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Covid-19 Containment: Demystifying the Research Challenges and Contributions Leveraging Digital Intelligence Technologies

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Machine Intelligence and Smart Systems

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

The whole world is in distress due to the uninhibited spread of the COVID-19 virus. Medical practitioners are striving to develop drugs and vaccines for the infection. In contrast, IT specialists are leveraging digital intelligence technologies and tools to surmount the endemic. Specially, mathematicians are develo** models for infection spread, computer science experts and data scientists are working with artificial intelligence to predict insights out of growing data, electronics engineers are instrumenting IoT based systems, IT professionals are setting up clouds for real-time data storage and processing to extricate actionable intelligence, security experts are ensuring data security through blockchain, etc. Several collaborative initiatives are on to arrive at strategically sound solutions for COVID-19. This is a best practices paper with the following contributions

  • To brief about the COVID-19 related literature which uses mathematical and statistical models and computing technologies like artificial intelligence, the Internet of Things, cloud services, big data, etc., for providing combating solutions for the infection, in a categorized manner

  • To identify various research problems around COVID-19

  • To highlight the practical issues and challenges that occur while resolving the above issues and.

  • To propose an integrated, end-to-end architecture which nullifies the limitations of individual technologies and serves as enabling architecture for COVID-19

The proposed architecture facilitates various stakeholders such as physicians, molecular biologists, IT professionals, and researchers by providing decision enabling, value-adding patterns, and other knowledge to take the right countermeasures in time against COVID-19 infection

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References

  1. Petrosillo N et al (2020) COVID-19, SARS and MERS: are they closely related? Clin Microbiol Infect. https://doi.org/10.1016/j.cmi.2020.03.026[Online]

    Article  Google Scholar 

  2. Coronavirus disease 2019 (COVID-19) situation report—40, WHO, 29 Feb 2020 [Online]. Available https://www.who.int/docs/default-source/coronaviruse/risk-comms-updates/update-20-epi-win-covid-19.pdf?sfvrsn=5e0b2d74_2

  3. Guan WJ et al (2020) China medical treatment expert group for Covid-19. Clinical Characteristics of Coronavirus Disease 2019 in China. New England J Med. https://doi.org/10.1056/NEJMoa2002032

  4. He X et al (2020) Temporal dynamics in viral shedding and transmissibility of COVID-19. Nat Med. https://doi.org/10.1038/s41591-020-0869-5

  5. Tong Z et al (2020) Potential Presymptomatic transmission of SARS-CoV-2, Zhejiang Province, China, 2020. Emerg Infect Dis 26(5):1052–1054. 2020. https://doi.org/10.3201/eid2605.200198

  6. Gandhi M et al Asymptomatic transmission, the achilles’ heel of current strategies to control Covid-19. New England J Med. https://doi.org/10.1056/NEJMe2009758

  7. Udugama B et al (2020) Diagnosing COVID-19: the disease and tools for detection. ACS Nano 14(4). https://doi.org/10.1021/acsnano.0c02624

  8. Hao Q, Wu H, Wang Q (2019) Difficulties in false negative diagnosis of coronavirus disease 2019: a case report, a case report in Infectious diseases. https://doi.org/10.21203/rs.3.rs-17319/v1 [Online]. Available https://www.researchsquare.com/article/rs-17319/v1

  9. Wang W et al (2020 )Detection of SARS-CoV-2 in different types of clinical specimens. JAMA. https://doi.org/10.1001/jama.2020.3786

  10. Green K et al, CEBM (2020) Molecular and antibody point-of-care tests to support the screening, diagnosis and monitoring of COVID-19, 7 Apr 2020 [Online]. Available https://www.cebm.net/Covid-19/molecular-and-antibody-point-of-care-tests-to-support-the-screening-diagnosis-and-monitoring-of-Covid-19/

  11. Ting DSW, Carin L, Dzau V, Wong TY (2020) Digital technology and COVID-19. Nat Med 26:459–461. https://doi.org/10.1038/s41591-020-0824-5

