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Artificial Intelligence Applications in Tracking Health Behaviors During Disease Epidemics

  • Kurubaran GanasegeranEmail author
  • Surajudeen Abiola Abdulrahman
Chapter
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 6)

Abstract

The threat of emerging and re-emerging infectious diseases to global population health remains significantly enormous, and the pandemic preparedness capabilities necessary to confront such threats must be of greater potency. Artificial Intelligence (AI) offers new hope in not only effectively pre-empting, preventing and combating the threats of infectious disease epidemics, but also facilitating the understanding of health-seeking behaviors and public emotions during epidemics. From a systems-thinking perspective, and in today’s world of seamless boundaries and global interconnectivity, AI offers enormous potential for public health practitioners and policy makers to revolutionize healthcare and population health through focussed, context-specific interventions that promote cost-savings on therapeutic care, expand access to health information and services, and enhance individual responsibility for their health and well-being. This chapter systematically appraises the dawn of AI technology towards empowering population health to combat the rise of infectious disease epidemics.

Keywords

Artificial intelligence Health behaviors Epidemics Infectious disease Global health 

Notes

Acknowledgements

We thank the Ministry of Health Malaysia for the support to publish this chapter.

References

  1. 1.
    K.L. Tsui, Z.S.Y. Wong, D. Goldsman, M. Edesess, Tracking infectious disease spread for global pandemic containment. IEEE Intell. Syst. 28(6), 60–64 (2013)CrossRefGoogle Scholar
  2. 2.
    D. Baud, D.J. Gubler, B. Schaub, M.C. Lanteri, D. Musso, An update on Zika virus infection. Lancet 390, 2099–2109 (2017)CrossRefGoogle Scholar
  3. 3.
    I.R.F. da Silva, J.A. Frontera, A.M.B. de Filippis, O.J.M.D. Nascimento, RIO-GBS-ZIKV Research Group, Neurologic complications associated with the Zika virus in Brazilian adults. JAMA. Neurol. 74(10), 1190–1198 (2017)Google Scholar
  4. 4.
    B. Mesko, G. Hetenyi, Z. Gyorffy, Will artificial intelligence solve the human crisis in healthcare? BMC Health. Serv. Res. 18, 545 (2018)CrossRefGoogle Scholar
  5. 5.
    Z.S.Y. Wong, J. Zhou, Q. Zhang, Artificial intelligence for infectious disease big data analytics. Infect. Dis. Health. 24, 44–48 (2019)CrossRefGoogle Scholar
  6. 6.
    S. Michie, J. Thomas, M. Johnston, P.M. Aonghusa, J. Shawe-Taylor, M.P. Kelly, L.A. Deleris, A.N. Finnerty, M.M. Marques, E. Norris, A. O’Mara-Eves, R. West, The human behavior-change project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation. Implement. Sci. 12, 121 (2017)CrossRefGoogle Scholar
  7. 7.
    H. Kagermann, H. Johannes, H. Ariane, W. Wolfgang, Recommendations for Implementing the Strategic Initiative INDUSTRIE 4.0: Securing the Future of German Manufacturing Industry. Final Report of the Industrie 4.0 Working Group (Forschungsunion, Frankfurt, Germany, 2013)Google Scholar
  8. 8.
    AI for Good Global Summit, Geneva (2017) http://www.itu.int/en/ITU-T/AI/Pages/201706-default.aspx
  9. 9.
