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An Introduction to Computational Intelligence in COVID-19: Surveillance, Prevention, Prediction, and Diagnosis

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Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis

Part of the book series: Studies in Computational Intelligence ((SCI,volume 923))

Abstract

Coronavirus disease 2019 (COVID-19) has been declared as pandemic which took the lives of more than 500 thousand people till mid of 2020 worldwide. Since the coronavirus is highly contagious in nature, COVID-19 is spreading rapidly despite observing social distancing and taking other recommended precautionary measures. Computational intelligence, a powerful tool that mimics human intelligence and learns specific tasks using data, is widely deployed to combat the COVID-19. This chapter briefly covers the computational intelligence methods and its applications in the surveillance, prevention, prediction, and diagnosis of COVID-19. Further, the limitation of current systems and prospects are also discussed.

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References

  1. Qazi, S., Sheikh, K., et al. (2020). A coadunation of biological and mathematical perspectives on the pandemic COVID-19: A review. Preprints 2020, 2020040007.

    Google Scholar 

  2. López, L., & Rodo, X. (2020). A modified SEIR model to predict the COVID-19 outbreak in Spain and Italy: Simulating control scenarios and multi-scale epidemics. Available at SSRN 3576802.

    Google Scholar 

  3. Qazi, S., Tanveer, K., et al. (2019). From telediagnosis to teletreatment: The role of computational biology and bioinformatics in tele-based healthcare. In Telemedicine Technologies (pp. 153–169). Amsterdam: Elsevier.

    Google Scholar 

  4. Qazi, S., & Raza, K. (2020) Smart biosensors for an efficient point of care (PoC) health management. In Smart Biosensors in Medical Care, Elsevier, pp. 65–85.

    Google Scholar 

  5. Choi, J. R. (2020). Development of point-of-care biosensors for COVID-19. Frontiers in Chemistry, 8.

    Google Scholar 

  6. Mourad, A., Srour, A., et al. (2020). Critical impact of social networks infodemic on defeating coronavirus COVID-19 pandemic: Twitter-based study and research directions. https://arxiv.org/pdf/2005.08820.pdf.

  7. Qin, L., Sun, Q., et al. (2020). Prediction of number of cases of 2019 Novel Coronavirus (COVID-19) using social media search index. International Journal of Environmental Research and Public Healthcare, 17, 2365.

    Article  MathSciNet  Google Scholar 

  8. Samaras, L., García-Barriocanal, E., & Sicilia, M. A. (2020). Syndromic surveillance using web data: A systematic review. Innovation in Health Informatics, 39–77.

    Google Scholar 

  9. Vaishya, R., Javaid, M., et al. (2020). Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 337–339.

    Article  Google Scholar 

  10. Ke, Y. Y., Peng, T. T., Yeh, T. K., Huang, W. Z., Chang, S. E., Wu, S. H., Hung, H. C., Hsu, T. A., Lee, S. J., Song, J. S., Lin, W. H., Chiang, T. J., Lin, J. H., Sytwu, H. K., Chen, C. T. (2020). Artificial intelligence approach fighting COVID-19 with repurposing drugs. Biomedical Journal.

    Google Scholar 

  11. Dzobo, K., Adotey, S., Thomford, N. E., & Dzobo, W. (2020). Integrating artificial and human intelligence: A partnership for responsible innovation in biomedical engineering and medicine. OMICS: A Journal of Integrative Biology, 24(5), 247–263.

    Article  Google Scholar 

  12. Duch, W. (2007). What is computational intelligence and where is it going? Studies in Computational Intelligence (SCI), 63, 1–13.

    Google Scholar 

  13. Sumathi, S., & Paneerselvam, S. (2010). Computational intelligence paradigms. Boca Raton: CRC Press.

    Book  Google Scholar 

  14. https://www.who.int/publicationsdetail/operational-considerations-for-covid-19-urveillance-using-gisrs-interim-guidance.

  15. Madurai Elavarasan, R., & Pugazhendhi, R. (2020). Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic. Science of the Total Environment, 725, 138858.

    Google Scholar 

  16. Halminen, O., Tenhunen, H., Heliste, A., & Seppälä, T. (2019). Factors affecting venture funding of healthcare AI companies. Stud Health Technol Inform., 262, 268–271.

    Google Scholar 

  17. WHO. (2020). Surveillance strategies for COVID-19 human infection Interim guidance 10 May 2020.

    Google Scholar 

  18. Carroll, L. N., et al. (2014). Visualization and analytics tools for infectious disease epidemiology: A systematic review. Journal of Biomedical Informatics, 51, 287–298.

