icu"].max())
#x1=df.copy(deep=True)
#Iday=x1.iloc[:,3]
#Iday.fillna(0,inplace=True,axis=0)
#Iday=Iday.values
#for i in range(len(Iday)-1,1,-1):
#Iday[i]=Iday[i]-Iday[i-1]
#if args.country=="ESP":
# df1=pd.read_csv('https://raw.githubusercontent.com/datadista/datasets/master/COVID%2019/nacional_covid19.csv')
# df1 = df1[df1["fecha"]>args.start]
# df1 = df1[df1["fecha"]<args.stop]
# x1=df1.copy(deep=True)
# Hday=x1.iloc[:,3]
# Hday.fillna(0,inplace=True,axis=0)
# Hday=Hday.values
if args.country == 'ESP':
I = df[
"confirmed"] # in questo caso rappresenta il totale degli individui che si sono infettati
#R = df["recovered"]# totale recovered
D = df["deaths"] # Totale morti
#H = df["hosp"]
S = N - I
t = np.array([i + 1 for i in range(len(I))])
i0 = df["confirmed"].iloc[0] # 35708 #infetti a t0
a0 = 2 * i0
r0 = 0
ra0 = 0
h0 = 0
d0 = df["deaths"].iloc[0] # 35587 #deceduti t0
s0 = N - i0 #-a0 #suscettibili a t0
y0 = s0, a0, i0, r0, ra0, h0, d0
else:
I = df["confirmed"] - df["recovered"] - df["hosp"] - df["deaths"]
R = df["recovered"]
D = df["deaths"]
H = df["hosp"]
S = N - df["confirmed"]
t = np.array([i + 1 for i in range(len(I))])
i0 = df["confirmed"].iloc[0] - df["recovered"].iloc[0] - df[
"deaths"].iloc[0] # 35708 #infetti a t0
a0 = 2 * i0 #asintomatici a t0
r0 = df["recovered"].iloc[0] # 211885 #recovered a t0
ra0 = 2 * r0
h0 = df["hosp"].iloc[0] # 2000 #ospedaliz. a t0
d0 = df["deaths"].iloc[0] # 35587 #deceduti t0
s0 = N - i0 - r0 - h0 - d0 - a0 - ra0 #suscettibili a t0
y0 = s0, a0, i0, r0, ra0, h0, d0
def resid_ESP(params, y0, t, N, age_effect, S, I, D, icu_beds):
print(params)
rates = {"rsa": 0, "rai": 0, "rih": 0, "rir": 0, "rhr": 0, "rhd": 0}
r0_max = params['r0_max']
r0_min = params['r0_min']
k = params['k']
startLockdown = params['startLockdown']
seihrd_det = solveSIRdet(y0, t, N, age_effect, r0_max, r0_min, k,
startLockdown, params, demographic, icu_beds)
#return sum((seihrd_det['S']-S)**2+(seihrd_det['I']-I)**2+(seihrd_det['H']-H)**2+(seihrd_det['R']-R)**2+(seihrd_det['D']-D)**2)
#er=np.array((((seihrd_det['S']-S)**2),((seihrd_det['I']-I)**2),((seihrd_det['H']-H)**2),((seihrd_det['R']-R)**2),((seihrd_det['D']-D)**2)))
a = np.array((S - (seihrd_det['S'] + seihrd_det['A']))**2)
b = np.array((I - (seihrd_det['I'] + seihrd_det['R'] +
seihrd_det['H'] + seihrd_det['D']))**2)
#b=np.array((Iday-seihrd_det['I'])**2)
#c=np.array((H-seihrd_det['H'])**2)
#d=np.array((R-seihrd_det['R'])**2)
e = np.array((1.2 * D - 1.2 * seihrd_det['D'])**2)
tot = np.concatenate((b, e))
#return er
#er=er.flatten()
#er2= np.array((seihrd_det['S']-S)**2,(seihrd_det['I']-I)**2,(seihrd_det['H']-H)**2,(seihrd_det['R']-R)**2,(seihrd_det['D']-D)**2)
return tot
def resid_ALL(params, y0, t, N, age_effect, S, I, H, R, D, icu_beds):
print(params)
rates = {"rsa": 0, "rai": 0, "rih": 0, "rir": 0, "rhr": 0, "rhd": 0}
r0_max = params['r0_max']
r0_min = params['r0_min']
k = params['k']
startLockdown = params['startLockdown']
seihrd_det = solveSIRdet(y0, t, N, age_effect, r0_max, r0_min, k,
startLockdown, params, demographic, icu_beds)
#return sum((seihrd_det['S']-S)**2+(seihrd_det['I']-I)**2+(seihrd_det['H']-H)**2+(seihrd_det['R']-R)**2+(seihrd_det['D']-D)**2)
