Hostname: page-component-7dd5485656-g8tfn Total loading time: 0 Render date: 2025-10-31T03:45:54.179Z Has data issue: false hasContentIssue false
  • English
  • Français

SECURE INTERNET OF THINGS-BASED CLOUD FRAMEWORK TO CONTROL ZIKA VIRUS OUTBREAK

Part of: Special Collection - Methods

Published online by Cambridge University Press:  24 April 2017

Sanjay Sareen ,
Sandeep K. Sood  and
Sunil Kumar Gupta
Sanjay Sareen
Affiliation:
Computer Section, Guru Nanak Dev University I. K. Gujral Punjab Technical University sareen.gndu@gmail.com
Sandeep K. Sood
Affiliation:
Department of Computer Science and Engineering, Guru Nanak Dev University
Sunil Kumar Gupta
Affiliation:
Department of Computer Science and Engineering, Beant College of Engineering and Technology
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: Zika virus (ZikaV) is currently one of the most important emerging viruses in the world which has caused outbreaks and epidemics and has also been associated with severe clinical manifestations and congenital malformations. Traditional approaches to combat the ZikaV outbreak are not effective for detection and control. The aim of this study is to propose a cloud-based system to prevent and control the spread of Zika virus disease using integration of mobile phones and Internet of Things (IoT).

Methods: A Naive Bayesian Network (NBN) is used to diagnose the possibly infected users, and Google Maps Web service is used to provide the geographic positioning system (GPS)-based risk assessment to prevent the outbreak. It is used to represent each ZikaV infected user, mosquito-dense sites, and breeding sites on the Google map that helps the government healthcare authorities to control such risk-prone areas effectively and efficiently.

Results: The performance and accuracy of the proposed system are evaluated using dataset for 2 million users. Our system provides high accuracy for initial diagnosis of different users according to their symptoms and appropriate GPS-based risk assessment.

Conclusions: The cloud-based proposed system contributed to the accurate NBN-based classification of infected users and accurate identification of risk-prone areas using Google Maps.

Keywords

Zika virusMosquitoIoTCloud computingNaive Bayesian NetworkGoogle map

Information

Type
Methods
Information
International Journal of Technology Assessment in Health Care , Volume 33 , Issue 1 , 2017 , pp. 11 - 18
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

