Systematic Review on the Effect of the Information Technology on Epidemic Control of COVID-19

Document Type : Original Article

Authors

1 MSc. Computer Engineering, Faculty of Electrical and Computer Engineering, Azad university of dezfull, dezfull, Iran. Email: Chahkhoie@gmail.com

2 Assistance Prof. Computer Engineering, Faculty of Electrical and Computer Engineering, Azad university of dezfull, dezfull, Iran. Email: myrshg@gmail.com

3 MSc. Computer Engineering, Faculty of Electrical and Computer Engineering, Azad university of dezfull, dezfull, Iran. Email: Nezhadfarhani@gmail.com

4 Assistance Prof. Computer Engineering, Faculty of Electrical and Computer Engineering, Azad University, Qom, Iran. Email: a.shahidinejad@gmail.com

Abstract

The emergence of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) in China on December 2019 has led to a global outbreak of COVID-19, which spread worldwide and became an international public health issue. People all over the world must fight in this unexpected battle and the part each individual plays, is important. The health care system has done an excellent job, and the government has done various actions to help the community control the virus spreads as well. On the other hand, in most cases people help to improve the situation alongside the policies. However, the role of information technology in helping various social institutions to fight COVID-19 is hidden and is not well appreciated. The purpose of this study is to discover the hidden role of information technology (IT) that ultimately helps to control the epidemic. Research has shown that strategies for using potential technologies can be beneficial. These IT strategies can also be tailored either to control the epidemic or to support community exclusion during the epidemic which in turn, helps control the spread of infection. This article sheds light on the impact of various technologies that help health care systems, government and public in different aspects to fight COVID-19. In addition to the technologies implemented, this paper also deals with the potential unexpected technologies that can be effective in controlling epidemic conditions for future applications. It has also tried to provide IT-based solutions to deal with the disease epidemic as well.

