Exploring Machine Learning in Healthcare and its Impact on the SARS-CoV-2 Outbreak

Dennie James; Tanya James

doi:10.18034/ajase.v10i1.17

Authors

Dennie James Faculty of Engineering and Design, Carleton University, Ottawa, CANADA & BAIOTEQ, Saint John, CANADA
Tanya James Department of Social Science, Health and Medicine, King's College London, UK

Keywords:

SARS-CoV-2

COVID-19

Coronavirus

Artificial intelligence

Machine learning

Deep learning

Smart cities

Abstract

Machine learning can be defined as a comprehensive range of tools utilized for recognizing patterns in data. Owing to its reliance on artificial intelligence in lieu of age-old, traditional methods, machine learning has established itself as an exceedingly quicker way of discerning patterns and trends from bulk data. The advanced system can even update itself on the availability of new data. This paper intends to elucidate different techniques involved in machine learning that have facilitated the prediction, detection, and restriction of infectious diseases in the past few decades. Moreover, in light of the unprecedented COVID-19 pandemic, such tools and techniques have been utilized extensively by smart cities to curb the proliferation of the SARS-CoV-2 virus. However, the strengths and weaknesses of this approach remain abstruse and therefore, this review also aims to evaluate the role of machine learning in the recent coronavirus outbreak.

References

Tandon PN. COVID-19: Impact on health of people & wealth of nations. Indian J Med Res 2020;151:121-3

Fatima, M. and Pasha, M. (2017) Survey of Machine Learning Algorithms for Disease Diagnostic. Journal of Intelligent Learning Systems and Applications, 9, 1-16. doi: 10.4236/jilsa.2017.91001.

Allen, Koya C. "Applications: Biosurveillance, biodefense, and biotechnology." Disaster Epidemiology. Academic Press, 2018. 143-151.

Stark, Karen A., et al. "Data Science, Analytics and Collaboration for a Biosurveillance Ecosystem." Online Journal of Public Health Informatics 11.1 (2019).

Lampos, Vasileios, and Nello Cristianini. "Tracking the flu pandemic by monitoring the social web." 2010 2nd international workshop on cognitive information processing. IEEE, 2010.

Lalmuanawma, Samuel, Jamal Hussain, and Lalrinfela Chhakchhuak. "Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review." Chaos, Solitons & Fractals (2020): 110059

Rothan, Hussin A., and Siddappa N. Byrareddy. "The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak." Journal of autoimmunity 109 (2020): 102433.

Funk S, Camacho A, Kucharski AJ, Eggo RM, EdmundsWJ. Realtime forecasting of infectious disease dynamics with a stochastic semi-mechanistic model. Epidemics. 2018; 22:56–61.

Johansson, Michael A., et al. "Erratum: An open challenge to advance probabilistic forecasting for dengue epidemics (Proceedings of the National Academy of Sciences of the United States of America (2019) 116 (24268-24274." Proceedings of the National Academy of Sciences of the United States of America 117.33 (2020): 20336.

Teng, Yue, et al. "Dynamic forecasting of Zika epidemics using Google Trends." PloS one 12.1 (2017): e0165085.

Zunic, Anastazia, Padraig Corcoran, and Irena Spasic. "Sentiment analysis in health and well-being: systematic review." JMIR medical informatics 8.1 (2020): e16023.

Lim, Sunghoon, Conrad S. Tucker, and Soundar Kumara. "An unsupervised machine learning model for discovering latent infectious diseases using social media data." Journal of biomedical informatics 66 (2017): 82-94.

Choi, Sungwoon, et al. "Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks." Methods 129 (2017): 50-59.

Wirz, Christopher D., et al. "Rethinking social amplification of risk: Social media and Zika in three languages." Risk Analysis 38.12 (2018): 2599-2624.

Wang, Yan, Haiyan Hao, and Lisa Sundahl Platt. "Examining risk and crisis communications of government agencies and stakeholders during early-stages of COVID-19 on Twitter." Computers in human behavior 114 (2021): 106568.

Pham, Quoc-Viet, et al. "Artificial intelligence (AI) and big data for coronavirus (COVID-19) pandemic: A survey on the state-of-the-arts." (2020).