    Article  Google Scholar 

  19. Elliot, A. J., et al. (2009). Monitoring the emergence of community transmission of influenza A/H1N1 2009 in England: A cross sectional opportunistic survey of self sampled telephone callers to NHS direct. BMJ 339.

    Google Scholar 

  20. Naudé, W. (2020) Artificial Intelligence against COVID-19: An early review. IZA Discussion Paper no. 13110, Bonn.

    Google Scholar 

  21. WHO reference number: WHO/2019-nCoV/Contact_Tracing/2020.1.

    Google Scholar 

  22. Raza, K. (2020). Artificial intelligence against COVID-19: A meta-analysis of current research. In Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach. Studies in Big Data, 78, 2020. Berlin: Springer (In Press).

    Google Scholar 

  23. Abi-Habib, M. &Yasir, S. (2020).India’s Coronavirus Lockdown Leaves Vast Numbers Stranded and Hungry. The New York Times. ISSN 0362-4331.

    Google Scholar 

  24. Franch-Pardo, I., Napoletano, B. M., et al. (2020). Spatial analysis and GIS in the study of COVID-19. A review. Science of The Total Environment, 739, 140033.

    Google Scholar 

  25. Rezaei, M., Nouri, A. A., et al. (2020). Application of geographic information system in monitoring and detecting the COVID-19 outbreak. Iranian Journal of Public Health, 49, 114–116.

    Google Scholar 

  26. Howard, J. (2019). Artificial intelligence: Implications for the future of work. American Journal of Industrial Medicine, 62(11), 917–926.

    Article  Google Scholar 

  27. Raza, K., & Qazi, S. (2019). Nanopore sequencing technology and internet of living things: A big hope for U-healthcare. Sensors for Health Monitoring, 5, 95–116.

    Article  Google Scholar 

  28. Farooqi, M. R., Iqbal, N., Singh, N. K., Affan, M., & Raza, K. (2019). Wireless sensor networks towards convenient infrastructure in health care industry: A systematic study. Sensors for Health Monitoring, 5, 31–46.

    Article  Google Scholar 

  29. Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020). Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 337–339.

    Google Scholar 

  30. Santosh, K. C. (2020). AI-driven tools for coronavirus outbreak: need of active learning and cross-population train/test models on multitudinal/multimodal data. Journal of Medical Systems, 44(5), 93.

    Article  Google Scholar 

  31. Thacker, S. B., Parrish, R. G., & Trowbridge, F. L. (1988). A method for evaluating systems of epidemiological surveillance. World Health Statistics Quarterly, 41(1), 11–18.

    Google Scholar 

  32. Zheng, Y., & Lai, W. (2020). Dermatology staff participate in fight against Covid-19 inChina. Journal of the European Academy of Dermatology and Venereology, 34(5), e210–e211.

    Article  MathSciNet  Google Scholar 

  33. Fields, A. C., Vacanti, J. C., Rhee, C., Klompas, M., Kanjilal, S., Maldonado, L., Robinson, M. K., Nguyen, L. L., Pimentel, M. P. T., Doherty, G. M., & Bleday, R. (2020) Restarting essential surgery in the Era of COVID-19: A cautious data driven approach based on the literature and local data. Annals of Surgery, May 22, 2020.

    Google Scholar 

  34. Ferdinandy, P., Koller, Á., Weber, C., & Waltenberger, J. (2020). Postponement of frontiers in cardiovascular biomedicine (FCVB) 2020 due to COVID-19: A look forward to 2021. Cardiovascular Research, 116(7), e78–e81.

    Article  Google Scholar 

  35. Race for a COVID-19 Vaccine. (2020). E Bio Medicine, 55, 102817. https://doi.org/10.1016/j.ebiom.2020.102817.

  36. Balaji, S. M. (2020). COVID-19—Future of dentistry. Indian Journal of Dental Research, 31(2), 167–168.

    Article  Google Scholar 

  37. Kapoor, A., Guha, S., Kanti Das, M., Goswami, K. C., & Yadav, R. (2020). Digital healthcare: The only solution for better healthcare during COVID-19 pandemic? Indian Heart Journal, 72(2), 61–64.

    Article  Google Scholar 

  38. Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., He, K., Shi, Y., & Shen, D. (2020). Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. IEEE Reviews in Biomedical Engineering.

    Google Scholar 

  39. Ucar, F., & Korkmaz, D. (2020). COVID diagnosis-net: Deep Bayes-squeeze net based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Medical Hypotheses, 140, 109761.

