[1] World Health Organization. Coronavirus Disease 2019 (COVID-19) Situation Report – 28.Who.int.2020. Available from: https://www.who.int/docs/defaultsource/coronaviruse/situation-reports/20200217-sitrep-28-covid-19.pdf. [Accessed 18 February 2020].

[2] A. S. Albahri, Rula A. Hamid, Jwan k. Alwan, Z.T. Al-qays, A. A. Zaidan, B. B. Zaidan, A O. S. Albahri, A. H. AlAmoodi, Jamal Mawlood Khlaf, E. M. Almahdi, Eman Thabet, Suha M. Hadi, K I. Mohammed, M. A. Alsalem, Jameel R. Al-Obaidi, H.T. Madhloom, Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review, J Med Syst. 2020; 44(7): 122. Published online 2020 May 25. doi: 10.1007/s10916-020-01582-x

[3]Akhtar, M., Kraemer, M., and Gardner, L.: A Dynamic Neural Network Model for Predicting Risk of Zika in Real Time. BMC Medicine, 17(171) (2019). https://doi.org/10.1186/s12916-019-1389-3.

[4] L. Huang, R. Han, T. Ai, P. Yu, H. Kang, Q. Tao, et al., Serial quantitative chest CT assessment of COVID-19: Deep-Learning Approach, Radiology: Cardiothoracic Imaging, vol. 2, p. e200075, 2020.

[5] Xi. Yang, Xu. He, J. Zhao, Y. Zhang , Sh. Zhang, et al., COVID-CT-Dataset: a CT scan dataset about COVID-19, arXiv preprint arXiv:2003.13865, 2020.

[6] L. Zhang, Xu. Kong, Xi. Li, P. Yu, H. Kang, Q. Tao, et al., CT imaging features of 34 patients infected with COVID-19, Radiology: Cardiothoracic Imaging, vol. 2, p. e200075, 2020.

[7] Ying Song, Shuangjia Zheng, Liang Li, Xiang Zhang, Xiaodong Zhang, Ziwang Huang,Jianwen Chen, Huiying Zhao, Yusheng Jie, Ruixuan Wang, et al. Deep learning enables accurate diagnosis of novel coronavirus (covid-19) with ct images. medRxiv, 2020.

[8] Shuai Wang, Bo Kang, Jinlu Ma, Xianjun Zeng, Mingming Xiao, Jia Guo, Mengjiao Cai, Jingyi Yang, Yaodong Li, Xiangfei Meng, et al. A deep learning algorithm using ct images to screen for corona virus disease (covid-19). medRxiv, 2020.

[9] Feng Shi, Liming Xia, Fei Shan, Dijia Wu, Ying Wei, Huan Yuan, Huiting Jiang, Yaozong Gao, He Sui, and Dinggang Shen. Large-scale screening of covid-19 from community acquired pneumonia using infection size-aware classi_cation. arXiv preprint arXiv:2003.09860, 2020.

[10] A. Narin, C. Kaya, and Z. Pamuk, "Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks," arXiv:2003.10849, 2020.

[11] ] X. Xu, X. Jiang, C. Ma, P. Du, X. Li, S. Lv, et al., "Deep learning system to screen Coronavirus disease 2019 pneumonia,"arXiv:2002.09334, 2020.

[12] E. Hosseini, K. Z. Ghafoor, A. S. Sadiq, M. Guizani and A. Emrouznejad, "COVID-19 Optimizer Algorithm, Modeling and Controlling of Coronavirus Distribution Process," in IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 10, pp. 2765-2775, Oct. 2020, doi: 10.1109/JBHI.2020.3012487.

[13] S. Wang et al., "A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19)," medRxiv, 2020.

[14] Fang Y, Zhang H, Xie J, Lin M, Ying L, Pang P, et al. Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology 2020:200432.

[15] Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, Tao Q, Sun Z, Xia L. Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology (2020).

[16] M.Y. Ng, E.Y.P. Lee, J. Yang, F. Yang, X. Li, H. Wang, et al. Imaging profile of the COVID-19 infection: radiologic findings and literature review. Radiol Cardiothorac Imaging, 2020.

[17] Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 395 (10223):497e506, 2020.

[18] Huang P, Liu T, Huang L, Liu H, Lei M, Xu W, et al. Use of chest CT in combination with negative RT-PCR assay for the 2019 novel coronavirus but high clinical suspicion. Radiology 295(1):22e3, 2020.

[19] Chung M, Bernheim A, Mei X, Zhang N, Huang M, Zeng X, et al. CT imaging features of 2019 novel coronavirus (2019-nCoV). Radiology 295(1):202e7,2020.

[20] Bernheim A, Mei X, Huang M, Yang Y, Fayad ZA, Zhang N, et al. ChestCT findings in coronavirus disease-19 (COVID-19): relationship toduration of infection. Radiology 200463, 2020.

[21] E. Frank, M. A. Hall, I. H. Witten, The WEKA workbench, Online Appendix for ”Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann, Fourth Edition (2016).

[22] Bishop CM. Neural Networks for Pattern Recognition. Oxford. New York, NY, 1995.

[23] Ricco G. Bayesian Vector Autoregressions, with Oxford Research Encyclopedia, Oxford University Press, 2018.

[24] H¨uhn, J., H¨ullermeier, E.: Furia: an algorithm for unordered fuzzy rule induction. Data Mining and Knowledge Discovery 19(3), 293–319, 2009.

[25] Geoff Hulten, Laurie Spencer, Pedro Domingos: Mining time-changing data streams. In: ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 97-106, 2001.

[26] Niels Landwehr, Mark Hall, and Eibe Frank. Logistic model trees (PDF). ECML PKDD, 2003.

[27] Altman, Naomi S. "An introduction to kernel and nearest-neighbour nonparametric regression" (PDF). The American Statistician. 46 (3): 175–185, 1992.

[28] Ziarko, W."The Discovery, Analysis and Representation of Data Dependencies in Databases", Knowledge Discovery in Databases, AAAI MIT Press, Cambridge, MA, pp.213-228, 1993.

[29] Mirzaee E., Esmaeilpour M. A New Hybrid Method to Increase the Prediction in Data Reduced Using Rough Set and Swarm Intelligence Model. JSDP. 14 (3):51-64, 2017.

[30] Chouchoulas and Q. Shen. Rough set-aided keyword reduction for text categorisation. Applied Artificial Intelligence,15(9):843-873, 2001.

[31] Grefenstette, J. J. (1994). Genetic algorithms for machine learning. Boston, MA: Kluwer.

[32] Ren, Y.-G., Wang, Y.: Rough Set Attribute Reduction Algorithm Based on GA. Mini-Micro Systems 27(5), 862–865 (2005)

[33] https://github.com/ieee8023/covid-chestxray-dataset. Available 18. June 2020.