Artificial Intelligence for infectious disease Big Data Analytics

Infection, Disease and Health

Volumn 24, Issue 1, 2019, Pages 44-48

  Wong, Zoie S Y ^a   Zhou, Jiaqi ^b   Zhang, Qingpeng ^b  

^a ST LUKE'S INTERNATIONAL UNIVERSITY   (Japan)

^b CITY UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

Artificial Intelligence; Emergency response; Infectious diseases modelling; Machine learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIG DATA; DATA EXTRACTION; DISEASE SURVEILLANCE; GOVERNMENT; HEALTH SERVICE; HEALTH SURVEY; HUMAN; INFECTION; INFECTION PREVENTION; INFORMATION PROCESSING; MACHINE LEARNING; MALARIA CONTROL; NOTE; COMMUNICABLE DISEASE; COMMUNICABLE DISEASE CONTROL; DISEASE ERADICATION; MALARIA;

ARTIFICIAL INTELLIGENCE; BIG DATA; COMMUNICABLE DISEASE CONTROL; COMMUNICABLE DISEASES; DISEASE ERADICATION; HUMANS; MALARIA;

EID: 85055753611     PISSN: 24680451     EISSN: 24680869     Source Type: Journal    
DOI: 10.1016/j.idh.2018.10.002     Document Type: Note

References (37)

