استخدام البيانات الضخمة والذكاء الاصطناعي في مواجهة جائحة فيروس كورونا المستجد

الإسكوا، (اللجنة الاقتصادية والاجتماعية لغربي آسيا). (2020). استجابة إقليمية طارئة للتخفيف من تداعيات الوباء فيروس كورونا https://www.unescwa.org/sites/www.unescwa.org/files/publications/files/20-00114_rer_mitigatingimpact_COVID-19_ar_apr8.pdf

العريشي، جبريل. ( 2019 أ). معايير جودة البيانات، صحيفة رسالة الجامعة. https://rs.ksu.edu.sa/issue-1338/11931

العريشي، جبريل. (2019ب، سبتمبر). إلى أي مدى يمكننا الوثوق بالذكاء الاصطناعي، صحيفة مكة، تم الاسترجاع من موقع. https://makkahnewspaper.com/article/1111015/

العريشي، جبريل. (2020أ، إبريل). دقة وشفافية البيانات الدولية في أزمة كورونا، صحيفة مكة. https://makkahnewspaper.com/article/1508594

العريشي، جبريل. (2020ب، مايو). دور التكنولوجيا في مواجهة جائحة كورونا، صحيفة مكة. https://makkahnewspaper.com/article/1511120/

المراجع الأجنبية

Ahmed, S. F., Quadeer, A. A., & McKay, M. R. (2020). Preliminary identification of potential vaccine targets for the COVID-19 coronavirus (SARS-CoV-2) based on SARS-CoV immunological studies. Viruses, 12(3), 254.

Bragazzi, N. L., Damiani, G., & Martini, M. (2019). From Rheumatology 1.0 to Rheumatology 4.0 and beyond: the contributions of Big Data to the field of rheumatology. Mediterranean journal of rheumatology, 30(1), 3.

Beck, B. R., Shin, B., Choi, Y., Park, S., & Kang, K. (2020). Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Computational and structural biotechnology journal.

Brauer, F., Castillo-Chavez, C., & Castillo-Chavez, C. (2012). Mathematical models in population biology and epidemiology (Vol. 2, p. 508). New York: Springer.

Castorina, P., Iorio, A., & Lanteri, D. (2020). Data analysis on Coronavirus spreading by macroscopic growth laws. International Journal of Modern Physics C, 2050103.

Corey J. A. Bradshaw (2020). Want to make social distancing even more effective? It’s about time (as well as space), MENAFN. https://theconversation.com/want-to-make-social-distancing-even-more-effective-its-about-time-as-well-as-space-134551

Dandekar, R., & Barbastathis, G. (2020). Neural Network aided quarantine control model estimation of COVID spread in Wuhan, China. arXiv preprint arXiv:2003.09403.

Eden, J. S., Rockett, R., Carter, I., Rahman, H., De Ligt, J., Hadfield, J., ... & Wells, J. (2020). An emergent clade of SARS-CoV-2 linked to returned travellers from Iran. Virus Evolution, 6(1), veaa027.

Goldman, D. S. (2020). Initial observations of psychological and behavioral effects of COVID-19 in the United States, using Google trends data.

Gupta, R., Pandey, G., Chaudhary, P., & Pal, S. K. (2020). SEIR and Regression Model based COVID-19 outbreak predictions in India. medRxiv.

Heroy, S. (2020). Metropolitan-scale COVID-19 outbreaks: how similar are they?. arXiv preprint arXiv:2004.01248.

Hou, Z., Du, F., Jiang, H., Zhou, X., & Lin, L. (2020). Assessment of public attention, risk perception, emotional and behavioural responses to the COVID-19 outbreak: social media surveillance in China. Risk Perception, Emotional and Behavioural Responses to the COVID-19 Outbreak: Social Media Surveillance in China (3/6/2020).

Hu, Z., Ge, Q., Jin, L., & Xiong, M. (2020). Artificial intelligence forecasting of COVID-19 in china. arXiv preprint arXiv:2002.07112.

Jin, Y. H., Cai, L., Cheng, Z. S., Cheng, H., Deng, T., Fan, Y. P., ... & Han, Y. (2020). A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version). Military Medical Research, 7(1), 4.