  12. Weng TC et al (2018) Identifying the most vulnerable groups by machine learning during the 2009 pandemic influenza in Taiwan. Int J Infect Dis 73:269. https://doi.org/10.1016/j.ijid.2018.04.4028

  13. Chae S, Kwon S, Lee D (2018) Predicting infectious disease using deep learning and big data. Int J Environ Res Publ Health 15(8):1596–1614. https://doi.org/10.3390/ijerph15081596

  14. Liu L, Han M, Zhou Y, Wang Y (2018) LSTM recurrent neural networks for influenza trends prediction. In: Zhang F, Cai Z, Skums P, Zhang S (eds) Bioinformatics research and applications. ISBRA 2018. Lecture notes in computer science, vol 10847. Springer, Cham. https://doi.org/10.1007/978-3-319-94968-0_25

  15. Yang C-T et al (2020) Influenza-like illness prediction using a long short-term memory deep learning model with multiple open data sources. J Supercomput. https://doi.org/10.1007/s11227-020-03182-5

  16. Wang M et al (2019) A novel model for malaria prediction based on ensemble algorithms. PLOS One 14(12). https://doi.org/10.1371/journal.pone.0226910

  17. Philemon D, Zuhaimy I, Dare J (2019) A review of epidemic forecasting using artificial neural networks. Int J Epidemiol Res 6:132–143. https://doi.org/10.15171/ijer.2019.24

    Article  Google Scholar 

  18. Siettos CI, Russo L (2013) Mathematical modeling of infectious disease dynamics. Virulence 4(4):295–306. https://doi.org/10.4161/viru.24041

  19. Zhao B et al (2020) Mathematical modeling and epidemic prediction of COVID-19 and its significance to epidemic prevention and control measures. Annal Infect Dis Epidemiol 5(1):1052

    Google Scholar 

  20. Ramos BVM (2020) Application of the Be-CoDiS mathematical model to forecast the international spread of the 2019–20 Wuhan coronavirus outbreak, Mar 2020 [Online]. https://doi.org/10.13140/RG.2.2.31460.94081

  21. Jia L, Jiang KL, Guo X, Zhao T (2020) Prediction and analysis of coronavirus disease 2019, Mar 2020 [Online]. Available https://arxiv.org/ftp/arxiv/papers/2003/2003.05447.pdf

  22. Wang H et al (2020) Phase-adjusted estimation of the number of coronavirus disease 2019 cases in Wuhan, China. Cell Discov 6:10. https://doi.org/10.1038/s41421-020-0148-0

  23. Tang B et al (2020) Estimation of the transmission risk of the 2019-nCoV and its implication for public health interventions. J Clin Med 9:462. https://doi.org/10.3390/jcm9020462

    Article  Google Scholar 

  24. Pipatsart N, Triampo W, Modchang C (2017) Stochastic models of emerging infectious disease transmission on adaptive random networks. J Comput Math Methods Med 2403851. https://doi.org/10.1155/2017/2403851

  25. Eggo RM (2020) Early dynamics of transmission and control of COVID-19: a mathematical modelling study. Lancet Infect Dis 20(5):553–558. https://doi.org/10.1016/S1473-3099(20)30144-4

  26. Wu JT, Leung K, Leung GM (2020) Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet 395(10225):689–697. https://doi.org/10.1016/S0140-6736(20)30260-9

  27. Chen T-M et al (2020) A mathematical model for simulating the phase-based transmissibility of a novel coronavirus. Infect Dis Poverty 9:24. https://doi.org/10.1186/s40249-020-00640-3

  28. Tapiwa G et al Estimating the generation interval for COVID-19based on symptom onset data. medRxiv [Online]. https://doi.org/10.1101/2020.03.05.20031815

  29. Ferrández IMR, Vela-Pérez M, Ramos AM (2020) Mathematical modeling of the spread of the coronavirus disease 2019 (COVID-19) considering its particular characteristics. The case of China”, communications in nonlinear science and numerical simulation, Apr 2020 (in Press). https://doi.org/10.1016/j.cnsns.2020.105303

  30. Gupta R, Pandey G, Chaudhary P, Pal SK SEIR and Regression Model based COVID-19 outbreak predictions in India. medrxiv. https://doi.org/10.1101/2020.04.01.20049825