    Google Scholar 

  40. Yan, Q., Wang, B., Gong, D., Luo, C., Zhao, W., Shen, J., … & You, Z. (2004). COVID-19 Chest CT image segmentation–A deep convolutional neural network solution. arXiv preprint arXiv:2004.10987.

  41. Belfiore, M. P., Urraro, F., et al. (2020). Artificial intelligence to codify lung CT in Covid-19 patients. La RadiologiaMedica, 125(5), 500–504.

    Google Scholar 

  42. Belfiore, M. P., Urraro, F., Grassi, R., Giacobbe, G., Patelli, G., Cappabianca, S., & Reginelli, A. (2020). Artificial intelligence to codify lung CT in Covid-19 patients. Radiol Med 125(5), 500–504.

    Google Scholar 

  43. Li, L., Qin, L., et al. (2020). Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology, 200905.

    Google Scholar 

  44. Yan, L., Zhang, H. T., et al. (2020). Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan. MedRxiv.

    Google Scholar 

  45. Bullock, J., Pham, K. H., Lam, C. S. N., & Luengo-Oroz, M. (2020). Mapping the landscape of artificial intelligence applications against COVID-19. arXiv preprint arXiv:2003.11336.

  46. Chen, J., Wu, L., et al. (2020). Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography: A prospective study. medRxiv.

    Google Scholar 

  47. Rawat, W., & Wang, Z. (2017). Deep convolutional neural networks for image classifcation: A comprehensive review. Neural Computation, 29, 23522449.

    Article  Google Scholar 

  48. Majeed, T., Rashid, R., Ali, D., & Asaad, A. (2020). Problems of deploying CNN transfer learning to detect COVID-19 from Chest X-rays. medRxiv.

    Google Scholar 

  49. Hashem, A. M., Alghamdi, B. S., et al. (2020). Therapeutic use of chloroquine and hydroxychloroquine in COVID-19 and other viral infections: A narrative review. Travel Medicine and Infectious Disease.

    Google Scholar 

  50. Chao, Y. X., Rötzschke, O., & Tan, E. K. (2020). The role of IgA in COVID-19. Brain, Behavior, and Immunity.

    Google Scholar 

  51. Xia Chao, Y., Rötzschke, O., & Tan, E. K. (2020). The role of IgA in COVID-19. Brain, Behavior, and Immunity, S0889–1591(20), 31047–3.

    Google Scholar 

  52. Belfiore, M. P., Urraro, F., Grassi, R., Giacobbe, G., Patelli, G., Cappabianca, S., & Reginelli, A. (2020). Artificial intelligence to codify lung CT in Covid-19 patients. La Radiologia Medica, 125(5), 500–504.

    Google Scholar 

  53. Allam, Z., & Jones, D. S. (2020). On the Coronavirus (COVID-19) outbreak and the Smart City Network: Universal data sharing standards coupled with artificial intelligence (AI) to benefit urban health monitoring and management. Healthcare (Basel), 8(1), 46.

    Article  Google Scholar 

  54. Thakur, A., Mishra, A. P., et al. (2020). Application of artificial intelligence in pharmaceutical and biomedical studies. Current Pharmaceutical Design.

    Google Scholar 

  55. Li, G., Xiong, Z., et al. (2020). Value of CT application in the screening, diagnosis, and treatment of COVID-19. Journal of Central South University. Medical Sciences, 45(3).

    Google Scholar 

  56. Amisha, M. P., Pathania, M., & Rathaur, V. K. (2019). Overview of artificial intelligencein medicine. Journal of Family Medicine and Primary Care, 8(7), 2328–2331.

    Google Scholar 

  57. Chen, M., & Decary, M. (2020). Artificial intelligence in healthcare: An essential guidefor health leaders. Healthcare Management Forum, 33(1), 10–18.

    Article  Google Scholar 

  58. ET Health World. (2020). Coronavirus treatment: How AI steered doctors toward a possible Coronavirus treatment, Health News, ET HealthWorld, June 18, 2020.

    Google Scholar 

  59. AI uncovers a potential treatment for Covid-19 patients, WIRED, June 18, 2020.

    Google Scholar 

  60. AI is screening billions of molecules for coronavirus treatments. The Scientist Magazine, June 18, 2020.

    Google Scholar 

  61. How AI is fighting COVID-19: The companies using intelligent tech to find new drugs.

    Google Scholar 

  62. COVID effect: Medical AI takes prominence in Asia Pacific—Geospatial World.

    Google Scholar 

  63. Singh, R. P., Javaid, M., Kataria, R., Tyagi, M., Haleem, A., & Suman, R. (2020). Significantapplications of virtual reality for COVID-19 pandemic. Diabetes & Metabolic Syndrome, 14(4), 661–664.

