COVID19 India Data Analysis, Predicting Total Case on 4th of May (by end of lockdown Version-02)

Do check prev post: Covid19_StateWise_Analysis_India_Part1

Here we trying to focus on what will be the confirmed case count on the last day of lockdown version-02 in India, the entire analysis is based on growth rate technique.  Let’s import required modules

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
import plotly.express as px
import plotly.offline as py
import plotly.graph_objs as go
py.init_notebook_mode(connected=True)
import folium
import seaborn as sns
import os

import datetime

Let try to find out growth rate, considering the data from 30^th Jan

confirmed_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/'+
                           'COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/'
                           +'time_series_covid19_confirmed_global.csv')

india_sel  = confirmed_df[confirmed_df['Country/Region']=='India'].loc[:'4/13/20']
india_confirmed_list = india_sel.values.tolist()[0]
india_confirmed_list[4]
growth_diff = []

for i in range(4,len(india_confirmed_list)):
    if (i == 4) or india_confirmed_list[i-1] == 0 :
        growth_diff.append(india_confirmed_list[i])
    else:
        growth_diff.append(india_confirmed_list[i] / india_confirmed_list[i-1])

growth_factor = sum(growth_diff)/len(growth_diff)
print('Average growth factor',growth_factor)

#OUTPUT: GROWTH RATE

Average growth factor 1.0637553331032963

Lets now calculate the next twenty 21 days case count and plot it in chart

x_axis_prediction_dt = []

dates = list(confirmed_df.columns[4:])
dates = list(pd.to_datetime(dates))

#we will add one day to the last day till which we have data
start_date = dates[len(dates) - 1]
for i in range(21):
    date = start_date + datetime.timedelta(days=1)
    x_axis_prediction_dt.append(date)
    start_date = date

# Get the last available day total number   
previous_day_cases = confirmed_df[confirmed_df['Country/Region']=='India'].iloc[:,-1]
# Converting series to float value
previous_day_cases = previous_day_cases.iloc[0]
y_axis_predicted_next21days_cases = []

for i in range(21):
    predicted_value = previous_day_cases *  growth_factor
    y_axis_predicted_next21days_cases.append(predicted_value)
    previous_day_cases = predicted_value
# print(previous_day_cases)

#add Graph
fig1=go.Figure()
fig1.add_trace(go.Scatter(x=x_axis_prediction_dt,
                          y=y_axis_predicted_next21days_cases,
                          name='India'
                               ))

fig1.layout.update(title_text='COVID-19 next twenty one prediction',xaxis_showgrid=False, yaxis_showgrid=False, width=800,
        height=500,font=dict(
#         family="Courier New, monospace",
        size=12,
        color="white"
    ))
fig1.layout.plot_bgcolor = 'Black'
fig1.layout.paper_bgcolor = 'Black'
fig1.show()

Growth rate predict cases will jump over 35k by 3rd of May

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