Keywords

References

Ajkomar A., Dean J., Kohane I. Machine learning in medicine. N. Engl. J. Med. 2019;380:1347–1358. DOI: https://doi.org/10.1056/NEJMra1814259 .
Allcott H., Gentzkow M. Social media and fake news in the 2016 election. J. Econ. Perspect. 2017;31:211–236. DOI: https://doi.org/10.1257/jep.31.2.211
Alsuliman T., Humaidan D., Sliman L. Machine learning and artificial intelligence in the service of medicine: necessity or potentiality? Current Research in Translational Medicine. 2020 DOI: https://doi.org/10.1016/j.retram.2020.01.002 . In Press.
Amukele T., Ness P.M., Tobian A.A.R., Boyd J., Street J. Drone transportation of blood products. Transfusion. 2016;57:582–588.  DOI: https://doi.org/10.1111/trf.13900.
Amukele T.K., Sokoll L.J., Pepper D., Howard D.P., Street J. Can unmanned aerial systems (drones) be used for the routine transport of chemistry, hematology, and coagulation laboratory specimens? PLoS One. 2015;10  DOI: https://doi.org/10.1371/journal.pone.0134020.
anzert S., Guttmann J., Kersting K., Kuhlen R., Putensen C., Sydow M., Kramer S. Analysis of respiratory pressure–volume curves in intensive care medicine using inductive machine learning. Artif. Intell. Med. 2002;26:69–86. DOI: https://doi.org/10.1016/S0933-3657(02)00053-2 .
Ardila D., Kiraly A.P., Bharadwaj S., Choi B., Reicher J.J., Peng L. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat. Med. 2019;25:954–961. DOI: https://doi.org/10.1038/s41591-019-0447-x  
argeya R., Leng T. Automated identification of diabetic retinopathy using deep learning. Ophthalmology. 2017;124:962–969. DOI: https://doi.org/10.1016/j.ophtha.2017.02.008 .
Babu N., Reddy B.S. Challenges and opportunity of E-learning in developed and developing countries- a review. International Journal of Emerging Research in Management and Technology. 2015;4:259–262. DOI: https://doi.org/10.1007/978-3-319-23207-2_41
Bates D.W., Saria S., Ohno-Machado L., Shah A., Escobar G. Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Aff. 2014;33:1123–1131. DOI: https://doi.org/10.1377/hlthaff.2014.0041  
Beam A.L., Kohane I.S. Big data and machine learning in health care. JAMA. 2018;319:1317. DOI: https://doi.org/10.1001/jama.2017.18391  
Ben-Israel D., Jacobs W.B., Casha S., Lang S., Ryu W.H.A. The impact of machine learning on patient care: a systematic review. Artif. Intell. Med. 2019;103 DOI: https://doi.org/10.1016/j.artmed.2019.101785 .
Bhagyashree S.I.R., Nagaraj K., Prince M., Fall C.H.D., Krishna M. Diagnosis of dementia by machine learning methods in epidemiological studies: a pilot exploratory study from south India. Soc. Psychiatry Psychiatr. Epidemiol. 2017;53:77–86. DOI: https://doi.org/10.1007/s00127-017-1410-0 .
Bizzo B.C., Almeida R.R., Michalski M.H., Alkasab T.K. Artificial intelligence and clinical decision support for radiologists and referring providers. Journal of American College of Radiology. 2019;16:1351–1356.  DOI: https://doi.org/10.1016/j.jacr.2019.06.010 .
Buch V.H., Ahmed I., Maruthappu M. Artificial intelligence in medicine: current trends and future possibilities. Br. J. Gen. Pract. 2018;68:143–144. DOI: https://doi.org/10.3399/bjgp18x695213.
Charlebois S., Summan A. A risk communication model for food regulatory agencies in modern society. Trends Food Sci. Technol. 2015;45:153–165. DOI: https://doi.org/10.1016/j.tifs.2015.05.004
Chen S., Xu H., Liu D., Hu B., Wang H. A vision of IoT: applications, challenges, and opportunities with China perspective. IEEE Internet Things J. 2014;1:349–359.  DOI: https://doi.org/10.1109/jiot.2014.2337336.
Cinelli M., Quattrociocchi W., Galeazzi A., Valensise C., Brugnoli E. 2020. The COVID-19 Social Media Infodemic. Social and Information Networks (Cs.SI) Published in ArXiv 2020. arXiv:2003.05004. DOI: https://doi.org/10.48550/arXiv.2003.05004  
CIO, 2020. How the COVID-19 pandemic is reshaping healthcare with technology. https://www.cio.com/article/3534499/how-the-covid-19-pandemic-is-reshaping-healthcare-with-technology.html (accessed 29 March 2020). DOI: https://doi.org/10.3390/bioengineering10050611
CISION PR Newswire Flirtey and 7-eleven complete first month of routine commercial drone deliveries, deliver 77 packages to customer homes in United States. 2016. https://www.prnewswire.com/news-releases/flirtey-and-7-eleven-complete-first-month-of-routine-commercial-drone-deliveries-deliver-77-packages-to-customer-homes-in-united-states-300381798.html
CNBC Use of Surveillance to Fight Coronavirus Raises Concerns about Government Power after Pandemic Ends. 2020. https://www.cnbc.com/2020/03/27/coronavirus-surveillance-used-by- governments-to-fight-pandemic-privacy-concerns.html  
