An In-Depth Review on COVID-19 Infection Count Prediction Using Deep Learning

References

[1] C. Moremada, C. Sandeepa, N. Dissanayaka, T. Gamage and M. Liyanage, "Energy efficient contact tracing and social interaction based patient prediction system for COVID-19 pandemic," in Journal of Communications and Networks, vol. 23, no. 5, pp. 390-407, Oct. 2021, doi: 10.23919/JCN.2021.000037.

[2] A. Ramchandani, C. Fan and A. Mostafavi, "DeepCOVIDNet: An Interpretable Deep Learning Model for Predictive Surveillance of COVID-19 Using Heterogeneous Features and Their Interactions," in IEEE Access, vol. 8, pp. 159915-159930, 2020, doi: 10.1109/ACCESS.2020.3019989.

[3] J. Tetteroo, M. Baratchi and H. H. Hoos, "Automated Machine Learning for COVID-19 Forecasting," in IEEE Access, vol. 10, pp. 94718-94737, 2022, doi: 10.1109/ACCESS.2022.3202220.

[4] O. Tutsoy, A. Polat, Ş. Çolak and K. Balikci, "Development of a Multi-Dimensional Parametric Model With Non-Pharmacological Policies for Predicting the COVID-19 Pandemic Casualties," in IEEE Access, vol. 8, pp. 225272-225283, 2020, doi: 10.1109/ACCESS.2020.3044929.

[5] Z. Yu, X. Zheng, Z. Yang, B. Lu, X. Li and M. Fu, "Interaction-Temporal GCN: A Hybrid Deep Framework For Covid-19 Pandemic Analysis," in IEEE Open Journal of Engineering in Medicine and Biology, vol. 2, pp. 97-103, 2021, doi: 10.1109/OJEMB.2021.3063890.

[6] R. G. Babukarthik, V. A. K. Adiga, G. Sambasivam, D. Chandramohan and J. Amudhavel, "Prediction of COVID-19 Using Genetic Deep Learning Convolutional Neural Network (GDCNN)," in IEEE Access, vol. 8, pp. 177647-177666, 2020, doi: 10.1109/ACCESS.2020.3025164.

[7] P. Wu et al., "An Effective Machine Learning Approach for Identifying Non-Severe and Severe Coronavirus Disease 2019 Patients in a Rural Chinese Population: The Wenzhou Retrospective Study," in IEEE Access, vol. 9, pp. 45486-45503, 2021, doi: 10.1109/ACCESS.2021.3067311.

[8] V. Martsenyuk, M. Bernas and A. Klos-Witkowska, "Two-Strain COVID-19 Model Using Delayed Dynamic System and Big Data," in IEEE Access, vol. 9, pp. 113866-113878, 2021, doi: 10.1109/ACCESS.2021.3104519.

[9] R. Niu, E. W. M. Wong, Y. -C. Chan, M. A. Van Wyk and G. Chen, "Modeling the COVID-19 Pandemic Using an SEIHR Model With Human Migration," in IEEE Access, vol. 8, pp. 195503-195514, 2020, doi: 10.1109/ACCESS.2020.3032584.

[10] K. Narayan, H. Rathore and F. Znidi, "Using Epidemic Modeling, Machine Learning and Control Feedback Strategy for Policy Management of COVID-19," in IEEE Access, vol. 10, pp. 98244-98258, 2022, doi: 10.1109/ACCESS.2022.3206790.

[11] E. Karaçuha et al., "Modeling and Prediction of the Covid-19 Cases With Deep Assessment Methodology and Fractional Calculus," in IEEE Access, vol. 8, pp. 164012-164034, 2020, doi: 10.1109/ACCESS.2020.3021952.

[12] O. Tutsoy, Ş. Çolak, A. Polat and K. Balikci, "A Novel Parametric Model for the Prediction and Analysis of the COVID-19 Casualties," in IEEE Access, vol. 8, pp. 193898-193906, 2020, doi: 10.1109/ACCESS.2020.3033146.

[13] F. Rustam et al., "COVID-19 Future Forecasting Using Supervised Machine Learning Models," in IEEE Access, vol. 8, pp. 101489-101499, 2020, doi: 10.1109/ACCESS.2020.2997311.

[14] L. Wang et al., "A Time-Series Feature-Based Recursive Classification Model to Optimize Treatment Strategies for Improving Outcomes and Resource Allocations of COVID-19 Patients," in IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 7, pp. 3323-3329, July 2022, doi: 10.1109/JBHI.2021.3139773.

[15] V. K. Gupta, A. Gupta, D. Kumar and A. Sardana, "Prediction of COVID-19 confirmed, death, and cured cases in India using random forest model," in Big Data Mining and Analytics, vol. 4, no. 2, pp. 116-123, June 2021, doi: 10.26599/BDMA.2020.9020016.

@article{1703970,
      author = {Maithali S. Patil, Prof. Bhagyashree Dhakulkar},
      title = {An In-Depth Review on COVID-19 Infection Count Prediction Using Deep Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {6},
      pages = {262-267},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703970.pdf},
      abstract = {The COVID-19 pandemic has been one of the most defining factor of the recent decade due to its extreme impact on the world and the people within it. The coronavirus caused a lot of problems at the personal level as well as the economic level. There have been massive losses of life and the governments across the world have been hit very badly and are yet to recover from this pandemic. The main problem that has led to the escalation of the pandemic has been the fact that it was unexpected. The lack of highly accurate prediction approaches led to the medical infrastructure not being able to handle the load of patients. Therefore, there is a need for a methodology that can predict the COVID patient count to adequately prepare the healthcare professionals in advance. The proposed methodology utilizes deep learning in the form of Convolutional Neural Networks and Long Short Term Memory to achieve the prediction of COVID-19 infection count. This approach will be elaborated further in the upcoming editions of this research. },
      keywords = {Convolutional Neural Networks-Long Short-Term Memory, Decision Tree.},
      month = {December},
  }