Li, C., Debruyne, D., Spencer, J., Kapoor, V., Liu, L. Y., Zhang, B., ... & Oliva, A. (2020). High sensitivity detection of SARS-CoV-2 using multiplex PCR and a multiplex-PCR-based metagenomic method. bioRxiv..

Li, S., Wang, Y., Xue, J., Zhao, N., & Zhu, T. (2020). The impact of COVID-19 epidemic declaration on psychological consequences: a study on active Weibo users. International journal of environmental research and public health, 17(6), 2032.

Li, Z., Li, X., Huang, Y. Y., Wu, Y., Zhou, L., Liu, R & Zhang, Y. (2020). FEP-based screening prompts drug repositioning against COVID-19. bioRxiv.

McCall, B. (2020). COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread. The Lancet Digital Health, 2(4), e166-e167.

MIN, C. O. W. (2020). Position Statement from the National Centre for Infectious Diseases and the Chapter of Infectious Disease Physicians, Academy of Medicine, Singapore: Period of Infectivity to Inform Strategies for De-isolation for COVID-19 Patients

Notari, A. (2020). Temperature dependence of COVID-19 transmission. arXiv preprint arXiv:2003.12417.

Pacheco Rocha, N., Dias, A., Santinha, G., Rodrigues, M., Queirós, A., & Rodrigues, C. (2019). Smart Cities and Healthcare: A Systematic Review. Technologies, 7(3), 58.

Pham, Q. V., Nguyen, D. C., Hwang, W. J., & Pathirana, P. N. (2020). Artificial Intelligence (AI) and Big Data for Coronavirus (COVID-19) Pandemic: A Survey on the State-of-the-Arts.

Qin, L., Sun, Q., Wang, Y., Wu, K. F., Chen, M., Shia, B. C., & Wu, S. Y. (2020). Prediction of number of cases of 2019 novel coronavirus (COVID-19) using social media search index. International journal of environmental research and public health, 17(7), 2365.

Richardson, P., Griffin, I., Tucker, C., Smith, D., Oechsle, O., Phelan, A., & Stebbing, J. (2020). Baricitinib as potential treatment for 2019-nCoV acute respiratory disease. Lancet (London, England), 395(10223), e30.

Strzelecki, A. (2020). The second worldwide wave of interest in coronavirus since the COVID-19 outbreaks in South Korea, Italy and Iran: a google trends study. arXiv preprint arXiv:2003.10998.

Sun, K., Chen, J., & Viboud, C. (2020). Early epidemiological analysis of the coronavirus disease 2019 outbreak based on crowdsourced data: a population-level observational study. The Lancet Digital Health.

Tátrai, D., & Várallyay, Z. (2020). COVID-19 epidemic outcome predictions based on logistic fitting and estimation of its reliability. arXiv preprint arXiv:2003.1416

Weisstein, Eric W. (2020). “SIR Model.” From MathWorld--A Wolfram

Wong, Z. S., Zhou, J., & Zhang, Q. (2019). Artificial intelligence for infectious disease big data analytics. Infection, disease & health, 24(1), 44-48.

Yang, Z., Zeng, Z., Wang, K., Wong, S. S., Liang, W., Zanin, M., ... & Liang, J. (2020). Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. Journal of Thoracic Disease, 12(3), 165.

Zhang, D. H., Wu, K. L., Zhang, X., Deng, S. Q., & Peng, B. (2020). In silico screening of Chinese herbal medicines with the potential to directly inhibit 2019 novel coronavirus. Journal of integrative medicine, 18(2), 152-158.

Zhao, X., Liu, X., & Li, X. (2020). Tracking the spread of novel coronavirus (2019-nCoV) based on big data. medRxiv.

Zhao, Y. F., Shou, M. H., & Wang, Z. X. (2020). Prediction of the Number of Patients Infected with COVID-19 Based on Rolling Grey Verhulst Models. International Journal of Environmental Research and Public Health, 17(12), 4582.

Zhu, G., Li, J., Meng, Z., Yu, Y., Li, Y., Tang, X., ... & Wang, K. (2020). Learning from Large-Scale Wearable Device Data for Predicting Epidemics Trend of COVID-19. Discrete Dynamics in Nature and Society, 2020.