santiagocasas

Keybase proof

I hereby claim:

• I am santiagocasas on github.
• I am santicas (https://keybase.io/santicas) on keybase.
• I have a public key ASADjPQPAhyCIiR2FUo4tPWImOCITahwdNpgr8ulUHrujQo

To claim this, I am signing this object:

santiagocasas

Verifying my Blockstack ID is secured with the address 1GjMeGXGmiiqC1kpTHamK46BvDxrexr5AW https://explorer.blockstack.org/address/1GjMeGXGmiiqC1kpTHamK46BvDxrexr5AW

santiagocasas

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from pandas.plotting import register_matplotlib_converters ##needed to properly use datetime in plots
register_matplotlib_converters()  ##needed to properly use datetime in plots
from datetime import datetime,timedelta
from sklearn.metrics import mean_squared_error
from scipy.optimize import curve_fit
from scipy.optimize import fsolve

santiagocasas

giturl_confirmed = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv'
giturl_deaths = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Deaths.csv'
giturl_recovered = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Recovered.csv'

casestr='Deaths'   #'Confirmed' #'Recovered', 'Deaths'
chosen_plotmodel = 'Logistic'  #'Logistc'

if casestr=='Confirmed':
    curl = giturl_confirmed

santiagocasas

datesformatted = [datetime.strptime(da, '%m/%d/%y') 
                  for da in corona_cases_df.columns.values]

daterange = pd.DatetimeIndex(datesformatted)
daterange
>>>
DatetimeIndex(['2020-01-22', '2020-01-23', '2020-01-24', '2020-01-25',
               '2020-01-26', '2020-01-27', '2020-01-28', '2020-01-29',
               '2020-01-30', '2020-01-31', '2020-02-01', '2020-02-02',
               '2020-02-03', '2020-02-04', '2020-02-05', '2020-02-06',

santiagocasas

country_official_list = (corona_cases_df.index.get_level_values('Country/Region').values)

wanted_countries = ['China', 'Korea','Italy', 'France', 'US']

countries_list = [np.unique(np.array([s for s in country_official_list if cou in s])) 
                                                 for cou in wanted_countries]
countries_list = np.array(countries_list).flatten()
countries_list.sort()
countries_list
>>>

santiagocasas

cases_percountry = {}
df_dict = {}
for country in countries_list:
    cou = corona_cases_df.loc[pd.IndexSlice[:, country], :].sum()
    cases_percountry[country] = cou[cou > 0]
    datesformat = [datetime.strptime(da, '%m/%d/%y') 
                   for da in cases_percountry[country].index]
    
    df_dict[country] = pd.DataFrame(cases_percountry[country], 
                                    columns=['Cases'], index=datesformat)

santiagocasas

for country in countries_list:
    print(country)
    dates=df_dict[country].index
    plt.semilogy(dates, np.array(list(df_dict[country]['Cases'].values)), label=country)
    plt.legend(loc='lower right')
    plt.tick_params(rotation=45, labelsize=12)
    plt.ylabel(casestr, fontsize=14)
plt.xlim([dates[0]-np.timedelta64(3,'D'), dates[-1]+np.timedelta64(10,'D')])
plt.show()

santiagocasas

def expo_model(x,p,N0=3,x0=1):
    return N0*(1+p)**(x-x0)

def logistic_model(x,a,b,c):
    return c/(1+np.exp(-(x-b)/a))

def gompertz_model(x,a,b,c):
    return c*np.exp(-b*np.exp(-x/a))

santiagocasas

tt= np.linspace(0,100,100)
plt.plot(tt, logistic_model(tt,4.8,50,100000), c='b', label='Logisitc')
plt.plot(tt, expo_model(tt, 0.23), c='g', label='Exponential')
plt.plot(tt, gompertz_model(tt, 12, 49, 100000), c='r', label='Gompertz')
plt.legend()
plt.xlabel('Time')
plt.ylabel('Cases')
plt.ylim(-1000,150000)