COVID-19
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model-mexico
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Gerardo Marx Chávez-Campos a4c338468a Update 'Readme.md' 4 years ago
data data 4 years ago
.gitignore updated march 25 4 years ago
Readme.md Update 'Readme.md' 4 years ago
covid-model.ipynb updated march 25 4 years ago
output_4_1.png updated march 25 4 years ago
output_7_1.png updated march 25 4 years ago
output_11_1.png updated march 25 4 years ago
output_18_1.png updated march 25 4 years ago
requirements.txt updated march 25 4 years ago

Readme.md 

# Reading data
import os
import git
import shutil
import tempfile

# Create temporary dir
t = tempfile.mkdtemp()
d = 'lwc/topics/covid19/covid-model'
# Clone into temporary dir
git.Repo.clone_from('http://gmarx.jumpingcrab.com:8088/COVID-19/covid19-data.git', 
                    t, branch='master', depth=1)
# Delete files
#os.remove('README.txt')
shutil.rmtree('data')
#shutil.rmtree('secondTest')
# Copy desired file from temporary dir
shutil.move(os.path.join(t, 'data'), '.')
# Remove temporary dir
shutil.rmtree(t)

import pandas as pd
import numpy as np
import os
def loadData(path, file):
    csvPath=os.path.join(path, file)
    return pd.read_csv(csvPath)

# import jtplot submodule from jupyterthemes
from jupyterthemes import jtplot
PATH=os.path.join("data")
covid_data=loadData(PATH,"time-series-19-covid-combined.csv")
covid_data.head()

Date Country/Region Province/State Lat Long Confirmed Recovered Deaths
0 2020-01-22 Afghanistan NaN 33.0 65.0 0 0.0 0
1 2020-01-23 Afghanistan NaN 33.0 65.0 0 0.0 0
2 2020-01-24 Afghanistan NaN 33.0 65.0 0 0.0 0
3 2020-01-25 Afghanistan NaN 33.0 65.0 0 0.0 0
4 2020-01-26 Afghanistan NaN 33.0 65.0 0 0.0 0 
from sklearn.model_selection import train_test_split 
train_set, test_set=train_test_split(covid_data,test_size=0.2,random_state=42)
train_cp=train_set.copy()

%matplotlib inline
covid_data.hist()


array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11e46ca50>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x116caea90>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x11e65dd10>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x11e6a16d0>],
       [<matplotlib.axes._subplots.AxesSubplot object at 0x11e6d3ed0>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x11e716710>]],
      dtype=object)

png

%matplotlib inline 
import matplotlib.pyplot as plt 
covid_mexico = covid_data[covid_data['Country/Region']=='Mexico']
covid_mexico.shape

(63, 8)



covid_data.plot(kind="scatter", x="Long", y="Lat")

<matplotlib.axes._subplots.AxesSubplot at 0x12b7c9910>

png

from datetime import datetime
#covid_mexico['Date'] =pd.to_datetime(covid_mexico.Date, format="%Y-%m-%d")
mexico_sort=covid_mexico.sort_values(by='Date', ascending=True)
mexico_sort

Date Country/Region Province/State Lat Long Confirmed Recovered Deaths
9954 2020-01-22 Mexico NaN 23.6345 -102.5528 0 0.0 0
9955 2020-01-23 Mexico NaN 23.6345 -102.5528 0 0.0 0
9956 2020-01-24 Mexico NaN 23.6345 -102.5528 0 0.0 0
9957 2020-01-25 Mexico NaN 23.6345 -102.5528 0 0.0 0
9958 2020-01-26 Mexico NaN 23.6345 -102.5528 0 0.0 0
... ... ... ... ... ... ... ... ...
10012 2020-03-20 Mexico NaN 23.6345 -102.5528 164 4.0 1
10013 2020-03-21 Mexico NaN 23.6345 -102.5528 203 4.0 2
10014 2020-03-22 Mexico NaN 23.6345 -102.5528 251 4.0 2
10015 2020-03-23 Mexico NaN 23.6345 -102.5528 316 4.0 3
10016 2020-03-24 Mexico NaN 23.6345 -102.5528 367 NaN 4

63 rows × 8 columns

mexico_filter = mexico_sort[mexico_sort['Confirmed']!=0]
mexico_filter

Date Country/Region Province/State Lat Long Confirmed Recovered Deaths
9991 2020-02-28 Mexico NaN 23.6345 -102.5528 1 0.0 0
9992 2020-02-29 Mexico NaN 23.6345 -102.5528 4 0.0 0
9993 2020-03-01 Mexico NaN 23.6345 -102.5528 5 0.0 0
9994 2020-03-02 Mexico NaN 23.6345 -102.5528 5 0.0 0
9995 2020-03-03 Mexico NaN 23.6345 -102.5528 5 1.0 0
9996 2020-03-04 Mexico NaN 23.6345 -102.5528 5 1.0 0
9997 2020-03-05 Mexico NaN 23.6345 -102.5528 5 1.0 0
9998 2020-03-06 Mexico NaN 23.6345 -102.5528 6 1.0 0
9999 2020-03-07 Mexico NaN 23.6345 -102.5528 6 1.0 0
10000 2020-03-08 Mexico NaN 23.6345 -102.5528 7 1.0 0
10001 2020-03-09 Mexico NaN 23.6345 -102.5528 7 1.0 0
10002 2020-03-10 Mexico NaN 23.6345 -102.5528 7 4.0 0
10003 2020-03-11 Mexico NaN 23.6345 -102.5528 8 4.0 0
10004 2020-03-12 Mexico NaN 23.6345 -102.5528 12 4.0 0
10005 2020-03-13 Mexico NaN 23.6345 -102.5528 12 4.0 0
10006 2020-03-14 Mexico NaN 23.6345 -102.5528 26 4.0 0
10007 2020-03-15 Mexico NaN 23.6345 -102.5528 41 4.0 0
10008 2020-03-16 Mexico NaN 23.6345 -102.5528 53 4.0 0
10009 2020-03-17 Mexico NaN 23.6345 -102.5528 82 4.0 0
10010 2020-03-18 Mexico NaN 23.6345 -102.5528 93 4.0 0
10011 2020-03-19 Mexico NaN 23.6345 -102.5528 118 4.0 1
10012 2020-03-20 Mexico NaN 23.6345 -102.5528 164 4.0 1
10013 2020-03-21 Mexico NaN 23.6345 -102.5528 203 4.0 2
10014 2020-03-22 Mexico NaN 23.6345 -102.5528 251 4.0 2
10015 2020-03-23 Mexico NaN 23.6345 -102.5528 316 4.0 3
10016 2020-03-24 Mexico NaN 23.6345 -102.5528 367 NaN 4 
n=mexico_filter.shape[0]
days=np.arange(1,n+1,1)
days

array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,
       18, 19, 20, 21, 22, 23, 24, 25, 26])

#mexico_filter = mexico_sort[mexico_sort['Confirmed']!=0]
plt.scatter(x=days, y=mexico_filter['Confirmed'])

<matplotlib.collections.PathCollection at 0x12acc8290>

png



from scipy.optimize import curve_fit
def exponential(x, a,k, b):
    return a*np.exp(x*k) + b

potp, pcov = curve_fit(exponential, days, mexico_filter['Confirmed'])
potp

array([ 1.07768657,  0.22640743, -3.90363561])









# Plot outputs
plt.scatter(days, mexico_filter['Confirmed'],  color='black')
plt.plot(days,exponential(days,*potp), color='blue', linewidth=2)

[<matplotlib.lines.Line2D at 0x12b888fd0>]

png