#er=np.array((((seihrd_det['S']-S)**2),((seihrd_det['I']-I)**2),((seihrd_det['H']-H)**2),((seihrd_det['R']-R)**2),((seihrd_det['D']-D)**2)))
a = np.array((S - (seihrd_det['S'] + seihrd_det['A']))**2)
b = np.array((I - seihrd_det['I'])**2)
c = np.array((H - seihrd_det['H'])**2)
d = np.array((R - seihrd_det['R'])**2)
e = np.array(1.44 * (D - seihrd_det['D'])**2) #mettere fuori 1.2
tot = np.concatenate((a, b, e, c, d))
#return er
#er=er.flatten()
#er2= np.array((seihrd_det['S']-S)**2,(seihrd_det['I']-I)**2,(seihrd_det['H']-H)**2,(seihrd_det['R']-R)**2,(seihrd_det['D']-D)**2)
return tot
if args.country == 'ESP':
params2 = Parameters()
params2.add('r0_max', value=5.0, min=2.0, max=5.0, vary=True)
params2.add('r0_min', value=0.9, min=0.3, max=3.5, vary=True)
params2.add('k', value=2.5, min=0.01, max=5.0, vary=True)
params2.add('startLockdown', value=31.0, min=0, max=100, vary=True)
params2.add('rsa', value=1.0, min=0.01, max=2.0, vary=True)
params2.add('rai', value=0.5, min=0.01, max=2, vary=True)
params2.add('rar',
value=0.010080807956200328,
min=0.01,
max=2,
vary=False)
params2.add('rih',
value=0.15376988655220583,
min=0.01,
max=2.0,
vary=False)
params2.add('rir',
value=0.012084665739583795,
min=0.01,
max=2,
vary=False)
params2.add('rhr', value=0.012, min=0.01, max=2,
vary=False) #0.0743636663768294
params2.add('rhd', value=0.125, min=0.01, max=2, vary=True) #
out = minimize(resid_ESP,
params2,
args=(y0, t, N, age_effect, S, I, D, icu_beds),
method='differential_evolution'
) #method='differential_evolution',method='leastsq'
else:
params2 = Parameters()
params2.add('r0_max', value=5.0, min=2.0, max=5.0, vary=True)
params2.add('r0_min', value=0.9, min=0.3, max=3.5, vary=True)
params2.add('k', value=2.5, min=0.01, max=5.0, vary=True)
params2.add('startLockdown', value=31.0, min=0, max=100, vary=True)
params2.add('rsa', value=1.0, min=0.01, max=2.0, vary=True)
params2.add('rai', value=0.5, min=0.01, max=2, vary=True)
params2.add('rar', value=0.1, min=0.01, max=2, vary=True)
params2.add('rih', value=0.1, min=0.01, max=2.0, vary=True)
params2.add('rir', value=0.1, min=0.01, max=2, vary=True)
params2.add('rhr', value=0.142, min=0.01, max=2, vary=True)
params2.add('rhd', value=0.125, min=0.01, max=2, vary=True)
#res3=resid_(params2,y0, t, N,age_effect,S,I,H,R,D,icu_beds)
out = minimize(resid_ALL,
params2,
args=(y0, t, N, age_effect, S, I, H, R, D, icu_beds),
method='differential_evolution'
) #method='differential_evolution',method='leastsq'
print(fit_report(out))
print(out.params.pretty_print())
#out.plot_fit(datafmt="-")
#plt.savefig(os.path.join(args.output_dir, "best_fit.png"))
#import ipdb; ipdb.set_trace()
print("**** Estimated parameters:")
print([out.params.items()])
rates_fit = {
"rsa": out.params['rsa'].value,
"rai": out.params['rai'].value,
"rar": out.params['rar'].value,
"rih": out.params['rih'].value,
"rir": out.params['rir'].value,
"rhr": out.params['rhr'].value,
"rhd": out.params['rhd'].value
}
seihrd_det_fit = solveSIRdet(y0, t, N, age_effect,
out.params['r0_max'].value,
out.params['r0_min'].value,
out.params['k'].value,
out.params['startLockdown'].value, rates_fit,
demographic, icu_beds)
plt.figure(figsize=(15, 12))
plt.suptitle(args.country)
if args.country == 'ESP':