Conrow E.H. American Institute of Aeronautics & Astronautics; Reston, FL, USA: 2003. Effective Risk Management; pp. 22–74.
Covello V.T., McCallum D.B., Pavlova M. Springer; New York, NY, USA: 1987. Effective Risk Communication: The Role and Responsibility of Government and Nongovernment Organizations. DOI: https://doi.org/10.1111/risa.14006
CREA Changes in pollution level due to India's coronavirus curfew. 2020. https://energyandcleanair.org/janata-curfew-pollution-levels/ 
Crisan G.C., Nechita E. On a cooperative truck-and-drone delivery system. Procedia Computer Science. 2019;159:38–47.  DOI: https://doi.org/10.1016/j.procs.2019.09.158.
Dai X., Li T., Bai Z. Breast cancer intrinsic subtype classification, clinical use and future trends. Am. J. Cancer Res. 2015;5:2929–2943. DOI: https://doi.org/10.1021/acsomega.2c08227
Dente C.J., Bradley M., Schobel S., Gaucher B., Buchman T., Kirk A.D., Elster E. Towards precision medicine. J. Trauma Acute Care Surg. 2017;83:609–616. DOI: https://doi.org/10.1097/ta.0000000000001596 .
Diginomica BlueDot spotted coronavirus before anyone else had a clue. 2020. https://diginomica.com/how-canadian-ai-start-bluedot-spotted-coronavirus-anyone-else-had-clue DOI: https://doi.org/10.1016/B978-0-12-824313-8.00006-1
Dohr A., Modre-Opsrian R., Drobics M., Hayn D., Schreier G. 2010. The Internet of Things for Ambient Assisted Living. 2010 Seventh International Conference on Information Technology: New Generations; pp. 804–809. DOI: https://doi.org/10.1109/ITNG.2010.104
Domingo M.C. An overview of the Internet of Things for people with disabilities. J. Netw. Comput. Appl. 2012;35:584–596. DOI: https://doi.org/10.1016/j.jnca.2011.10.015.
Doyle O.M., Mehta M.A., Brammer M.J. The role of machine learning in neuroimaging for drug discovery and development. Psychopharmacology. 2015;232:4179–4189. DOI: https://doi.org/10.1007/s00213- 015-3968-0 .
echcrunch Dyson and Gtech support for COVID-19. 2020. https://techcrunch.com/2020/03/26/dyson-and-gtech-answer-uk-call-for-ventilator-design-and-production-to-support-covid-19-response/?guccounter=1  (Accessed 29 March 2020)
EdTechReview Countries Which Are Leading the Way in Online Education. 2020. https://edtechreview.in/e-learning/3028-countries-leading-in-online-education  DOI: https://doi.org/10.1007/s11356-021-13823-8
ertolaccini L., Solli P., Pardolesi A., Pasini A. An overview of the use of artificial neural networks in lung cancer research. Journal of Thoracic Disease. 2017;9:924–931. DOI: https://doi.org/10.21037/jtd.2017.03.157.
Escobar G.J., Turk B.J., Ragins A., Ha J., Hoberman B., LeVine S.M. Piloting electronic medical record-based early detection of inpatient deterioration in community hospitals. J. Hosp. Med. 2016;11:S18–S24. DOI: https://doi.org/10.1002/jhm.2652 .
esser-Edelsburg A., Shir-Raz Y., Hayek S., Sassoni-Bar Lev O. What does the public know about Ebola? The public’s risk perceptions regarding the current Ebola outbreak in an as-yet unaffected country. Am. J. Infect. Control. 2015;43:669–675. DOI: https://doi.org/10.1016/j.ajic.2015.03.005
European Food Safety Authority Risk communication. 2017. http://www.efsa.europa.eu/en/corporate/pub/riskcommguidelines170524  DOI: https://doi.org/10.2903/sp.efsa.2018.EN-1396
Fan Y.J., Yin Y.H., Xu L.D., Zeng Y., Wu F. IoT-based smart rehabilitation system. IEEE Transactions on Industrial Informatics. 2014;10:1568–1577.  DOI: https://doi.org/10.1109/tii.2014.2302583.
Financial Express Tech-Ing on Covid-19. 2020. https://www.financialexpress.com/opinion/tech-ing- on-covid-19-govt-must-extensively-use-tech-solutions-to-fight-coronavirus/1909609/  DOI: https://doi.org/10.1016/j.matchemphys.2020.123943
Flightradar24 Flight Tracking Statistics. 2020. https://www.flightradar24.com/data/statistics  
Fuller C., Cellura A.P., Hibler B.P., Burris K. Computer-assisted diagnosis of melanoma. Seminars in Cutaneous Medicine and Surgery. 2016;35:25–30. DOI: https://doi.org/10.12788/j.sder.2016.004  
GCN Tech called up in the war against the unexpected. 2020. https://gcn.com/articles/2020/03/19/downstream-tech-effects-pandemic.aspx  
Goldberg J.E., Rosenkrantz A.B. Artificial intelligence and radiology: a social media perspective. Curr. Probl. Diagn. Radiol. 2018;48:308–311. DOI: https://doi.org/10.1067/j.cpradiol.2018.07.005 .