1. Schneeweiss, S., Learning from big health care data. N Engl J Med 370:23 (2014), 2161–2163.
2. Raghupathi, W., Raghupathi, V., Big data analytics in healthcare: promise and potential. Health Inf Sci Syst, 2, 2014, 3.
3. Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., et al. Mastering the game of Go without human knowledge. Nature 550:7676 (2017), 354–359.
4. IBM, Watson for oncology. 2017 [cited 2017 15 Sept]. Available from: https://www.ibm.com/watson/health/oncology-and-genomics/oncology/.
5. Wong, Z.S., Nohr, C., Kuziemsky, C.E., Leung, E., Chen, F., Context sensitive health informatics: delivering 21st century healthcare – building a quality-and-efficiency driven system. Stud Health Technol Inf 241 (2017), 1–5.
6. CDC, Data & statistics. 2018 [cited 2018 22 Feb]. Available from: https://www.cdc.gov/datastatistics/index.html.
7. UKgovernment, Health promotion. Infectious diseases. 2018 [cited 2018 22 Feb]. Available from: https://www.gov.uk/topic/health-protection/infectious-diseases.
8. Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S., Brilliant, L., Detecting influenza epidemics using search engine query data. Nature 457:7232 (2009), 1012–1014.
9. Gianfredi, V., Bragazzi, N.L., Nucci, D., Martini, M., Rosselli, R., Minelli, L., et al. Harnessing big data for communicable tropical and sub-tropical disorders: implications from a systematic review of the literature. Front Public Health, 6, 2018, 90.
10. Air Transport Statistics, International air transport association (IATA). 2018 [cited 2018 22 Feb]. Available from: http://www.iata.org/services/statistics/air-transport-stats/Pages/index.aspx.
11. Bragazzi, N.L., Gianfredi, V., Villarini, M., Rosselli, R., Nasr, A., Hussein, A., et al. 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, 2018, 62.
12. HILDA survey, 2018 [cited 2018 22 Feb]. Available from: http://melbourneinstitute.unimelb.edu.au/hilda.
13. Mossong, J., Hens, N., Jit, M., Beutels, P., Auranen, K., Mikolajczyk, R., et al. Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Med, 5(3), 2008, e74.
14. Hoyt, R.E., Yoshihashi, A.K., Health informatics: practical guide for healthcare and information technology professionals. 6th ed., 2014, Lulu.com.
15. Tsui, K., Wong, Z., Goldsman, D., Edesess, M., Tracking infectious disease spread for global pandemic containment. Intell Syst IEEE 28:6 (2013), 60–64.
16. Han, B.A., Schmidt, J.P., Bowden, S.E., Drake, J.M., Rodent reservoirs of future zoonotic diseases. Proc Natl Acad Sci Unit States Am 112:22 (2015), 7039–7044.
17. Goodman, K.E., Lessler, J., Cosgrove, S.E., Harris, A.D., Lautenbach, E., Han, J.H., et al. A clinical decision tree to predict whether a bacteremic patient is infected with an extended-spectrum beta-lactamase-producing organism. Clin Infect Dis Off Pub Infect Dis Soc Am 63:7 (2016), 896–903.
18. Blumenthal, A., Artificial Intelligence to fight the spread of infectious diseases. Phys Org, 2018 20 Feb [cited 2018 25 Oct]. Available from: https://phys.org/news/2018-02-artificial-intelligence-infectious-diseases.html.
19. Bragazzi, N.L., Alicino, C., Trucchi, C., Paganino, C., Barberis, I., Martini, M., et al. Global reaction to the recent outbreaks of Zika virus: insights from a Big Data analysis. PloS One, 12(9), 2017, e0185263.
20. Alicino, C., Bragazzi, N.L., Faccio, V., Amicizia, D., Panatto, D., Gasparini, R., et al. Assessing Ebola-related web search behaviour: insights and implications from an analytical study of Google Trends-based query volumes. Infect Dis Poverty, 4, 2015, 54.
21. Mahroum, N., Adawi, M., Sharif, K., Waknin, R., Mahagna, H., Bisharat, B., et al. Public reaction to Chikungunya outbreaks in Italy-Insights from an extensive novel data streams-based structural equation modeling analysis. PloS One, 13(5), 2018, e0197337.
22. Heesterbeek, H., Anderson, R.M., Andreasen, V., Bansal, S., De Angelis, D., Dye, C., et al. Modeling infectious disease dynamics in the complex landscape of global health. Science (New York, NY), 347(6227), 2015 aaa4339.
23. Wong, Z.S.Y., Bui, C.M., Chughtai, A.A., Macintyre, C.R., A systematic review of early modelling studies of Ebola virus disease in West Africa. Epidemiol Infect, 2017, 1–26.
24. Keeling, M.J., Rohani, P., (eds.) Modeling infectious diseases in humans and animals, 2008, Princeton University Press, Woodstock.
25. Moyes, C.L., Temperley, W.H., Henry, A.J., Burgert, C.R., Hay, S.I., Providing open access data online to advance malaria research and control. Malar J, 12(1), 2013, 161.
26. Deroost, K., Opdenakker, G., Van den Steen, P.E., MalarImDB: an open-access literature-based malaria immunology database. Trends Parasitol 30:6 (2014), 309–316.
27. Mapping malaria risk in Africa (MARA) database, 2009 Available from: http://www.mara-database.org//login.html;jsessionid=571B54B64F54C08C6DC0306A2BF3360B.
28. Palsnodium genomics resource, 2018 Available from: http://plasmodb.org/plasmo/.
29. Okell, L.C., Drakeley, C.J., Bousema, T., Whitty, C.J., Ghani, A.C., Modelling the impact of artemisinin combination therapy and long-acting treatments on malaria transmission intensity. PLoS Med, 5, 2008.
30. Bent, O.R.S., Roberts, S., Walcott-Bryant, A., Novel exploration techniques (NETs) for malaria policy interventions. 2018.
31. Izak, D., Klim, J., Kaczanowski, S., Host-parasite interactions and ecology of the malaria parasite-a bioinformatics approach. Briefings Funct Genom, 2018, 1–7.
32. Wan, J., Zou, C., Ullah, S., Lai, C.F., Zhou, M., Wang, X., Cloud-enabled wireless body area networks for pervasive healthcare. IEEE Network 27:5 (2013), 56–61.
33. Kononenko, I., Machine learning for medical diagnosis: history, state of the art and perspective. Artif Intell Med 23:1 (2001), 89–109.
34. Foster, K.R., Koprowski, R., Skufca, J.D., Machine learning, medical diagnosis, and biomedical engineering research – commentary. Biomed Eng Online, 13, 2014, 94.
35. Wang, Y., Fan, Y., Bhatt, P., Davatzikos, C., High-dimensional pattern regression using machine learning: from medical images to continuous clinical variables. Neuroimage 50:4 (2010), 1519–1535.
36. Rolka, H., Burkom, H., Cooper, G.F., Kulldorff, M., Madigan, D., Wong, W.K., Issues in applied statistics for public health bioterrorism surveillance using multiple data streams: research needs. Stat Med 26:8 (2007), 1834–1856.
37. Tsui, K.-L., Wong, S.Y., Jiang, W., Lin, C.-J., Recent research and developments in temporal and spatiotemporal surveillance for public health. IEEE Trans Reliab 60:1 (2011), 49–58.