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