  31. Bhatnagar M (2020) COVID-19: mathematical modeling and predictions. Researchgate, Apr 2020 [Online]. https://doi.org/10.13140/RG.2.2.29541.96488

  32. Arti MK, Bhatnagar K (2020) Modeling and predictions for COVID-19 spread in India. Researchgate. https://doi.org/10.13140/RG.2.2.11427.81444

  33. Ai T et al (2020) Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology. https://doi.org/10.1148/radiol.2020200642

  34. Li L, Qin L et al (2020) Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Thorac Imaging. https://doi.org/10.1148/radiol.2020200905

  35. Brian H, Seth K, Albert H (2020) Deep learning localization of pneumonia 2019 coronavirus (COVID-19) outbreak. J Thorac Imaging 35(3):W87–W89. https://doi.org/10.1097/RTI.0000000000000512

    Article  Google Scholar 

  36. Dell’Aversana P (2020) Deep neural networks and reinforcement learning for studying COVID-19 pandemic. Researchgate. https://www.researchgate.net/publication/339953916_Deep_Neural_Netcworks_and_Reinforcement_Learning_for_studying_COVID-19_pandemic

  37. Pourhomayoun M, Shakibi M Predicting mortality risk in patients with COVID-19 using artificial intelligence to help medical decision-making. medRxiv. https://doi.org/10.1101/2020.03.30.20047308

  38. Santosh KC (2020) AI-Driven tools for coronavirus outbreak: need of active learning and cross-population train/test models on multitudinal/multimodal data. J Med Syst 44:93. https://doi.org/10.1007/s10916-020-01562-1

  39. Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T (2018) The rise of deep learning in drug discovery. Drug Discovery Today 23(6):1241–1250. https://doi.org/10.1016/j.drudis.2018.01.039

    Article  Google Scholar 

  40. Senior AW et al (2020) Improved protein structure prediction using potentials from deep learning. Nature 577:706–710

    Google Scholar 

  41. Zhavoronkov A et al Potential COVID-2019 3C-like protease inhibitors designed using generative deep learning approaches. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.11829102.v2

  42. Batra R et al (2020) Screening of therapeutic agents for COVID-19 using machine learning and ensemble docking simulations. Researchgate Reposit

    Google Scholar 

  43. Song Y, Jiang J, Wang X, Yang D, Bai C (2020) Prospect and application of Internet of Things technology for prevention of SARIs. Clin eHealth 3:1–4. https://doi.org/10.1016/j.ceh.2020.02.001

    Article  Google Scholar 

  44. Abdulrazaq MN et al (2020) Novel Covid-19 detection and diagnosis system using IoT based smart helmet. Int J Psychosoc Rehabil 24(7):2296–2303. https://doi.org/10.37200/IJPR/V24I7/PR270221

  45. Jiang Z, Hu M, Zhai G Portable health screening device of respiratory infections. Available https://arxiv.org/pdf/2004.01479.pdf

  46. Sethi P, Sarangi SR (2017) Internet of Things: architectures, protocols, and applications. J Electr Comput Eng 9324035. https://doi.org/10.1155/2017/9324035

  47. Maghdid HS, Ghafoor K, Sadiq AS, Curran K, Rabie K A novel AI-enabled framework to diagnose coronavirus COVID 19 using smartphone embedded sensors: design study. https://arxiv.org/abs/2003.07434

  48. Rao A, Vazquez J (2020) Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone-based survey in the populations when cities/towns are under quarantine. Infect Control Hosp Epidemiol:1–18. https://doi.org/10.1017/ice.2020.61

  49. Khan FA, Asif M, Ahmad M, Alharbi M, Aljuaid H (2020) Blockchain technology, improvement suggestions, security challenges on smart grid and its application in healthcare for sustainable development. Sustain Cities Soc 55:102018.https://doi.org/10.1016/j.scs.2020.102018

  50. Torky M, Hassanien AE (2020) COVID-19 blockchain framework: innovative approach, Apr 2020. https://doi.org/10.13140/RG.2.2.21696.48640

