FOCOMO: Forecasting and monitoring the worldwide spread of COVID-19 using machine learning methods

References

• F. Jiang, L. Deng, L. Zhang, Y. Cai, C. W. Cheung, and Z. Xia, “Review of the clinical characteristics of coronavirus disease 2019 (COVID-19),” J. Gen. Intern. Med., pp. 1–5, 2020.
   PubMed Web of Science ®Google Scholar
• Haleem, M. Javaid, and R. Vaishya, “Effects of COVID 19 pandemic in daily life,” Curr. Med. Res. Pract., 2020.
   PubMedGoogle Scholar
• K. Biswas and P. Sen, “Space-time dependence of corona virus (COVID-19) outbreak,” arXiv Prepr. arXiv2003.03149, 2020.
   Google Scholar
• S. Makridakis, A. Wakefield, R. Kirkham, and others, “Predicting medical risks and appreciating uncertainty,” Foresight Int. J. Appl. Forecast., no. 52, pp. 28–35, 2019.
   Google Scholar
• R. J. Hyndman, A. B. Koehler, R. D. Snyder, and S. Grose, “A state space framework for automatic forecasting using exponential smoothing methods,” Int. J. Forecast., vol. 18, no. 3, pp. 439–454, 2002. doi: 10.1016/S0169-2070(01)00110-8
   Web of Science ®Google Scholar
• J. W. Taylor, “Exponential smoothing with a damped multiplicative trend,” Int. J. Forecast., vol. 19, no. 4, pp. 715–725, 2003. doi: 10.1016/S0169-2070(03)00003-7
   Web of Science ®Google Scholar
• S. Makridakis et al., “The accuracy of extrapolation (time series) methods: Results of a forecasting competition,” J. Forecast., vol. 1, no. 2, pp. 111–153, 1982. doi: 10.1002/for.3980010202
   Web of Science ®Google Scholar
• S. Makridakis and M. Hibon, “The M3-Competition: results, conclusions and implications,” Int. J. Forecast., vol. 16, no. 4, pp. 451– 476, 2000. doi: 10.1016/S0169-2070(00)00057-1
   Web of Science ®Google Scholar
• T. Chakraborty and I. Ghosh, “Real-time forecasts and risk assessment of novel coronavirus (COVID-19) cases: A data-driven analysis,” Chaos, Solitons & Fractals, p. 109850, 2020. doi: 10.1016/j.chaos.2020.109850
   PubMed Web of Science ®Google Scholar
• S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “The M4 Competition: 100,000 time series and 61 forecasting methods,” Int. J. Forecast., vol. 36, no. 1, pp. 54–74, 2020. doi: 10.1016/j.ijforecast.2019.04.014
   Web of Science ®Google Scholar
• L. Fang, G. Karakiulakis, and M. Roth, “Are patients with hypertension and diabetes mellitus at increased risk for COVID-19 infection?,” Lancet. Respir. Med., vol. 8, no. 4, p. e21, 2020. doi: 10.1016/S2213-2600(20)30116-8
   Web of Science ®Google Scholar
• J. Norman, Y. Bar-Yam, and N. N. Taleb, “Systemic risk of pandemic via novel pathogens—Coronavirus: A note,” New Engl. Complex Syst. Inst. (January 26, 2020), 2020.
   Google Scholar
• G. S. Randhawa, M. P. M. Soltysiak, H. El Roz, C. P. E. de Souza, K. A. Hill, and L. Kari, “Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study,” PLoS One, vol. 15, no. 4, p. e0232391, 2020. doi: 10.1371/journal.pone.0232391
   PubMed Web of Science ®Google Scholar
• Y. Wang, M. Hu, Q. Li, X.-P. Zhang, G. Zhai, and N. Yao, “Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner,” arXiv Prepr. arXiv2002.05534, 2020.
   Google Scholar
• P. Zhou et al., “A pneumonia outbreak associated with a new coronavirus of probable bat origin,” Nature, vol. 579, no. 7798, pp. 270–273, 2020. doi: 10.1038/s41586-020-2012-7
   PubMed Web of Science ®Google Scholar
• Alimadadi, S. Aryal, I. Manandhar, P. B. Munroe, B. Joe, and X. Cheng, “Artificial intelligence and machine learning to fight COVID-19.” American Physiological Society Bethesda, MD, 2020.
