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Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study

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  1. Shruti Sharma & Yogesh Kumar Gupta & Abhinava K. Mishra, 2023. "Analysis and Prediction of COVID-19 Multivariate Data Using Deep Ensemble Learning Methods," IJERPH, MDPI, vol. 20(11), pages 1-23, May.
  2. Medeiros, Marcelo C. & Street, Alexandre & Valladão, Davi & Vasconcelos, Gabriel & Zilberman, Eduardo, 2022. "Short-term Covid-19 forecast for latecomers," International Journal of Forecasting, Elsevier, vol. 38(2), pages 467-488.
  3. Rasheed, Jawad & Jamil, Akhtar & Hameed, Alaa Ali & Aftab, Usman & Aftab, Javaria & Shah, Syed Attique & Draheim, Dirk, 2020. "A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
  4. Schaum, A. & Bernal-Jaquez, R. & Alarcon Ramos, L., 2022. "Data-assimilation and state estimation for contact-based spreading processes using the ensemble kalman filter: Application to COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
  5. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
  6. Faik Bilgili & Emrah Koçak & Sevda Kuşkaya, 2023. "Dynamics and Co-movements Between the COVID-19 Outbreak and the Stock Market in Latin American Countries: An Evaluation Based on the Wavelet-Partial Wavelet Coherence Model," Evaluation Review, , vol. 47(4), pages 630-652, August.
  7. Christian Acal & Manuel Escabias & Ana M. Aguilera & Mariano J. Valderrama, 2021. "COVID-19 Data Imputation by Multiple Function-on-Function Principal Component Regression," Mathematics, MDPI, vol. 9(11), pages 1-23, May.
  8. Khan, Junaid Iqbal & Ullah, Farman & Lee, Sungchang, 2022. "Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
  9. Farrukh Saleem & Abdullah Saad AL-Malaise AL-Ghamdi & Madini O. Alassafi & Saad Abdulla AlGhamdi, 2022. "Machine Learning, Deep Learning, and Mathematical Models to Analyze Forecasting and Epidemiology of COVID-19: A Systematic Literature Review," IJERPH, MDPI, vol. 19(9), pages 1-18, April.
  10. Galasso, Joseph & Cao, Duy M. & Hochberg, Robert, 2022. "A random forest model for forecasting regional COVID-19 cases utilizing reproduction number estimates and demographic data," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
  11. Antoni Wiliński & Łukasz Kupracz & Aneta Senejko & Grzegorz Chrząstek, 2022. "COVID-19: average time from infection to death in Poland, USA, India and Germany," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(6), pages 4729-4746, December.
  12. Xiaojin Xie & Kangyang Luo & Zhixiang Yin & Guoqiang Wang, 2021. "Nonlinear Combinational Dynamic Transmission Rate Model and Its Application in Global COVID-19 Epidemic Prediction and Analysis," Mathematics, MDPI, vol. 9(18), pages 1-17, September.
  13. Masum, Mohammad & Masud, M.A. & Adnan, Muhaiminul Islam & Shahriar, Hossain & Kim, Sangil, 2022. "Comparative study of a mathematical epidemic model, statistical modeling, and deep learning for COVID-19 forecasting and management," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
  14. Middya, Asif Iqbal & Roy, Sarbani, 2022. "Spatio-temporal variation of Covid-19 health outcomes in India using deep learning based models," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
  15. Behzad Vahedi & Morteza Karimzadeh & Hamidreza Zoraghein, 2021. "Spatiotemporal prediction of COVID-19 cases using inter- and intra-county proxies of human interactions," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
  16. Harrou, Fouzi & Dairi, Abdelkader & Kadri, Farid & Sun, Ying, 2020. "Forecasting emergency department overcrowding: A deep learning framework," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
  17. Jiří Mazurek, 2021. "The evaluation of COVID-19 prediction precision with a Lyapunov-like exponent," PLOS ONE, Public Library of Science, vol. 16(5), pages 1-9, May.
  18. Kalantari, Mahdi, 2021. "Forecasting COVID-19 pandemic using optimal singular spectrum analysis," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
  19. Huang, Chiou-Jye & Shen, Yamin & Kuo, Ping-Huan & Chen, Yung-Hsiang, 2022. "Novel spatiotemporal feature extraction parallel deep neural network for forecasting confirmed cases of coronavirus disease 2019," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
  20. Yu-Chih Wei & Yan-Ling Ou & Jianqiang Li & Wei-Chen Wu, 2022. "Forecasting the Potential Number of Influenza-like Illness Cases by Fusing Internet Public Opinion," Sustainability, MDPI, vol. 14(5), pages 1-24, February.
  21. Yongchao Jin & Renfang Wang & Xiaodie Zhuang & Kenan Wang & Honglian Wang & Chenxi Wang & Xiyin Wang, 2022. "Prediction of COVID-19 Data Using an ARIMA-LSTM Hybrid Forecast Model," Mathematics, MDPI, vol. 10(21), pages 1-13, October.
  22. Yu Fu & Shaofu Lin & Zhenkai Xu, 2022. "Research on Quantitative Analysis of Multiple Factors Affecting COVID-19 Spread," IJERPH, MDPI, vol. 19(6), pages 1-13, March.
  23. Ahed Abugabah & Farah Shahid, 2023. "Intelligent Health Care and Diseases Management System: Multi-Day-Ahead Predictions of COVID-19," Mathematics, MDPI, vol. 11(4), pages 1-19, February.
  24. Faizeh Hatami & Shi Chen & Rajib Paul & Jean-Claude Thill, 2022. "Simulating and Forecasting the COVID-19 Spread in a U.S. Metropolitan Region with a Spatial SEIR Model," IJERPH, MDPI, vol. 19(23), pages 1-16, November.
  25. Juyoung Song & Dal-Lae Jin & Tae Min Song & Sang Ho Lee, 2023. "Exploring Future Signals of COVID-19 and Response to Information Diffusion Using Social Media Big Data," IJERPH, MDPI, vol. 20(9), pages 1-11, May.
  26. Iloanusi, Ogechukwu & Ross, Arun, 2021. "Leveraging weather data for forecasting cases-to-mortality rates due to COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
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