Predicting the Outbreak of COVID-19 Pandemic using Machine Learning

df_full = pd.merge(India_coord,df,on='Name of State / UT')
map = folium.Map(location=[20, 70], zoom_start=4,tiles='Stamenterrain')
for lat, lon, value, name inzip(df_full['Latitude'], df_full['Longitude'], df_full['Total cases'], df_full['Name of State / UT']):
&nbsp;&nbsp;&nbsp;&nbsp;folium.CircleMarker([lat,&nbsp;lon],&nbsp;radius=value*0.8,&nbsp;popup&nbsp;=&nbsp;('<strong>State</strong>:&nbsp;'&nbsp;+&nbsp;str(name).capitalize()&nbsp;+&nbsp;'
''<strong>Total&nbsp;Cases</strong>:&nbsp;'&nbsp;+&nbsp;str(value)&nbsp;+&nbsp;'
'),color='red',fill_color='red',fill_opacity=0.3&nbsp;).add_to(map)
map

Next, you’re going to use Plotly to obtain graphs depicting the trends of the rise of coronavirus cases across India.

#This cell's code is required when you are working with plotly on colab
import plotly
plotly.io.renderers.default = 'colab'

# Rise of COVID-19 cases in India
fig = go.Figure()
fig.add_trace(go.Scatter(x=dbd_India['Date'], y = dbd_India['Total Cases'], mode='lines+markers',name='Total Cases'))
fig.update_layout(title_text='Trend of Coronavirus Cases in India (Cumulative cases)',plot_bgcolor='rgb(230, 230, 230)')
fig.show()

import plotly.express as px
fig = px.bar(dbd_India, x="Date", y="New Cases", barmode='group', height=400)
fig.update_layout(title_text='Coronavirus Cases in India on daily basis',plot_bgcolor='rgb(230, 230, 230)')
fig.show()

From the visualization above, one can infer the following:

• Confirmed cases in India is rising exponentially with no fixed pattern (Very less test in India)
• Confirmed cases in Italy is rising exponentially with a certain fixed pattern
• Confirmed cases in S.Korea is rising gradually
• There have been almost a negligible number confirmed cases in Wuhan a week.

2.2 Comparison between the rise of cases in Wuhan, S.Korea, Italy and India

# import plotly.graph_objects as go
from plotly.subplots import make_subplots
fig = make_subplots(
    rows=2, cols=2,
    specs=[[{}, {}],
           [{"colspan": 2}, None]],
    subplot_titles=("S.Korea","Italy", "India","Wuhan"))
fig.add_trace(go.Bar(x=dbd_Korea['Date'], y=dbd_Korea['Total Cases'],
                    marker=dict(color=dbd_Korea['Total Cases'], coloraxis="coloraxis")),1, 1)
fig.add_trace(go.Bar(x=dbd_Italy['Date'], y=dbd_Italy['Total Cases'],
                    marker=dict(color=dbd_Italy['Total Cases'], coloraxis="coloraxis")),1, 2)
fig.add_trace(go.Bar(x=dbd_India['Date'], y=dbd_India['Total Cases'],
                    marker=dict(color=dbd_India['Total Cases'], coloraxis="coloraxis")),2, 1)
# fig.add_trace(go.Bar(x=dbd_Wuhan['Date'], y=dbd_Wuhan['Total Cases'],
#                     marker=dict(color=dbd_Wuhan['Total Cases'], coloraxis="coloraxis")),2, 2)
fig.update_layout(coloraxis=dict(colorscale='Bluered_r'), showlegend=False,title_text="Total Confirmed cases(Cumulative)")
fig.update_layout(plot_bgcolor='rgb(230, 230, 230)')
fig.show()