Greaves R., Bernardini S., Ferrari M., Fortina P., Gouget B., Gruson D. Key questions about the future of laboratory medicine in the next decade of the 21st century: a report from the IFCC-emerging technologies division. Clin. Chim. Acta. 2019;495:570–589.  DOI: https://doi.org/10.1016/j.cca.2019.05.021.
Gruson D., Helleputte T., Rousseau P., Gruson D. Data science, artificial intelligence, and machine learning: opportunities for laboratory medicine and the value of positive regulation. Clin. Biochem. 2019;69:1–7. DOI: https://doi.org/10.1016/j.clinbiochem.2019.04.013  
Guess A., Nagler J., Tucker J. Less than you think: prevalence and predictors of fake news dissemination on Facebook. Sci. Adv. 2019;5 DOI: https://doi.org/10.1126/sciadv.aau4586
Haleem A., Javaid D.M., Khan I.H. Current status and applications of artificial intelligence (AI) in medical field: an overview. Current Medicine Research and Practice. 2019;9:231–237.  DOI: https://doi.org/10.1016/j.cmrp.2019.11.005 .
Haleem A., Javaid M. Industry 5.0 and its expected applications in medical field. Current Medicine Research and Practice. 2019;9:167–169 DOI: https://doi.org/10.1016/j.cmrp.2019.07.002
Hall R., Pasipanodya J., Swancutt M., Meek C., Leff R., Gumbo T. Supervised machine-learning reveals that old and obese people achieve low dapsone concentrations. CPT Pharmacometrics Syst. Pharmacol. 2017;6:552–559. DOI: https://doi.org/10.1002/psp4.12208 .
Hamet P., Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69:S36–S40. DOI: https://doi.org/10.1016/j.metabol.2017.01.011  
Hathaliya J.J., Tanwar S. An exhaustive survey on security and privacy issues in Healthcare 4.0. Comput. Commun. 2020;153:311–335.  DOI: https://doi.org/10.1016/j.comcom.2020.02.018 .
Heinson A., Gunawardana Y., Moesker B., Hume C., Vataga E., Hall Y. Enhancing the biological relevance of machine learning classifiers for reverse vaccinology. Int. J. Mol. Sci. 2017;18:312. DOI: https://doi.org/10.3390/ijms18020312  
Hinton G., Deng L., Yu D., Dahl G., Mohamed A., Jaitly N. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 2012;29:82–97. DOI: https://doi.org/10.1109/msp.2012.2205597 .
Ho C.W.L., Soon D., Caals K., Kapur J. Governance of automated image analysis and artificial intelligence analytics in healthcare. Clin. Radiol. 2019;74:329–337.  DOI: https://doi.org/10.1016/j.crad.2019.02.005.
Huynh T.L.D. The COVID-19 risk perception: a survey on socioeconomics and media attention. Econ. Bull. 2020;40:758–764. DOI: https://doi.org/10.1016/j.dib.2020.105530
idyasagar M. Identifying predictive features in drug response using machine learning: opportunities and challenges. Annu. Rev. Pharmacol. Toxicol. 2015;55:15–34. DOI: https://doi.org/10.1146/annurev-pharmtox- 010814-124502 .
India Today Rapid antibody tests. 2020. https://www.medicaldevice-network.com/features/types-of- covid-19-test-antibody-pcr-antigen/ 
ITN Dutch companies offer free innovative COVID-19 AI software. 2020. https://www.itnonline.com/content/dutch-companies-offer-free-innovative-covid-19-ai-software DOI: https://doi.org/10.1007/s13205-020-02581-y
ITN Two Studies Use SIRD Model to Forecast COVID-19 Spread. 2020. https://www.itnonline.com/content/two-studies-use-sird-model-forecast-covid-19-spread (Accessed 2 April 2020) DOI: https://doi.org/10.1016/j.chaos.2021.111039
Jiang F., Jiang Y., Zhi H., Dong Y., Li H., Ma S. Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology. 2017;2:230–243.  DOI: https://doi.org/10.1136/svn-2017-000101
Jiang X., Coffee M., Bari A., Wang J., Jiang X. Towards an artificial intelligence framework for data- driven prediction of coronavirus clinical severity. CMC-Computers, Materials & Continua. 2020;63:537–551. DOI: https://doi.org/10.32604/cmc.2020.010691 .
jordona K., Dossou P.-E., Junior J.C. Using lean manufacturing and machine learning for improving medicines procurement and dispatching in a hospital. Procedia Manufacturing. 2019;38:1034– 1041. DOI: https://doi.org/10.1016/j.promfg.2020.01.189  
Kellermann R., Biehle T., Fischer L. Transportation Research Interdisciplinary Perspectives. 2020. Drones for parcel and passenger transportation: a literature review; p. 100088. In Press corrected proof DOI: https://doi.org/10.1016/j.trip.2019.100088
Kidwai B., Nadesh R.K. Design and development of diagnostic Chabot for supporting primary health care systems. Procedia Computer Science. 2020;167:75–84.  DOI: https://doi.org/10.1016/j.procs.2020.03.184 .
Kim, S. J., Cho, K. J., & Oh, S. (2017). Development of machine learning models for diagnosis of glaucoma. PloS one, 12(5). DOI: https://doi.org/10.1371/journal.pone.0177726
Klaviyo Key trends in brands. 2020. https://www.klaviyo.com/covid-19-ecommerce-marketing-poll  (Accessed 28 March 2020)
