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

Jaspreet  Kaur; Pallavi  Chattopadhyay; Lakhwinder  Singh; Kausik  Chattopadhyay; Nitin Mishra

doi:10.13052/jgeu0975-1416.922

Jaspreet Kaur Department of Computer Science and Engineering, Sri Guru Granth Sahib World University, Fatehgarh Sahib, Punjab, India
Pallavi Chattopadhyay Department of Earth Sciences, Indian Institute of Technology Roorkee, Roorkee, India
Lakhwinder Singh Department of Water Resource Development and Management, Indian Institute of Technology Roorkee, Roorkee, 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

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.

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.

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.

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 Journals, 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

References

Barmparis, G. D., and Tsironis, G. P. (2020). Estimating the infection horizon of COVID-19 in eight countries with a data-driven approach. Chaos, Solitons and Fractals, 135, 109842. https://doi.org/10.1016/j.chaos.2020.109842

Chakraborty, T., and Ghosh, I. (2020). Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis. MedRxiv, 135, 2020.04.09.20059311. https://doi.org/10.1101/2020.04.09.20059311

Chatterjee, K., Chatterjee, K., Kumar, A., and Shankar, S. (2020). Healthcare impact of COVID-19 epidemic in India: A stochastic mathematical model. Medical Journal Armed Forces India, 76(2), 147–155. https://doi.org/10.1016/j.mjafi.2020.03.022

Culp, W. C. (2020). Coronavirus Disease 2019. A & A Practice, 14(6), e01218. https://doi.org/10.1213/xaa.0000000000001218

Ghosh, A., Gupta, R., and Misra, A. (2020). Telemedicine for diabetes care in India during COVID19 pandemic and national lockdown period: Guidelines for physicians. Diabetes and Metabolic Syndrome: Clinical Research and Reviews, 14(4), 273–276. https://doi.org/10.1016/j.dsx.2020.04.001

Hassanien, A. E., Mahdy, L. N., Ezzat, K. A., Elmousalami, H. H., and Ella, H. A. (2020). Automatic X-ray COVID-19 Lung Image Classification System based on Multi-Level Thresholding and Support Vector Machine. MedRxiv, 2020.03.30.20047787. https://doi.org/10.1101/2020.03.30.20047787

Li, L., Yang, Z., Dang, Z., Meng, C., Huang, J., Meng, H., Wang, D., Chen, G., Zhang, J., Peng, H., and Shao, Y. (2020). Propagation analysis and prediction of the COVID-19. Infectious Disease Modelling, 5, 282–292. https://doi.org/10.1016/j.idm.2020.03.002

Magee, L. (2016). Nonlocal Behavior in Polynomial Regressions Author ( s ): Lonnie Magee Published by: Taylor & Francis , Ltd . on behalf of the American Statistical Association Stable URL: http://www.jstor.org/stable/2685560 Nonlocal Behavior in Polynomial Regressions. 52(1), 20–22.

Mahendra Dev, S., and Sengupta, R. (2020). Covid-19: Impact on the Indian Economy. April. https://time.com/5818819/imf-coronavirus-economic-collapse/

Pandey, G., Chaudhary, P., Gupta, R., and Pal, S. (2019). SEIR and Regression Model based COVID-19 outbreak predictions in India. 1–10.

Paul, A., Chatterjee, S., and Bairagi, N. (2020). Prediction on Covid-19 epidemic for different countries: Focusing on South Asia under various precautionary measures. MedRxiv, March, 2020.04.08.20055095. https://doi.org/10.1101/2020.04.08.20055095

Petropoulos, F., and Makridakis, S. (2020). Forecasting the novel coronavirus COVID-19. PLoS ONE, 15(3), 1–8. https://doi.org/10.1371/journal.pone.0231236

Ray, D., Salvatore, M., Bhattacharyya, R., Wang, L., Mohammed, S., Purkayastha, S., Halder, A., Rix, A., Barker, D., Kleinsasser, M., Zhou, Y., Song, P., Bose, D., Banerjee, M., Baladandayuthapani, V., Ghosh, P., and Mukherjee, B. (2020). Predictions, role of interventions and effects of a historic national lockdown in India’s response to the COVID-19 pandemic: data science call to arms. MedRxiv, 2020.04.15.20067256. https://doi.org/10.1101/2020.04.15.20067256

Remuzzi, A., and Remuzzi, G. (2020). Health Policy COVID-19 and Italy: what next? The Lancet, 395(10231), 1225–1228. https://doi.org/10.1016/S0140-6736(20)30627-9

Ribeiro, M. H. D. M., da Silva, R. G., Mariani, V. C., and Coelho, L. dos S. (2020). Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil. Chaos, Solitons & Fractals, 135, 109853. https://doi.org/10.1016/j.chaos.2020.109853

Roda, W. C., Varughese, M. B., Han, D., and Li, M. Y. (2020). Why is it difficult to accurately predict the COVID-19 epidemic? Infectious Disease Modelling, 5, 271–281. https://doi.org/10.1016/j.idm.2020.03.001

Roosa, K., Lee, Y., Luo, R., Kirpich, A., Rothenberg, R., Hyman, J. M., Yan, P., and Chowell, G. (2020). Real-time forecasts of the COVID-19 epidemic in China from February 5th to February 24th, 2020. Infectious Disease Modelling, 5, 256–263. https://doi.org/10.1016/j.idm.2020.02.002

Sarkar, K., Khajanchi, S., and Nieto, J. J. (2020). Modeling and forecasting the COVID-19 pandemic in India. Chaos, Solitons and Fractals, 139, 1–16. https://doi.org/10.1016/j.chaos.2020.110049

Singh, S., Parmar, K. S., Kumar, J., and Makkhan, S. J. S. (2020). Development of New Hybrid Model of Discrete Wavelet Decomposition and Autoregressive Integrated Moving Average (ARIMA) Models in Application to One Month Forecast the Casualties Cases of COVID-19. Chaos, Solitons, and Fractals, 135, 109866. https://doi.org/10.1016/j.chaos.2020.109866

Singhal, A., Singh, P., Lall, B., and Joshi, S. D. (2020). Modeling and prediction of COVID-19 pandemic using Gaussian mixture model. Chaos, Solitons and Fractals, 138, 110023. https://doi.org/10.1016/j.chaos.2020.110023

Singhal, T. (2020). A Review of Coronavirus Disease-2019 (COVID-19). Indian Journal of Pediatrics, 87(4), 281–286. https://doi.org/10.1007/s12098-020-03263-6

Tomar, A., and Gupta, N. (2020). Prediction for the spread of COVID-19 in India and effectiveness of preventive measures. Science of the Total Environment, 728, 138762. https://doi.org/10.1016/j.scitotenv.2020.138762