Tavanaei, Amirhossein, et al. "Deep learning in spiking neural networks." Neural Networks 111 (2019): 47-63.

Riad, Mahbubul H., et al. "Risk assessment of Ebola virus disease spreading in Uganda using a two-layer temporal network." Scientific reports 9.1 (2019): 1-17.

Ferguson, Neil, et al. "Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand." (2020).

Lutz, Chelsea S., et al. "Applying infectious disease forecasting to public health: a path forward using influenza forecasting examples." BMC Public Health 19.1 (2019): 1-12.

Hu, Chien-An, et al. "Using a machine learning approach to predict mortality in critically ill influenza patients: a cross-sectional retrospective multicentre study in Taiwan." BMJ open 10.2 (2020): e033898.

Koo, Joel R., et al. "Interventions to mitigate early spread of SARS-CoV-2 in Singapore: a modelling study." The Lancet Infectious Diseases 20.6 (2020): 678-688.

COVID, IHME, and Christopher JL Murray. "Forecasting COVID-19 impact on hospital bed-days, ICU-days, ventilator-days and deaths by US state in the next 4 months." MedRxiv (2020).

Li, Qun, et al. "Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia." New England journal of medicine (2020).

Zhao, Shi, et al. "Estimating the unreported number of novel coronavirus (2019-nCoV) cases in China in the first half of January 2020: a data-driven modelling analysis of the early outbreak." Journal of clinical medicine 9.2 (2020): 388.

Kobayashi, Genya, et al. "Predicting intervention effect for COVID-19 in Japan: state space modeling approach." BioScience Trends (2020).

Berryhill, Jamie, et al. "Hello, World: Artificial intelligence and its use in the public sector." (2019).

Naudé, Wim. "Artificial intelligence vs COVID-19: limitations, constraints and pitfalls." AI & society 35.3 (2020): 761-765.

Akhtar, Mahmood, Moritz UG Kraemer, and Lauren M. Gardner. "A dynamic neural network model for predicting risk of Zika in real time." BMC medicine 17.1 (2019): 1-16.

Linka, Kevin, et al. "Outbreak dynamics of COVID-19 in Europe and the effect of travel restrictions." Computer Methods in Biomechanics and Biomedical Engineering 23.11 (2020): 710-717..

Dong, Ensheng, Hongru Du, and Lauren Gardner. "An interactive web-based dashboard to track COVID-19 in real time." The Lancet infectious diseases 20.5 (2020): 533-534.

Boulos, Maged N. Kamel, and Estella M. Geraghty. "Geographical tracking and mapping of coronavirus disease COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic and associated events around the world: how 21st century GIS technologies are supporting the global fight against outbreaks and epidemics." (2020): 1-12.

Shu, Yuelong, and John McCauley. "GISAID: Global initiative on sharing all influenza data–from vision to reality." Eurosurveillance 22.13 (2017): 30494.

Zhang, Guang Lan, et al. "Neural models for predicting viral vaccine targets." Journal of bioinformatics and computational biology 3.05 (2005): 1207-1225.

Nakayasu, Ernesto S., et al. "Improved proteomic approach for the discovery of potential vaccine targets in Trypanosoma cruzi." Journal of proteome research 11.1 (2012): 237-246.

Alimadadi, Ahmad, et al. "Artificial intelligence and machine learning to fight COVID-19." (2020): 200-202.

Hayati, Maryam, Priscila Biller, and Caroline Colijn. "Predicting the short-term success of human influenza virus variants with machine learning." Proceedings of the Royal Society B 287.1924 (2020): 20200319.

Zhang, Zheng, et al. "Rapid identification of human‐infecting viruses." Transboundary and emerging diseases 66.6 (2019): 2517-2522.

Srinivasa Rao, Arni SR, and Jose A. Vazquez. "Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone-based survey when cities and towns are under quarantine." Infect Control Hosp Epidemiol (2020): 1-5.

Hyder, Adnan A., et al. "Noncommunicable disease risk factors and mobile phones: a proposed research agenda." Journal of medical Internet research 19.5 (2017): e133.