  51. Nguyen DC, Ding M, Pathirana PN, Seneviratne A (2020) Blockchain and AI-based solutions to combat coronavirus (COVID-19)-like epidemics: a survey, Apr 2020. https://doi.org/10.36227/techrxiv.12121962.v1

  52. Johnstone S (2020) A viral warning for change. COVID-19 versus the red cross: better solutions via blockchain and artificial intelligence, 3 Feb 2020. University of Hong Kong Faculty of Law Research Paper No. 2020/005. Available https://doi.org/10.2139/ssrn.3530756

  53. Mashamba-Thompson T, Clayton E Blockchain and artificial intelligence technology for novel coronavirus disease-19 self-testing. https://doi.org/10.3390/diagnostics10040198

  54. Jason Wang C, Ng CY, Brook RH (2020) Response to COVID-19 in Taiwan big data analytics, new technology, and proactive testing. JAMA 323(14):1341–1342. https://doi.org/10.1001/jama.2020.3151

  55. Naicker S, Yang C-W, Hwang S-J, Liu B-C, Chen J-H, Jha V (2020) The novel coronavirus 2019 epidemic and kidneys. Kidney Int 97:824–828.https://doi.org/10.1016/j.kint.2020.03.001

  56. Guan W et al Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis. Euro Respirat J. https://doi.org/10.1183/13993003.00547-2020

  57. Filice RW, Frantz SK (2019) Effectiveness of deep learning algorithms to determine laterality in radiographs. J Digit Imaging 32:656–664. https://doi.org/10.1007/s10278-019-00226-y

    Article  Google Scholar 

  58. Chen B et al Roles of meteorological conditions in COVID-19 transmission on a worldwide scale. https://doi.org/10.1101/2020.03.16.20037168

  59. Maa Y et al (2020) Effects of temperature variation and humidity on the death of COVID-19 in Wuhan, China. Sci Total Environ 724.https://doi.org/10.1016/j.scitotenv.2020.138226

  60. Modjarrad K, Moorthy VS, Millett P, Gsell P-S, Roth C, Kieny M-P (2016) Develo** global norms for sharing data and results during public health emergencies. PLoS Med 13(1):e1001935. https://doi.org/10.1371/journal.pmed.1001935PMID:26731342

  61. Moorthy V, Restrepo AMH, Preziosi M-P, Swaminathan S (2020) Data sharing for novel coronavirus (COVID-19). Bull World Health Organ 98(3):150. https://doi.org/10.2471/BLT.20.251561

    Article  Google Scholar 

  62. Zhou C et al (2020) COVID-19: challenges to GIS with big data. Geogr Sustain. https://doi.org/10.1016/j.geosus.2020.03.005

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Authors and Affiliations

  1. Bharathidasan University Constituent Arts and Science College, Affiliated To Bharathidasan University, Tiruchirappalli, Tamil Nadu, India

    Chellammal Surianarayanan

  2. Reliance Site Reliability Engineering (SRE) Division, Reliance Jio Platforms Ltd., Bangalore, 560103, India

    Pethuru Raj Chelliah

Authors
  1. Chellammal Surianarayanan
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  2. Pethuru Raj Chelliah
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Correspondence to Chellammal Surianarayanan .

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Editors and Affiliations

  1. University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, Madhya Pradesh, India

    Shikha Agrawal

  2. Rustamji Institute of Technology, Gwalior, Madhya Pradesh, India

    Kamlesh Kumar Gupta

  3. King Mongkut’s University of Technology Thonburi, Bangkok, Thailand

    Jonathan H. Chan

  4. School of Information Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya, Bhopal, Madhya Pradesh, India

    Jitendra Agrawal

  5. Vikrant Institute of Technology and Management, Gwalior, Madhya Pradesh, India

    Manish Gupta

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Surianarayanan, C., Chelliah, P.R. (2021). Covid-19 Containment: Demystifying the Research Challenges and Contributions Leveraging Digital Intelligence Technologies. In: Agrawal, S., Kumar Gupta, K., H. Chan, J., Agrawal, J., Gupta, M. (eds) Machine Intelligence and Smart Systems . Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-33-4893-6_18

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