Krizhevsky A., Sutskever I., Hinton G.E. ImageNet classification with deep convolutional neural networks. Commun. ACM. 2017;60:84–90. DOI: https://doi.org/10.1145/3065386 .
Kulikowski C. Beginnings of artificial intelligence in medicine (AIM): computational artifice assisting scientific inquiry and clinical art - with reflections on present AIM challenges. Yearbook of Medical Informatics. 2019 DOI: https://doi.org/10.1055/s-0039-1677895.
Kulkarni S., Seneviratne N., Baig M.S., Khan A.H.A. Artificial intelligence in medicine: where are we now? Acad. Radiol. 2019 DOI: https://doi.org/10.1016/j.acra.2019.10.001  
Kuo, C.-C., Chang, C.-M., Liu, K.-T., Lin, W.-K., Chiang, H.-Y., Chung, C.-W., et al., 2019. Automation of the kidney function prediction and classification through ultrasound-based kidney imaging using deep learning. Npj Digital Medicine, 2, 29. DOI: https://doi.org/10.1038/s41746-019-0104-2
Lai, C. C., Shih, T. P., Ko, W. C., Tang, H. J., & Hsueh, P. R. (2020). Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and corona virus disease-2019 (COVID-19): the epidemic and the challenges. International journal of antimicrobial agents, 105924 DOI: https://doi.org/10.1016/j.ijantimicag.2020.105924
Lakhani P., Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284:574–582. DOI: https://doi.org/10.1148/radiol.2017162326 .
Lee I., Shin Y.J. Machine learning for enterprises: applications, algorithm selection, and challenges. Business Horizons. 2020;63:157–170. DOI: https://doi.org/10.1016/j.bushor.2019.10.005 .
Lei Y., Yang B., Jiang X., Jia F. Applications of machine learning to machine fault diagnosis: a review and roadmap. Mech. Syst. Signal Process. 2020;138 DOI: https://doi.org/10.1016/j.ymssp.2019.106587 .
Leo Kumar S.P. State of the art-intense review on artificial intelligence systems application in process planning and manufacturing. Eng. Appl. Artif. Intell. 2017;65:294–329.  DOI: https://doi.org/10.1016/j.engappai.2017.08.005 .
Liang H., Tsui B.Y., Ni H. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Natural Medicine. 2019;25:433–438. DOI: https://doi.org/10.1038/s41591-018-0335-9
Liu, W., Cao, J., Yang, L., Xu, L., Qiu, X., & Li, J. (2016). AppBooster: Boosting the performance of interactive mobile applications with computation offloading and parameter tuning. IEEE Transactions on Parallel and Distributed Systems, 28(6), 1593-1606. DOI: https://doi.org/10.1109/TPDS.2016.2624733.
Lo-Ciganic W.-H., Donohue J.M., Thorpe J.M., Perera S., Thorpe C.T., Marcum Z.A., Gellad W.F. Using machine learning to examine medication adherence thresholds and risk of hospitalization. Med. Care. 2015;53:720–728. DOI: https://doi.org/10.1097/mlr.0000000000000394 .
Lundberg S.M., Nair B., Vavilala M.S. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nature Biomedical Engineering. 2018;2:749–760. DOI: https://doi.org/10.1038/s41551-018-0304-0 .
Lundgren R.E., McMakin A.H. John Wiley & Sons, Inc; Hoboken, NJ, USA: 2013. Risk Communication: A Handbook for Communicating Environmental, Safety, and Health Risks. DOI: https://doi.org/10.1002/9781118645734
Luo J., Wu M., Gopukumar D., Zhao Y. Big data application in biomedical research and health care: a literature review. Biomedical Informatics Insights. 2016;8:BII.S31559. DOI: https://doi.org/10.4137/bii.s31559 .
Mai M.V., Krauthammer M. Controlling testing volume for respiratory viruses using machine learning and text mining. AMIA annual symposium proceedings. AMIA Symposium. 2017;2016:1910– 1919 DOI: https://doi.org/10.1101/2020.07.18.20156794  
Marella W.M., Sparnon E., Finley E. Screening electronic health record–related patient safety reports using machine learning. Journal of Patient Safety. 2017;13:31–36. DOI: https://doi.org/10.1097/pts.0000000000000104 .
McLean, G., & Wilson, A. (2019). Shopping in the digital world: Examining customer engagement through augmented reality mobile applications. Computers in Human Behavior, 101, 210-224. DOI: https://doi.org/10.1016/j.chb.2019.07.002
Mesar, T., Lessig, A., King, D.R., 2018. Use of drone technology for delivery of medical supplies during prolonged field care. Journal of Special Operations Medicine. 18, 34–35. DOI: https://doi.org/10.55460/M63P-H7DM
Miorandi D., Sicari S., De Pellegrini F., Chlamtac I. Internet of things: vision, applications and research challenges. Ad Hoc Netw. 2012;10:1497–1516.  DOI: https://doi.org/10.1016/j.adhoc.2012.02.016.