Santillana, Mauricio, et al. "Combining search, social media, and traditional data sources to improve influenza surveillance." PLoS Comput Biol 11.10 (2015): e1004513.

Insel, Thomas R. "Digital phenotyping: a global tool for psychiatry." World Psychiatry 17.3 (2018): 276.

Onnela, Jukka-Pekka, and Scott L. Rauch. "Harnessing smartphone-based digital phenotyping to enhance behavioral and mental health." Neuropsychopharmacology 41.7 (2016): 1691-1696.

Jhunjhunwala, Ashok. "Role of telecom network to manage Covid-19 in India: Aarogya setu." Transactions of the Indian National Academy of Engineering 5 (2020): 157-161.

Bastawrous, Andrew, and Matthew J. Armstrong. "Mobile health use in low-and high-income countries: an overview of the peer-reviewed literature." Journal of the royal society of medicine 106.4 (2013): 130-142.

Huang, Yhu-Chering, Ping-Ing Lee, and Po-Ren Hsueh. "Evolving reporting criteria of COVID-19 in Taiwan during the epidemic." Journal of Microbiology, Immunology and Infection 53.3 (2020): 413-418.

Gozes, Ophir, et al. "Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis." arXiv preprint arXiv:2003.05037 (2020).

Fang, Yicheng, et al. "Sensitivity of chest CT for COVID-19: comparison to RT-PCR." Radiology 296.2 (2020): E115-E117.

Xu, Xiaowei, et al. "A deep learning system to screen novel coronavirus disease 2019 pneumonia." Engineering 6.10 (2020): 1122-1129.

John, Maya, and Hadil Shaiba. "Main factors influencing recovery in MERS Co-V patients using machine learning." Journal of infection and public health 12.5 (2019): 700-704.

Azkur, Ahmet Kursat, et al. "Immune response to SARS‐CoV‐2 and mechanisms of immunopathological changes in COVID‐19." Allergy 75.7 (2020): 1564-1581.

Zhou, Fei, et al. "Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study." The lancet 395.10229 (2020): 1054-1062.

Jayatilaka, Gihan Chanaka, et al. "Use of Artificial Intelligence on spatio-temporal data to generate insights during COVID-19 pandemic: A Review." medRxiv (2020).

Jiang, Xiangao, et al. "Towards an artificial intelligence framework for data-driven prediction of coronavirus clinical severity." Computers, Materials & Continua 63.1 (2020): 537-551.

Ekins, Sean, et al. "Exploiting machine learning for end-to-end drug discovery and development." Nature materials 18.5 (2019): 435-441.

Mohanty, Sweta, et al. "Application of Artificial Intelligence in COVID-19 drug repurposing." Diabetes & Metabolic Syndrome: Clinical Research & Reviews (2020).

Ekins, Sean, et al. "Déjà vu: Stimulating open drug discovery for SARS-CoV-2." Drug discovery today 25.5 (2020): 928-941.

Cascella, Marco, et al. "Features, evaluation and treatment coronavirus (COVID-19)." Statpearls [internet] (2020).

Psychology Can Explain Why Coronavirus Drives Us to Panic Buy. It Also Provides Tips on How to Stop. The Conversation

Bakir, Nesrin, Sean Humpherys, and Kareem Dana. "Students' Perceptions of Challenges and Solutions to Face-to-Face and Online Group Work." Information Systems Education Journal 18.5 (2020): 75-88.

Hai, Nguyen Dao Xuan, Luong Huu Thanh Nam, and Nguyen Truong Thinh. "Remote healthcare for the elderly, patients by tele-presence robot." 2019 International Conference on System Science and Engineering (ICSSE). IEEE, 2019.

Storper, Michael, and Anthony J. Venables. "Buzz: face-to-face contact and the urban economy." Journal of economic geography 4.4 (2004): 351-370.

Hur, Joon-Young, et al. "The “smart work” myth: how bureaucratic inertia and workplace culture stymied digital transformation in the relocation of South Korea’s capital." Asian Studies Review 43.4 (2019): 691-709.