COVID-19 trend forecasting and visualising with Facebook Prophet

A simple Python script (Jupyter Notebook : COVID19_Trend.ipynb) to forcast COVID-19 daily cases for a given country using Facebook Prophet and "Our World in Data" data source (continuously up-to-date).

Initial Parameters

Please set initial paramerters as required.

1. Set data source : data_source = 'https://covid.ourworldindata.org/data/owid-covid-data.csv'
2. Select Country : show_country = 'Spain'
3. Adjusting trend flexibility : trend_flexibility = 1.0
4. Set number of forcasting days : days_forcasting = 90
5. Set number of previous days to be considered for forcasting : last_days_count = 30

Requirements

1. Facebook Prophet : "install -c conda-forge fbprophet"
2. Plotly : "conda install -c plotly plotly"
3. Pandas : "conda install -c conda-forge pandas"
4. Matplotlib : "conda install -c conda-forge matplotlib"

Credits

1. "Our World in Data" and its respective authors for providing up-to date data at https://github.com/owid/covid-19-data/tree/master/public/data
2. Facebook, Inc. and its affiliates for "Facebook Prophet" library at https://github.com/facebook/prophet
3. Authors and affiliates of all other libraries and technologies (including Python, Pandas, Plotly and Matplotlib) used in this script.

References

1. Facebook Prophet : https://facebook.github.io/prophet/docs/quick_start.html#python-api
2. "Our World in Data" Data Source CSV : https://github.com/owid/covid-19-data/blob/master/public/data/owid-covid-data.csv
3. Trend Flexibility : https://facebook.github.io/prophet/docs/trend_changepoints.html#automatic-changepoint-detection-in-prophet

Sponsor

DataDisca Pty Ltd, Melbourne, Australia

https://www.datadisca.com