Machine Learning Based Prediction and Impact Analysis of Various Lockdown Stages of COVID-19 Outbreak – A Case Study of India

Authors

  • Lakhwinder Singh Department of Water Resource Development and Management, Indian Institute of Technology Roorkee, Roorkee, India
  • Pallavi Chattopadhyay Department of Earth Sciences, Indian Institute of Technology Roorkee, Roorkee, India
  • Jaspreet Kaur Department of Computer Science and Engineering, Sri Guru Granth Sahib World University, Fatehgarh Sahib, Punjab, India
  • Kausik Chattopadhyay Technical Officer III, ICC, Indian Institute of Technology Roorkee, Roorkee, India
  • Nitin Mishra Department of Civil Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

DOI:

https://doi.org/10.13052/jgeu0975-1416.922

Keywords:

COVID-19, machine learning, SVM, SIR model, lockdown predictions.

Abstract

Various measures have been taken into account for the virus outbreak. But
how much it successes to control outbreak to fights against COVID-19.
Machine learning is used as a tool to study these complex impacts on various
stages of the epidemic. While India is forced to open up the economy after
an extended lockdown, the effect of lockdown, which is critical to decide the
future course of action, is yet to be understood. The study suggests Support
Vector Machine (SVM) and Polynomial Regression (PR) are better suited compared to Long Short-Term Memory (LSTM) in scenarios consisting of
sparse and discrete events. The time-series memory of LSTM is outperformed
by the contextual hyperplanes of SVM which classifies the data even more
precisely. The study suggests while phase 1 of lockdown was effective, the
rest of them were not. Had India continued with lockdown 1, it would have
flattened the COVID-19 infection curve by mid of May 2020. With the current
rate, India will hit the 8 million mark by 23 October 2020. The SVM model is
further integrated with an SIR (Susceptible, Infected and Recovered) model
of epidemiology, which suggests that 70% of India’s population is infected
by this pandemic during this 8 month and the peak reached in October 2020
if vaccine not found. With increasing recovery rate increases the possibility
of decreasing COVID-19 cases. According to the SVM model’s prediction,
90% of cases of COVID-19 will be end in February.

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Author Biographies

Lakhwinder Singh, Department of Water Resource Development and Management, Indian Institute of Technology Roorkee, Roorkee, India

Lakhwinder Singh has wide expertise in Remote sensing and GIS. Area of
Interest includes Machine Learning, Climate change and Hydrology. He has
developed number of models for GIS. Having wide experience in number
of Govt. of India Projects. Doing PhD at IIT Roorkee. Completed M.Tech
in GIS in 2010, from BDU, Tiruchirappalli. He has number of technology
followers on social media. Received International Award for scientific and
online support contribution to the SWAT model at the 2018 from Texas A&M
University, USA and USDA at Brussels, Belgium.

Pallavi Chattopadhyay, Department of Earth Sciences, Indian Institute of Technology Roorkee, Roorkee, India

Pallavi Chattopadhyay is working as Assistant professor in the Department
of Earth Sciences, Indian Institute of Technology Roorkee, Roorkee, India.
She is an expert in HydroGeophysics. Her research interests are Near surface
Geophysics and Sub surface geological controls. She has more than 10 years
of experience in Artificial Intelligence and Machine Learning and has worked
in various roles in India and in USA.

Jaspreet Kaur, Department of Computer Science and Engineering, Sri Guru Granth Sahib World University, Fatehgarh Sahib, Punjab, India

Jaspreet Kaur is Bachelor of Technology (B.Tech) student in Computer
science and Engineering at Sri Guru Granth Sahib World University, Punjab,
India. She has wide interest in application development. She developed
number of applications including Android apps and games. Her expertise
includes C, C++, Python, Java and TensorFlow. She focused on Machine
Learning based application and algorithms development and successfully
implemented it.

Kausik Chattopadhyay, Technical Officer III, ICC, Indian Institute of Technology Roorkee, Roorkee, India

Kausik Chattopadhyay is currently working as Technical Officer III, ICC,
at Indian Institute of Technology Roorkee. He has more than 20 years of
experience in Software Development, Cyber Security, Big Data & Machine
Learning. He has worked in various multinational companies in India as well
as in USA. He holds a BTech in Computer Science and an MBA in Finance.

Nitin Mishra, Department of Civil Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

Nitin Mishra born in Roorkee, Haridwar District in 1979. He received
the B.E. in Agricultural Engg. (2002) and M.Tech. in Irrigation Water
Management (IIT Roorkee, 2013), Perusing PhD from Department of Civil
Engineering, Graphic Era Deemed to be University, Dehradun, India. He is
presently working as Assistant Professor Department of Civil Engineering,
Graphic Era Deemed to be University, Dehradun 2013 onward. Nitin Mishra
has published about 91 research papers in National and International Jour-
nals, Conferences and Book Chapters. He has guided 14 M.Tech and has
conducted 15 Expert Lecture/Seminars/Workshops/Conferences. He received
07 Awards for his contribution in Teaching and Research. His main area of
research interest is Irrigation Water Management, Climate Change, Remote
Sensing and GIS Applications in Water Resources with a focus on efficient
water resources management. He is an Editorial Board Member and Reviewer
of many reputed international and national journals

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Published

2021-06-09

How to Cite

Singh, L., Chattopadhyay, P., Kaur, J., Chattopadhyay, K., & Mishra, N. (2021). Machine Learning Based Prediction and Impact Analysis of Various Lockdown Stages of COVID-19 Outbreak – A Case Study of India. Journal of Graphic Era University, 9(2), 121–136. https://doi.org/10.13052/jgeu0975-1416.922

Issue

2021: Vol 9 Iss 2 2021

Section

Articles