I_tot = seihrd_det_fit['I'] + seihrd_det_fit['R'] + seihrd_det_fit[
'D'] + seihrd_det_fit['H']
ax3 = plt.subplot(331)
ax3.plot(t, I_tot, color="r")
ax3.plot(t, I, label='Infected-cumulative', color="r", ls="--")
plt.xlabel('day')
plt.ylabel('Infected')
ax4 = plt.subplot(332)
ax4.plot(t, seihrd_det_fit['D'], color="black")
ax4.plot(t, D, label='Dead', color="black", ls="--")
plt.xlabel('day')
plt.ylabel('Dead')
ax5 = plt.subplot(333)
ax5.plot(t, seihrd_det_fit['R'], color="g")
plt.xlabel('day')
plt.ylabel('Recovered-seihrd')
#ax6=plt.subplot(334)
#ax6.plot(t,seihrd_det_fit['S'],color="y")
#ax6.plot(t,S,label='Susceptible',color="y",ls="--")
#plt.xlabel('day')
#plt.ylabel('Susceptible')
ax7 = plt.subplot(335)
ax7.plot(t, seihrd_det_fit['H'], color="orange")
plt.xlabel('day')
plt.ylabel('Hosp-seihrd')
ax8 = plt.subplot(336)
ax8.plot(t, I_tot, color="r")
ax8.plot(t, I, label='Infected-cumulative', color="r", ls="--")
ax8.plot(t, seihrd_det_fit['R'], color="g")
ax8.plot(t, seihrd_det_fit['D'], color="black")
ax8.plot(t, D, label='Dead', color="black", ls="--")
ax8.plot(t, seihrd_det_fit['H'], color="orange")
plt.xlabel('day')
plt.ylabel('people')
else:
ax3 = plt.subplot(331)
ax3.plot(t, seihrd_det_fit['I'], color="r")
ax3.plot(t, I, label='Infected', color="r", ls="--")
plt.xlabel('day')
plt.ylabel('Infected')
ax4 = plt.subplot(332)
ax4.plot(t, seihrd_det_fit['D'], color="black")
ax4.plot(t, D, label='Dead', color="black", ls="--")
plt.xlabel('day')
plt.ylabel('Dead')
ax5 = plt.subplot(333)
ax5.plot(t, seihrd_det_fit['R'], color="g")
ax5.plot(t, R, label='Recovered', color="g", ls="--")
plt.xlabel('day')
plt.ylabel('Recovered')
ax6 = plt.subplot(334)
ax6.plot(t, ((seihrd_det_fit['S'] + seihrd_det_fit['A'])), color="y")
ax6.plot(t, S, label='Susceptible', color="y", ls="--")
plt.xlabel('day')
plt.ylabel('Susceptible')
ax7 = plt.subplot(335)
ax7.plot(t, seihrd_det_fit['H'], color="orange")
ax7.plot(t, H, label='Hosp', color="orange", ls="--")
plt.xlabel('day')
plt.ylabel('Hosp.')
ax8 = plt.subplot(336)
ax8.plot(t, seihrd_det_fit['I'], color="r")
ax8.plot(t, I, label='Infected', color="r", ls="--")
ax8.plot(t, seihrd_det_fit['R'], color="g")
ax8.plot(t, R, label='Recovered', color="g", ls="--")
ax8.plot(t, seihrd_det_fit['D'], color="black")
ax8.plot(t, D, label='Dead', color="black", ls="--")
ax8.plot(t, seihrd_det_fit['H'], color="orange")
ax8.plot(t, H, label='Hosp', color="orange", ls="--")
plt.xlabel('day')
plt.ylabel('people')
#df_fitSAIHRD=pd.read_csv(os.path.join('fit','df_fitSAIHRD.csv'))
risult = np.array([[
args.country, out.params['rsa'].value, out.params['rai'].value,
out.params['rih'].value, out.params['rir'].value,
out.params['rhr'].value, out.params['rhd'].value,
out.params['r0_max'].value, out.params['r0_min'].value,
out.params['k'].value, out.params['startLockdown'].value
]])
df_fitSAIHRD = pd.DataFrame(risult,
columns=[
"regione", "rsa", "rai", "rih", "rir",
"rhr", "rhd", "r0_max", "r0_min", "k",
"startLockdown"
])
fname = "df_fitSAIHRD.csv"
df_fitSAIHRD.to_csv(os.path.join(args.output_dir, fname), index=False)
saveFIG = args.country + '.png'
plt.savefig(os.path.join(args.output_dir, saveFIG))
mainVars = inspect.currentframe().f_locals