REFERENCES

1. Dick, GWA, Kitchen, SF, Haddow, AJ. Zika virus (I). Isolations and serological specificity. Trans R Soc Trop Med Hyg. 1952;46:509–520.Google Scholar
2. World Health Organization, Zika Situation Report. 2016. https://www.who.int/emergencies/zika-virus/situation-report/14-april-2016/en/ (accessed April 19, 2016).Google Scholar
3. Sarmiento-Ospina, A, Vsquez-Serna, H, Jimenez-Canizales, CE, Villamil-Gmez, WE, Rodriguez-Morales, AJ. Zika virus associated deaths in Colombia. Lancet Infect Dis. 2016;16:523–524.CrossRefGoogle ScholarPubMed
4. World Health Organization, Zika outbreak: WHO's global emergency response plan. 2016. https://www.who.int/emergencies/zika-virus/response/en/ (accessed April 20, 2016).Google Scholar
5. Musso, D, Roche, C, Robin, E, Nhan, T, Teissier, A, Cao-Lormeau, VM. Potential sexual transmission of Zika virus. Emerg Infect Dis. 2015;21:359–361.Google Scholar
6. Marano, G, Pupella, S, Vaglio, S, Liumbruno, G, Grazzini, G. Zika virus and the never-ending story of emerging pathogens and transfusion medicine. Blood Transfus. 2015;14:95–100.Google Scholar
7. Oliveira Melo, AS, Malinger, G, Ximenes, R, Szejnfeld, PO, Alves Sampaio, S, Bispo de Filippis, AM. Zika virus intrauterine infection causes fetal brain abnormality and microcephaly: Tip of the iceberg? Ultrasound Obstet Gynecol. 2016;47:6–7.Google Scholar
8. Lounis, A, Hadjidj, A, Bouabdallah, A, Challal, Y. Healing on the cloud: Secure cloud architecture for medical wireless sensor networks. Future Gener Comput Syst. 2015;55:266–277.Google Scholar
9. He, C, Fan, X, Li, Y. Toward ubiquitous healthcare services with a novel efficient cloud platform. IEEE Trans Biomed Eng. 2013;60:230–234.Google Scholar
10. Sareen, S, Sood, SK, Gupta, SK. A cloud-based seizure alert system for epileptic patients that uses higher-order statistics. IEEE Comp Sci Eng. 2016;18:56–67.Google Scholar
11. Paixao, ES, Barreto, F, Teixeira, GM, Costa, CM, Rodrigues, L. History, epidemiology, and clinical manifestations of Zika: A systematic review. Am J Public Health. 2016;106:606–612.Google Scholar
12. Nishiura, H, Mizumoto, K, Rock, KS, Yasuda, Y, Kinoshita, R, Miyamatsu, Y. A theoretical estimate of the risk of microcephaly during pregnancy with Zika virus infection. Epidemics. 2016;15:66–70.Google Scholar
13. Petersen, E, Wilson, ME, Touch, S, et al. Rapid spread of Zika virus in the Americas - Implications for public health preparedness for mass gatherings at the 2016 Brazil Olympic Games. Int J Infect Dis. 2016;44:11–15.Google Scholar
14. Lopez-Barbosa, N, Gamarra, JD, Osma, JF. The future point-of-care detection of disease and its data capture and handling. Anal Bioanal Chem. 2016;408:2827–2837.Google Scholar
15. Quwaider, M, Jararweh, Y. A cloud supported model for efficient community health awareness. Pervasive Mob Comput. 2016;28:35–50.Google Scholar
16. Mamun, KAA, Alhussein, M, Sailunaz, K, Islam, MS. Cloud based framework for Parkinsons disease diagnosis and monitoring system for remote healthcare applications. Future Gener Comput Syst. 2017;66:36–47.CrossRefGoogle Scholar
17. Zhang, Z, Wang, H, Wang, C, Fang, H. Cluster-based epidemic control through smartphone-based body area networks. IEEE Trans Parallel Distrib Syst. 2015;26:681–690.CrossRefGoogle ScholarPubMed
18. Sareen, S, Sood, SK, Gupta, SK. Towards the design of a secure data outsourcing using fragmentation and secret sharing scheme. Information Security Journal: A Global Perspective. 2016;25:39–53.Google Scholar
19. John, GH, Langley, P. Estimating continuous distributions in Bayesian classifiers. Amsterdam: Morgan Kaufmann Publishers Inc; 1995.Google Scholar
20. Google Maps. 2016. https://www.google.co.in/maps (accessed May 7, 2016).Google Scholar
21. AdultDataset. 2016. https://archive.ics.uci.edu/ml/datasets/Adult (accessed May 5, 2016).Google Scholar
22. Hall, M, Frank, E, Holmes, G, Pfahringer, B, Reutemann, P, Witten, IH. The WEKA data mining software: An update. ACM SIGKDD Explorations Newsletter. 2009;11:10–18.Google Scholar
23. Baldi, P, Brunak, S, Chauvin, Y, Andersen, CA, Nielsen, H. Assessing the accuracy of prediction algorithms for classification: An overview. Bioinformatics. 2000;16:412–424.Google Scholar
24. Amazon EC2 Instance Comparison. 2016. https://www.ec2instances.info/ (accessed October 3, 2016).Google Scholar
25. Silva, DF, Souza, VMA, Ellis, DPW, Keogh, EJ, Batista, GEAPA. Exploring low cost laser sensors to identify flying insect species. J Intell Robot Syst. 2015;80:313–330.Google Scholar