Misawa M., Kudo S., Mori Y., Cho T., Kataoka S. Artificial intelligence-assisted polyp detection for colonoscopy: initial experience. Gastroenterology. 2018;154:2027–2029.e3 DOI: https://doi.org/10.1053/j.gastro.2018.04.003
Mishra B., Garg D., Narang P., Mishra V. Drone-surveillance for search and rescue in natural disaster. Comput. Commun. 2020;156:1–10. DOI: https://doi.org/10.1016/j.comcom.2020.03.012
MIT Technology Review A new app would say if you've crossed paths with someone who is infected. 2020. https://www.technologyreview.com/2020/03/17/905257/coronavirus-infection-tests-app-pandemic-location-privacy/ DOI: https://doi.org/10.1016/j.scitotenv.2020.138858
MIT Technology Review AI Could Help with the Next Pandemic - But Not with this One. 2020. https://www.technologyreview.com/2020/03/12/905352/ai-could-help-with-the-next-pandemicbut-not-with-this-one/  DOI: https://doi.org/10.1038/s41746-021-00459-8
MIT Technology Review Over 24,000 Coronavirus Research Papers Are Now Available in One Place. 2020. https://www.technologyreview.com/2020/03/16/905290/coronavirus-24000-research-papers-available-open-data/ DOI: https://doi.org/10.1016/j.scitotenv.2020.138858
Moser E.C., Narayan G. Improving breast cancer care coordination and symptom management by using AI driven predictive toolkits. Breast. 2020;50:25–29.  DOI: https://doi.org/10.1016/j.breast.2019.12.006 .
Murdoch T.B., Detsky A.S. The inevitable application of big data to health care. JAMA. 2013;309:1351. DOI: https://doi.org/10.1001/jama.2013.393  
NS Medical Devices Manufacturing of key medical kit during Covid-19. 2020. https://www.nsmedicaldevices.com/analysis/companies-ventilators-shortage-coronavirus/  (Accessed 2 April 2020)
Nuanmeesri, S. (2019). Mobile application for the purpose of marketing, product distribution and location-based logistics for elderly farmers. Applied Computing and Informatics. DOI: https://doi.org/10.1016/j.aci.2019.11.001
oga A.W., Foster I., Kesselman C., Madduri R., Chard K., Deutsch E.W. Big biomedical data as the key resource for discovery science. J. Am. Med. Inform. Assoc. 2015;22:1126–1131. DOI: https://doi.org/10.1093/jamia/ocv077  
Özdemir A., Barshan B. Detecting falls with wearable sensors using machine learning techniques. Sensors. 2014;14:10691–10708. DOI: https://doi.org/10.3390/s14061069  
Patel V.L., Shortliffe E.H., Stefanelli M., Szolovits P., Berthold M.R., Bellazzi R., Abu-Hanna A. The coming of age of artificial intelligence in medicine. Artif. Intell. Med. 2009;46:5–17.  DOI: https://doi.org/https://doi.org/10.1016/j.artmed.2008.07.017.
Perou C.M., Sorlie T., Eisen M.B., van de Rijn M., Jeffrey S.S. Molecular portraits of human breast tumours. Nature. 2000;406:747–752. DOI: https://doi.org/10.1038/35021093 .
Poljak M., Sterbenc A. Use of drones in clinical microbiology and infectious diseases: current status, challenges and barriers. Clin. Microbiol. Infect. 2019;26:425–430.  DOI: https://doi.org/10.1016/j.cmi.2019.09.014.
POSOCO Load demand data. 2020. https://posoco.in/  (Accessed 1 April 2020)
Priye A., Wong S., Bi Y., Carpio M., Chang J., Coen M. Lab-on-a-drone: toward pinpoint deployment of smartphone-enabled nucleic acid-based diagnostics for mobile health care. Anal. Chem. 2016;88:4651–4660.  DOI: https://doi.org/10.1021/acs.analchem.5b04153.
Rajpurkar P., Irvin J., Zhu K., Yang B. CheXNet: radiologist-level pneumonia detection on chest X- rays with deep learning. ArXiv. 2017 https://arxiv.org/abs/1711.05225  DOI: https://doi.org/10.48550/arXiv.1711.05225
Ratzan, S. C., Gostin, L. O., Meshkati, N., Rabin, K., & Parker, R. M. (2020). COVID-19: An Urgent Call for Coordinated, Trusted Sources to Tell Everyone What they Need to Know and Do. NAM Perspectives DOI: https://doi.org/10.1080/10810730.2020.1894015  
rench S. Expert judgment, meta-analysis, and participatory risk analysis. Decis. Anal. 2012;9:119– 127. DOI: https://doi.org/10.1287/deca.1120.0234
Renn O. Earthscan; London, UK: 2008. Risk Governance: Coping with Uncertainty in a Complex World. DOI: https://doi.org/10.1007/978-1-4020-6799-0
Richardson M.L., Garwood E.R., Lee Y., Li M.D., Lo H.S. Noninterpretive uses of artificial intelligence in radiology. Acad. Radiol. 2020  DOI: https://doi.org/10.1016/j.acra.2020.01.012 . In Press.
Rosser J.C., Vignesh V., Terwilliger B.A., Parker B.C. Surgical and medical applications of drones: a comprehensive review. Journal of the Society of Laparoendoscopic Surgeons. 2018;22 . DOI: https://doi.org/10.4293/jsls.2018.00018.
Rumsfeld J.S., Joynt K.E., Maddox T.M. Big data analytics to improve cardiovascular care: promise and challenges. Nat. Rev. Cardiol. 2016;13:350–359. DOI: https://doi.org/10.1038/nrcardio.2016.42 .