    Article  Google Scholar 

  64. https://www.bbva.com/en/ai-driven-project-identifies-up-to-390-potential-drugs-against-covid/ AI-driven project identifies up to 390 potential drugs against COVID.

  65. Javaid, M., Haleem, A., Vaishya, R., Bahl, S., Suman, R., & Vaish, A. (2020). Industry 4.0 technologies and their applications in fighting COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 419–422.

    Google Scholar 

  66. Grange, E. S., Neil, E. J., Stoffel, M., Singh, A. P., Tseng, E., Resco-Summers, K., Fellner, B. J., Lynch, J. B., Mathias, P. C., Mauritz-Miller, K., Sutton, P. R., & Leu, M. G. (2020). Responding to COVID-19: The UW medicine information technology services experience. Applied Clinical Informatics, 11(2), 265–275.

    Google Scholar 

  67. Bragazzi, N. L., Dai, H., Damiani, G., Behzadifar, M., Martini, M., & Wu, J. (2020). How big data and artificial intelligence can help better manage the COVID-19 pandemic. International Journal of Environmental Research and Public Health, 17(9), E3176.

    Article  Google Scholar 

  68. Abdulla, A., Wang, B., Qian, F., Kee, T., Blasiak, A., Ong, Y. H., … & Keppo, J. (2020). Project IDentif. AI: Harnessing artificial intelligence to rapidly optimize combination therapy development for infectious disease intervention. Advanced Therapeutics, 3(7), 2000034.

    Google Scholar 

  69. Kearney, E., Wojcik, A., & Babu, D. (2020). Artificial intelligence in genetic services delivery: Utopia or apocalypse? Journal of Genetic Counseling, 29(1), 8–17.

    Article  Google Scholar 

  70. Kumar, A., Gupta, P. K., & Srivastava, A. (2020). A review of modern technologies for tackling COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 569–573.

    Article  Google Scholar 

  71. Ito, R., Iwano, S., & Naganawa, S. (2020). A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019. Diagnostic and Interventional Radiology. https://doi.org/10.5152/dir.2019.20294.

    Article  Google Scholar 

  72. Naudé, W. (2020). Artificial intelligence vs COVID-19: limitations, constraints and pitfalls. AI and Society, pp. 1–5.

    Google Scholar 

  73. Naudé, W. (2020). Artificial intelligence vs COVID-19: Limitations, constraints and pitfalls. AI & Society.

    Google Scholar 

  74. Rubinelli, S., Myers, K., Rosenbaum, M., & Davis, D. (2020). Implications of the currentCOVID-19 pandemic for communication in healthcare. Patient Education and Counseling, 103(6), 1067–1069.

    Article  Google Scholar 

  75. Galli, F., Reglero, G., Bartolini, D., & Visioli, F. (2020). Better prepare for the next one. Lifestyle lessons from the COVID-19 pandemic. Pharma Nutrition, 12, 100193.

    Google Scholar 

  76. Lodha, R., & Kabra, S. K. (2020). COVID-19 Pandemic: The way forward. The Indian Journal of Pediatrics.

    Google Scholar 

  77. Gan, Y., Chen, Y., Wang, C., Latkin, C., & Hall, B. J. (2020). The fight against COVID-19 and the restoration of trust in Chinese medical professionals. Asian Journal of Psychiatry, 51, 102072.

    Article  Google Scholar 

  78. Blake, H., Bermingham, F., Johnson, G., & Tabner, A. (2020). Mitigating the psychological impact of COVID-19 on healthcare workers: A digital learning package. International Journal of Environmental Research and Public Health, 17(9), E2997.

    Article  Google Scholar 

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  1. Department of Computer Science, Jamia Millia Islamia, New Delhi, 110025, India

    Khalid Raza,  Maryam & Sahar Qazi

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  1. Khalid Raza
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Correspondence to Khalid Raza .

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  1. Department of Computer Science, Jamia Millia Islamia (Central University), New Delhi, India

    Khalid Raza

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Raza, K., Maryam, Qazi, S. (2021). An Introduction to Computational Intelligence in COVID-19: Surveillance, Prevention, Prediction, and Diagnosis. In: Raza, K. (eds) Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis. Studies in Computational Intelligence, vol 923. Springer, Singapore. https://doi.org/10.1007/978-981-15-8534-0_1

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