   Google Scholar
• Y. Song, J. Jiang, X. Wang, D. Yang, and C. Bai, “Prospect and application of Internet of Things technology for prevention of SARIs,” Clin. eHealth, vol. 3, pp. 1–4, 2020. doi: 10.1016/j.ceh.2020.02.001
   Google Scholar
• F. Petropoulos and S. Makridakis, “Forecasting the novel coronavirus COVID-19,” PLoS One, vol. 15, no. 3, p. e0231236, 2020. doi: 10.1371/journal.pone.0231236
   PubMed Web of Science ®Google Scholar
• D. Liu et al., “A machine learning methodology for real-time forecasting of the 2019-2020 COVID-19 outbreak using Internet searches, news alerts, and estimates from mechanistic models,” arXiv Prepr. arXiv2004.04019, 2020.
   Google Scholar
• E. O. Nsoesie, J. S. Brownstein, N. Ramakrishnan, and M. V Marathe, “A systematic review of studies on forecasting the dynamics of influenza outbreaks,” Influenza Other Respi. Viruses, vol. 8, no. 3, pp. 309–316, 2014.
   Google Scholar
• Pirouz, S. Shaffiee Haghshenas, S. Shaffiee Haghshenas, and P. Piro, “Investigating a serious challenge in the sustainable development process: analysis of confirmed cases of COVID-19 (new type of coronavirus) through a binary classification using artificial intelligence and regression analysis,” Sustainability, vol. 12, no. 6, p. 2427, 2020. doi: 10.3390/su12062427
   Web of Science ®Google Scholar
• G. D. More, M. Dunowska, E. Acke, and N. J. Cave, “A serological survey of canine respiratory coronavirus in New Zealand”, N. Z. Vet. J., vol. 68, no. 1, pp. 54–59, 2020. doi: 10.1080/00480169.2019.1667282
   PubMed Web of Science ®Google Scholar
• COVID-19 dataset as per WHO, https://covid19.who.int/, Accessed: 2020 July 01.
   Google Scholar
• COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, https://github.com/CSSEGISandData/COVID-19, Accessed: 2020 July 01.
   Google Scholar
• Kumari, R., Kumar, S., Poonia, R. C., Singh, V., Raja, L., Bhatnagar, V., & Agarwal, P. (2020). Analysis and Predictions of Spread, Recovery, and Death Caused by COVID-19 in India. Big Data Mining and Analytics, IEEE.
   Google Scholar
• Bhatnagar, V., Poonia, R. C., Nagar, P., Kumar, S., Singh, V., Raja, L., & Dass, P. (2020). Descriptive analysis of COVID-19 patients in the context of India. Journal of Interdisciplinary Mathematics, 1-16. doi: 10.1080/09720502.2020.1761635
   Web of Science ®Google Scholar
• Singh, V., Poonia, R. C., Kumar, S., Dass, P., Agarwal, P., Bhatnagar, V., & Raja, L. (2020). Prediction of COVID-19 Coronavirus pandemic based on time series data using Support Vector Machine. Journal of Discrete Mathematical Sciences & Cryptography.
   Web of Science ®Google Scholar
• Ratnadip Adhikari, R. K. Agrawal, “An Introductory Study on Time Series Modeling and Forecasting”, ArXiv https://arxiv.org/ftp/arxiv/papers/1302/1302.6613.pdf
   Google Scholar
• Taylor SJ, Letham B., “Forecasting at scale”, Peer J Preprints 5:e3190v2, 2017 https://doi.org/10.7287/peerj.preprints.3190v2
   Google Scholar
• Chenghao Liu, Steven C. H. Hoi, Peilin Zhao, Jianling Sun, “Online ARIMA Algorithms for Time Series Prediction”, Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16), pp 1867-1873, 2016.
   Google Scholar
• Eimutis Valakevicius, Mindaugas Bražėnas, “Application of Seasonal Holt-Winter model for the prediction of exchange rate volatility”, Engineering Economics, 2015, 26(4), 384–390. doi: 10.5755/j01.ee.26.4.5210
   Google Scholar
• Bhatnagar, Vaibhav, and Ramesh C. Poonia. “Design of prototype model for irrigation based decision support system.” Journal of Information and Optimization Sciences 39.7 (2018): 1607-1612. doi: 10.1080/02522667.2018.1507763
   Web of Science ®Google Scholar
• Bhatnagar, Vaibhav, and Ramesh C. Poonia. “A prototype model for decision support system of NPK fertilization.” Journal of Statistics and Management Systems 21.4 (2018): 631-638. doi: 10.1080/09720510.2018.1471266
   Web of Science ®Google Scholar
• Kumar, Ankit, et al. “An enhanced quantum key distribution protocol for security authentication.” Journal of Discrete Mathematical Sciences and Cryptography 22.4 (2019): 499-507. doi: 10.1080/09720529.2019.1637154
   Web of Science ®Google Scholar