    United Nations: Looking to Future UN to Consider How Artificial Intelligence Could Help Achieve Economic Growth and Reduce Inequalities, http://www.un.org/sustainabledevelopment/blog/2017/10/looking-to-future-un-to-consider-how-artificial-intelligence-could-help-achieve-economic-growth-and-reduce-inequalities/2017
  10. 10.
    J. Ginsberg, M.H. Mohebbi, R.S. Patel, L. Brammer, M.S. Smolinski, L. Brilliant, Detecting influenza epidemics using search engine query data. Nature 457(7232), 1012e4 (2009)CrossRefGoogle Scholar
  11. 11.
    A. Mavragani, G. Ochoa, Google Trends in infodemiology and infoveillance: methodology framework. JMIR Public Health Surveill. 5(2), e13439 (2019)CrossRefGoogle Scholar
  12. 12.
    A.R. Daughton, M.J. Paul, Identifying protective health behaviors on Twitter: observational study of travel advisories and Zika virus. J. Med. Internet Res. 21(5), e13090 (2019)CrossRefGoogle Scholar
  13. 13.
    A. Signorini, A.M. Segre, P.M. Polgreen, The use of Twitter to track levels of disease activity and public concern in the US during the influenza A H1N1 pandemic. PLoS ONE 6(5), e19467 (2011)CrossRefGoogle Scholar
  14. 14.
    V. Gianfredi, N.L. Bragazzi, D. Nucci, M. Martini, R. Rosselli, L. Minelli, M. Moretti, Harnessing big data for communicable tropical and sub-tropical disorders: implications from a systematic review of the literature. Front. Public Health 6, 90 (2018)Google Scholar
  15. 15.
    Air Transport Statistics 2018. International Air Transport Association (IATA), http://www.iata.org/services/statistics/air-transport-stats/Pages/index.aspx
  16. 16.
    N.L. Bragazzi, V. Gianfredi, M. Villarini, R. Rosselli, A. Nasr, A. Hussein, M. Martini, M. Behzadifar, Vaccines meet big data: state-of-the-art and future prospects. From the classical 3Is (“isolate-inactivate-inject”) Vaccinology 1.0 to Vaccinology 3.0, vaccinomics and beyond: a historical overview. Front. Public Health 6, 62 (2018)Google Scholar
  17. 17.
    J. Mossong, N. Hens, M. Jit, P. Beutels, K. Auranen, R. Mikolajczyk, M. Massari, S. Salmaso, G.S. Tomba, J. Wallinga, J. Heijne, M. Sadkowska-Todys, M. Rosinska, W.J. Edmunds, Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Med. 5(3), e74 (2008)CrossRefGoogle Scholar
  18. 18.
    D. da Silva Motta, R. Badaro, A. Santos, F. Kirchner, Chapter 7: Use of artificial intelligence on the control of vector-borne diseases, in Vectors and Vector-Borne Zoonotic Diseases, ed. by S. Savic (IntechOpen, United Kingdom, 2018). ISBN 978-1-78985-293-6Google Scholar
  19. 19.
    C.S. Malley, J.C. Kuylenstierna, H.W. Vallack, D.K. Henze, H. Blencowe, M.R. Ashmore, Preterm birth associated with maternal fine particulate matter exposure: a global, regional and national assessment. Environ. Int. 101, 173–182 (2017)CrossRefGoogle Scholar
  20. 20.
    B. Wahl, A. Cossy-Gantner, S. Germann, N.R. Schwalbe, Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? BMJ Glob. Health 3, e000798 (2018)CrossRefGoogle Scholar
  21. 21.
    J.P. Munoz, R. Boger, S. Dexter, J. Li, R. Low, Image recognition of disease-carrying insects: a system for combating infectious diseases using image classification techniques and citizen science, in Proceedings of the 51st Hawaii International Conference on System Sciences (HICSS, 2018), pp. 2835–2844. ISBN 978-0-9981331-1-9Google Scholar
  22. 22.
    G. Fleming, M. Mvander, G. McFerren, Fuzzy expert systems and GIS for cholera health risk prediction in southern Africa. Environ. Model. Softw. 22, 442–448 (2007)CrossRefGoogle Scholar