2.3 Trend after crossing 100 cases

# import plotly.graph_objects as go
title = 'Main Source for News'
labels = ['S.Korea', 'Italy', 'India']
colors = ['rgb(122,128,0)', 'rgb(255,0,0)', 'rgb(49,130,189)']
mode_size = [10, 10, 12]
line_size = [1, 1, 8]
fig = go.Figure()
fig.add_trace(go.Scatter(x=dbd_Korea['Days after surpassing 100 cases'], 
                 y=dbd_Korea['Total Cases'],mode='lines',
                 name=labels[0],
                 line=dict(color=colors[0], width=line_size[0]),            
                 connectgaps=True))
fig.add_trace(go.Scatter(x=dbd_Italy['Days after surpassing 100 cases'], 
                 y=dbd_Italy['Total Cases'],mode='lines',
                 name=labels[1],
                 line=dict(color=colors[1], width=line_size[1]),            
                 connectgaps=True))
fig.add_trace(go.Scatter(x=dbd_India['Days after surpassing 100 cases'], 
                 y=dbd_India['Total Cases'],mode='lines',
                 name=labels[2],
                 line=dict(color=colors[2], width=line_size[2]),            
                 connectgaps=True))
annotations = []
annotations.append(dict(xref='paper', yref='paper', x=0.5, y=-0.1,
                              xanchor='center', yanchor='top',
                              text='Days after crossing 100 cases ',
                              font=dict(family='Arial',
                                        size=12,
                                        color='rgb(150,150,150)'),
                              showarrow=False))
fig.update_layout(annotations=annotations,plot_bgcolor='white',yaxis_title='Cumulative cases')
fig.show()

Visualizing: Worldwide COVID-19 cases

In this segment, we’re going to generate a week ahead forecast of confirmed cases of COVID-19 using Prophet, with specific prediction intervals by creating a base model both with and without tweaking of seasonality-related parameters and additional regressors.

What is Prophet?

Prophet is open source software released by Facebook’s Core Data Science team. It is available for download on CRAN and PyPI.

We use Prophet, a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

Why Prophet?

• Accurate and fast: Prophet is used in many applications across Facebook for producing reliable forecasts for planning and goal setting. Facebook finds it to perform better than any other approach in the majority of cases. It fit models in Stan, so that you get forecasts in just a few seconds.

• Fully automatic: Get a reasonable forecast on messy data with no manual effort. Prophet is robust to outliers, missing data, and dramatic changes in your time series.

• Tunable forecasts: The Prophet procedure includes many possibilities for users to tweak and adjust forecasts. You can use human-interpretable parameters to improve your forecast by adding your domain knowledge

• Available in R or Python: Facebook has implemented the Prophet procedure in R and Python. Both of them share the same underlying Stan code for fitting. You can use whatever language you’re comfortable with to get forecasts.

4.1 Forecasting Confirmed COVID-19 Cases Worldwide with Prophet (Base model)

Generating a week ahead forecast of confirmed cases of COVID-19 using Prophet, with a 95% prediction interval by creating a base model with no tweaking of seasonality-related parameters and additional regressors.

confirmed_forecast_plot =m.plot_components(forecast)

4.2 Forecasting Worldwide Deaths using Prophet (Base model)

Generating a week ahead forecast of confirmed cases of COVID-19 using the Machine Learning library – Prophet, with 95% prediction interval by creating a base model with no tweaking of seasonality-related parameters and additional regressors.

deaths.columns = ['ds','y']
deaths['ds'] = pd.to_datetime(deaths['ds'])
m = Prophet(interval_width=0.95)
m.fit(deaths)
future = m.make_future_dataframe(periods=7)
future.tail()

forecast = m.predict(future)
forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()

deaths_forecast_plot = m.plot(forecast)

4.3 Forecasting Worldwide Recovered Cases with Prophet (Base model)

Generating a week ahead forecast of confirmed cases of COVID-19 using Prophet, with 95% prediction interval by creating a base model with no tweaking of seasonality-related parameters and additional regressors.

recovered.columns = ['ds','y']
recovered['ds'] = pd.to_datetime(recovered['ds'])
m = Prophet(interval_width=0.95)
m.fit(recovered)
future = m.make_future_dataframe(periods=7)
future.tail()


forecast = m.predict(future)
forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()


recovered_forecast_plot = m.plot(forecast)