Sahni, Y., Cao, J., Zhang, S., & Yang, L. (2017). Edge mesh: A new paradigm to enable distributed intelligence in internet of things. IEEE access, 5, 16441-16458. DOI: https://doi.org/10.1109/ACCESS.2017.2739804
Saria S., Koller D., Penn A. Proceedings of Neural Information Processing Systems (NIPS) Predictive Models in Personalized Medicine. 2010. Learning individual and population level traits from clinical temporal data. (Whistler) DOI: https://doi.org/10.1377/hlthaff.2014.0041
Savage L.J. The theory of statistical decision. J. Am. Stat. Assoc. 1951;46:55–67. DOI: https://doi.org/10.1080/01621459.1951.10500768
Sjöberg, L. (2000). Factors in risk perception. Risk analysis, 20(1), 1-12. DOI: https://doi.org/10.1111/0272-4332.00001
Slovic P. Perception of risk. Science. 1987;236(4799):280–285. DOI: https://doi.org/10.1126/science.3563507
Sorlie T., Perou C.M., Tibshirani R., Aas T., Geisler S., Johnsen H. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci. 2001;98:10869–10874. DOI: https://doi.org/10.1073/pnas.191367098
STAT Surge in patients overwhelms telehealth services amid coronavirus pandemic. 2020. https://www.statnews.com/2020/03/17/telehealth-services-overwhelmed-amid-coronavirus- pandemic/ (Accessed 20 March 2020)  DOI: https://doi.org/10.2196/19264
Stevic Z., Pamucar D., Puska A., Chatterjee P. Sustainable supplier selection in healthcare industries using a new MCDM method: measurement alternatives and ranking according to COmpromise solution (MARCOS) Comput. Ind. Eng. 2019;140:106231. DOI: https://doi.org/10.1016/j.cie.2019.106231
The Economic Times Robots help combat COVID-19 in world, and maybe soon in India too. 2020. https://economictimes.indiatimes.com/news/science/robots-help-combat-covid-19-in-world-and-maybe-soon-in-india-too/articleshow/74893405.cms  (Accessed 1 April 2020) DOI: https://doi.org/10.1109/ACCESS.2020.3045792
The New York Times An Island Nation's Health Experiment: Vaccines Delivered by Drone. 2018. https://www.nytimes.com/2018/12/17/health/vanuatu-vaccines-drones.html  DOI: https://doi.org/10.3390/healthcare10102102
Thomson Reuters Foundation News AI-powered technology. 2020. https://news.trust.org/item/20200316140626-x791z/  (Accessed 19 March 2020)
Tojo Y. Clinical sequence of leukemia applying artificial intelligence (AI) Clinical Bood. 2017;58:1913–1917. DOI: https://doi.org/10.11406/rinketsu.58.1913 .
Ullah Z., Al-Turjman F., Mostarda L., Gagliardi R. Applications of artificial intelligence and machine learning in smart cities. Comput. Commun. 2020;154:313–323 DOI: https://doi.org/10.1016/j.comcom.2020.02.069 .
Upadhyay A., Khandelwal K. Artificial intelligence-based training learning from application. Development and Learning in Organizations. 2019;33:20–23.  DOI: https://doi.org/10.1108/DLO-05-2018-0058 .
utodesk-Redshift Companies Help to Fight COVID-19. 2020. https://www.autodesk.com/redshift/manufacturing-covid-19/
Vacca A., Onishi H. Drones: military weapons, surveillance or mapping tools for environmental monitoring? The need for legal framework is required. Transportation Research Procedia. 2017;25:51–62.  DOI: https://doi.org/10.1016/j.trpro.2017.05.209.
Valdes, K., Gendernalik, E., Hauser, J., & Tipton, M. (2020). Use of mobile applications in hand therapy. Journal of Hand Therapy. DOI: https://doi.org/10.1016/j.jht.2019.10.003
Valdes, K., Gendernalik, E., Hauser, J., & Tipton, M. (2020). Use of mobile applications in hand therapy. Journal of Hand Therapy. DOI: https://doi.org/10.1016/j.jht.2019.10.003
Van Bavel J.J., Baicker K., Boggio P., Capraro V., Cichocka A., Crockett M. Using social and behavioural science to support COVID-19 pandemic response. PsyArXiv preprints. 2020 DOI: https://doi.org/10.31234/osf.io/y38m9
Verdict Medical devices Screening for Covid-19. 2020. https://www.medicaldevice-network.com/features/types-of-covid-19-test-antibody-pcr-antigen/ (Accessed 4 April 2020)
Wahl B., Cossy-Gantner A., Germann S., Schwalbe N.R. Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? BMJ Glob. Health. 2018;3  DOI: https://doi.org/10.1136/bmjgh-2018-000798.
Wan, S., Gu, Z., & Ni, Q. (2020). Cognitive computing and wireless communications on the edge for healthcare service robots. Computer Communications, 149, 99-106. DOI: https://doi.org/10.1016/j.comcom.2019.10.012.
Wan, S., Zhao, Y., Wang, T., Gu, Z., Abbasi, Q. H., & Choo, K. K. R. (2019). Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things. Future Generation Computer Systems, 91, 382-391. DOI: https://doi.org/10.1016/j.future.2018.08.007
Wang L., Li J., Guo S., Xie N. Real-time estimation and prediction of mortality caused by COVID-19 with patient information-based algorithm. Sci. Total Environ. 2020 DOI: https://doi.org/10.1016/j.scitotenv.2020.138394 . In Press
Wang L., Li J., Guo S., Xie N. Real-time estimation and prediction of mortality caused by COVID-19 with patient information-based algorithm. Sci. Total Environ. 2020 DOI: https://doi.org/10.1016/j.scitotenv.2020.138394 . In Press
Wang S., Kang B., Ma J., Zeng X., Xiao M. A deep learning algorithm using CT images to screen for corona virus disease (COVID-19) medRxiv. 2020 DOI: https://doi.org/10.1101/2020.02.14.20023028.  Preprint.
Weinstein, N. D. (1988). The precaution adoption process. Health psychology, 7(4), 355. DOI: https://doi.org/10.1037/0278-6133.7.4.355
Weintraub W.S., Fahed A.C., Rumsfeld J.S. Translational medicine in the era of big data and machine learning. Circ. Res. 2018;123:1202–1204. DOI: https://doi.org/10.1161/circresaha.118.313944  
WHO Basic protective measures against the new coronavirus. 2020. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public  (Accessed 18 March 2020)
WHO Coronavirus disease (COVID-2019) situation reports. 2020. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/ 
WHO Declaration of Public Health Emergency of International Concern. 2020. (Accessed 2 March 2020)1 DOI: https://doi.org/10.2807/1560-7917.ES.2020.25.5.200131e
WHO Guidelines. 2020. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance  (Accessed 6 April 2020)
WHO The COVID-19 Risk Communication Package for Healthcare Facilities. 2020. https://iris.wpro.who.int/bitstream/handle/10665.1/14482/COVID-19-022020.pdf
World Economic Forum Changes due to coronavirus impact. 2020. https://www.weforum.org/agenda/2020/04/covid-19-things-to-know-about-coronavirus-2-april/  (Accessed 3 April 2020)
World Economic Forum Government and companies response to COVID-19. 2020. https://www.weforum.org/agenda/2020/03/how-are-companies-responding-to-the-coronavirus- crisis-d15bed6137  
World Economic Forum Innovation in meeting the ventilator demands. 2020. https://www.weforum.org/agenda/2020/03/coronavirus-ventilators-covid19-healthcare  
Yan H., Xu L.D., Bi Z., Pang Z., Zhang J., Chen Y. An emerging technology - wearable wireless sensor networks with applications in human health condition monitoring. Journal of Management Analytics. 2015;2:121–137.  DOI: https://doi.org/10.1080/23270012.2015.1029550.
Yang L.B. Application of artificial intelligence in electrical automation control. Procedia Computer Science. 2020;166:292–295. DOI: https://doi.org/10.1016/j.procs.2020.02.097 .
Yi, S., Moon, D., Yang, Y., & Kim, K. (2008). Healthcare robot technology development. IFAC Proceedings Volumes, 41(2), 5318-5323. DOI: https://doi.org/10.37624/IJERT/13.6.2020.1266-1272
Ying W., Qian Y., Kun Z. Drugs supply and pharmaceutical care management practices at a designated hospital during the COVID-19 epidemic. Res. Soc. Adm. Pharm. 2020 . DOI: https://doi.org/10.1016/j.sapharm.2020.04.001.
Yuan M., Yin W., Tao Z., Tan W., Hu Y. Association of radiologic findings with mortality of patients infected with 2019 novel coronavirus in Wuhan, China. PLoS One. 2020;15 DOI: https://doi.org/10.1371/journal.pone.0230548 .
Zafeiris D., Rutella S., Ball G.R. An artificial neural network integrated pipeline for biomarker discovery using Alzheimer’s disease as a case study. Computational and Structural Biotechnology Journal. 2018;16:77–87. DOI: https://doi.org/10.1016/j.csbj.2018.02.001
Zeng X., Luo G. Progressive sampling-based Bayesian optimization for efficient and automatic machine learning model selection. Health Information Science and Systems. 2017;5:2. DOI: https://doi.org/10.1007/s13755-017-0023-z .
Zhang L., Li H., Chen K. Effective risk communication for public health emergency: reflection on the COVID-19 (2019-nCoV) outbreak in Wuhan, China. Healthcare. 2020;8:64. DOI: https://doi.org/10.3390/healthcare8010064
Zlobec I. A predictive model of rectal tumor response to preoperative radiotherapy using classification and regression tree methods. Clin. Cancer Res. 2005;11:5440–5443. DOI: https://doi.org/10.1158/1078-0432.ccr- 04-2587  
Zoroja J., Merkac Skok M., Pejic Bach M. 2014. E-Learning Implementation in Developing Countries: Perspectives and Obstacles. Online Tutor 2.0: Methodologies and Case Studies for Successful Learning. DOI: https://doi.org/10.4018/978-1-4666-5832-5.ch004
Send comment about this article
CAPTCHA Image

Files

Share

How to cite

Statistics