  23. 23.
    G. Eysenbach, Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the internet. J. Med. Internet Res. 11(1), e11 (2009)CrossRefGoogle Scholar
  24. 24.
    G. Eysenbach, Infodemiology and infoveillance: tracking online health information and cyber-behavior for public health. Am. J. Prev. Med. 40(5), S154–S158 (2011)CrossRefGoogle Scholar
  25. 25.
    H.T. Ho, T.M. Carvajal, J.R. Bautista, J.D.R. Capistrano, K.M. Viacrusis, L.F.T. Hernandez, K. Watanabe, Using Google Trends to examine the spatio-temporal incidence and behavioral patterns of dengue disease: a case study in metropolitan Manila, Philippines. Trop. Med. Infect. Dis. 3, 118 (2018)CrossRefGoogle Scholar
  26. 26.
    C. Alicino, N.L. Bragazzi, V. Faccio, D. Amicizia, D. Panatto, R. Gasparini, G. Icardi, A. Orsi, Assessing Ebola-related web search behavior: insights and implications from an analytical study of Google Trends-based query volumes. Infect. Dis. Poverty 4, 54 (2015)CrossRefGoogle Scholar
  27. 27.
    N. Mahroum, M. Adawi, K. Sharif, R. Waknin, H. Mahagna, B. Bisharat, M. Mahamid, A. Abu-Much, H. Amital, N.L. Bragazzi, A. Watad, Public reaction to Chikungunya outbreaks in Italy—insights from an extensive novel data streams-based structural equation modeling analysis. PLoS ONE 13(5), e0197337 (2018)CrossRefGoogle Scholar
  28. 28.
    O. Oluwagbemi, E. Adeoye, S. Fatumo, Building a computer-based expert system for malaria environmental diagnosis: an alternative malaria control strategy. Egypt. Comput. Sci. J. 33(1), 55–69 (2009)Google Scholar
  29. 29.
    A. Sheikhtaheri, F. Sadoughi, Z.H. Dehaghi, Developing and using expert systems and neural networks in medicine: a review on benefits and challenges. J. Med. Syst. 38, 110 (2014)CrossRefGoogle Scholar
  30. 30.
    A. Caliskan, J.J. Bryson, A. Narayanan, Semantics derived automatically from language corpora contain human-like biases. Science 356, 183–186 (2017)CrossRefGoogle Scholar
  31. 31.
    J.L.K. Angwin, S. Mattu, L. Kirchner, Machine Bias (ProPublica, 2016)Google Scholar
  32. 32.
    R. Moss, A.E. Zarebski, S.J. Carlson, J.M. McCaw, Accounting for healthcare-seeking behaviors and testing practices in real-time influenza forecasts. Trop. Med. Infect. 4(1), 12 (2019)CrossRefGoogle Scholar
  33. 33.
    IEEE Symposium, Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems. Security and Privacy (SP) (IEEE, 2016)Google Scholar
  34. 34.
    A. Shaban-Nejad, M. Michalowski, D.L. Buckeridge, Health intelligence: how artificial intelligence transforms population and personalized health. NPJ Digit. Med. 1, 53 (2018)CrossRefGoogle Scholar
  35. 35.
    S. Feng, K.A. Grepin, R. Chunara, Tracking health seeking behavior during an Ebola outbreak via mobile phones and SMS. NPJ Digit. Med. 1(1), 51 (2018)CrossRefGoogle Scholar
  36. 36.
    K. Ganasegeran, S.A. Abdulrahman, Adopting m-Health in clinical practice: a boon or a bane?, in Telemedicine Technologies, ed. by H.D. Jude, V.E. Balas (Elsevier Academic Press, United States, 2019), pp. 31–41CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Kurubaran Ganasegeran
    • 1
    Email author
  • Surajudeen Abiola Abdulrahman
    • 2
  1. 1.Clinical Research CenterSeberang Jaya Hospital, Ministry of Health MalaysiaPenangMalaysia
  2. 2.Emergency Medicine DepartmentJames Paget University HospitalGreat YarmouthUK

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