Ebola Outbreak 2014

In [1]:

    

df = pd.DataFrame.from_csv('https://raw.githubusercontent.com/cmrivers/ebola/master/country_timeseries.csv',
                 index_col=0)
df = df.sort_index()
df = df.fillna(method='ffill')


    

In [2]:

    

titles = [k for k in df.columns if 'cases'  in k.lower()]
df.plot(y=titles, kind='area')
legend()


    

    
Out[2]:





<matplotlib.legend.Legend at 0x7fedd7c33590>






    

In [3]:

    

titles = [k for k in df.columns if 'Deaths' in k]
df.plot(y=titles,kind='area')
legend()


    

    
Out[3]:





<matplotlib.legend.Legend at 0xb9eee50>






    

In [5]:

    

df['total deaths'] = df[titles].sum(axis =1)
df.plot(y='total deaths', 
        title='Total Deaths in \n 2014 Ebola Outbreak')
ylabel('Total Deaths')


    

    
Out[5]:





<matplotlib.text.Text at 0xc13b390>






    

In [6]:

    

import seaborn as sn
df['log total deaths'] = log10(df['total deaths'].values)
sn.lmplot('Day','log total deaths', df)


    

    
Out[6]:





<seaborn.axisgrid.FacetGrid at 0xc144bd0>






    

In [7]:

    

import statsmodels.formula.api as sm

ols = sm.OLS(df['Day'].values, df['log total deaths'].values)
ols.fit().summary()


    

    
Out[7]:





OLS Regression Results

  
Dep. Variable:
            
y
        
  R-squared:         
 
   0.864


  
Model:
                   
OLS
       
  Adj. R-squared:    
 
   0.862


  
Method:
             
Least Squares
  
  F-statistic:       
 
   520.9


  
Date:
             
Fri, 17 Oct 2014
 
  Prob (F-statistic):
 
2.83e-37


  
Time:
                 
12:07:40
     
  Log-Likelihood:    
 
 -430.28


  
No. Observations:
      
    83
      
  AIC:               
 
   862.6


  
Df Residuals:
          
    82
      
  BIC:               
 
   865.0


  
Df Model:
              
     1
      
                     
     
 
   




   
     
coef
     
std err
      
t
      
P>|t|
 
[95.0% Conf. Int.]
 


  
x1
 
   39.8578
 
    1.746
 
   22.823
 
 0.000
 
   36.384    43.332




  
Omnibus:
       
66.167
 
  Durbin-Watson:     
 
   0.003


  
Prob(Omnibus):
 
 0.000
 
  Jarque-Bera (JB):  
 
   6.733


  
Skew:
          
 0.003
 
  Prob(JB):          
 
  0.0345


  
Kurtosis:
      
 1.605
 
  Cond. No.          
 
    1.00

In [ ]: