Using Big Data Technology to Contain Current and ... - Springer Link

Using Big Data Technology to Contain Current and Future Occurrence of Ebola Viral Disease and Other Epidemic Diseases in West Africa Foluso Ayeni, Sanjay Misra(), and Nicholas Omoregbe Department of Computer and Information Sciences, Covenant University, Ota, Nigeria {Foluso.Ayeni,Sanjay.Misra,Nicholas. Omoregbe}@covenantuniversity.edu.ng

Abstract. West Africa is currently plagued with Ebola Viral Disease (EVD) and other minor epidemic diseases which has led to major economic meltdown and high mortality rate in countries like Guinea, Sierra Leone and Liberia as a result of immigration, emigration, foreign trade and investment, bilateral, poor health care issues amidst others. Harmonized EVD related data can help identify individuals who are at risk of contracting the terminal disease and at the same time controlling the outbreak which will in turn lower cost of health care across West Africa. This paper presents the significance, framework as well as an implementation plan and design for using Big Data Technologies (BDT) as an aid to prevent and control EVD in West Africa and the provision of how the principles of cloud computing could be applied to present and impending expectations of the West African Health sector. Keywords: Ebola Viral Disease · Big data · Analytics · Electronic health records · Immigration and emigration · Data bank · Harmonized · West Africa

1

Introduction

Cloud computing is a model that enables on demand, convenient, ever present network access to a shared puddle of configurable computing resources that can be provisioned rapidly and released with insignificant management effort [1]. It can be squared back to the 1950’s when enormous scale mainframes were made available to institutes and corporations [2]. According to [3] it is a platform in which services are carried out on behalf of client’s resident on technologies that the clients do not own or manage. At the heart of cloud computing is the BDT which harmonizes pool of fragmented data. BDT is amongst the eight technology-enabled business trends that has reshaped strategy across a wide range of industries [4]. It is a cloud computing techniques that provides storage as a service. It describes a new generation of technologies and architectures designed so organizations and institutions can economically extract value from very large volumes of a wide variety of data by enabling high velocity capture, discovery and analysis [5]. BDT requires a move in computer architecture so that © Springer International Publishing Switzerland 2015 Y. Tan et al. (Eds.): ICSI-CCI 2015, Part III, LNCS 9142, pp. 107–114, 2015. DOI: 10.1007/978-3-319-20469-7_13

108

F. Ayeni et al.

clients can handle both data storage requirements and server processing needed to analyse and process large volumes of data economically. It is all about the ever increasing challenge that industries and organizations face as they deal with large and very fast growing sources of information and data that presents difficult range of analysis and use problem. It has been successfully proven in organizations and businesses such as financial institutions, logistics, telecommunications, oil and transportation. Despite the great characteristics of BDT, they haven’t been utilized fairly in the health care industry, [6] most especially Africa which is commonly prone to terminal diseases such as the recent EVD upsetting West Africa. EVD previously known as Ebola Haemorrhagic Fever is a severe, often fatal illness in humans [7]. It is a virus transmitted to people from wild animals and spreads in the human populace through human-to-human transmission. It was first discovered in 1976 in the Democratic Republic of Congo near the Ebola River [8]. The first set of outbreaks was in Central Africa but the most recent outbreaks in West Africa involve major countries such as Guinea, Sierra Leone, Liberia and Nigeria [9]. It is recorded that about fifty out of every 100 EVD patients die which is shockingly high. Although local and international agencies are not relenting in the fight against the EVD, technologies such as Big Data can be used as an aid to contain and control outbreaks of this deadly disease. According to [9] effective and efficient outbreak control depends on application of interventions namely case management, surveillance and contact tracing, good laboratory service, safe burials and social mobilization. This work is proposed to prove that EVD outbreak in West Africa can be contained and controlled using BDT. The study would adopt a combination of qualitative and quantitative research method.

2

Statement of Problem and Objective of Study

Technological advancements made in the world especially for the last ten to fifteen years have altered the entire scenario in every field including education, entertainment, health, workplace, environment and personal life [2]. There is also Zero Level of significance of BDT in EVD struck West African Countries. This is the major reason why BDT need to be implemented in order to aid EVD health care workers in their fight against the deadly disease. BDT is simply the collection of data sets so large or complex that it becomes difficult to process using the traditional data processing applications. The main objective of this study is to explore the usefulness of BDT in fighting and containing EVD and also presents a technology based framework that allows stakeholders to harmonize all EVD related data towards containing the virus and the provision of how the principles of cloud computing could be applied to current and future expectations of the West African Health sector.

Using Big Data Technology to Contain Current and Future Occurrence

3

Review of Related Findings/Technologies

3.1

Existing Technologies

109

Wiser Together is leading the change in the field of BDT’s in health care. It is a BDT based hospital resident on the web committed to motivate people to improve the quality of health care while reducing costs for themselves. Millions of people today are connected to this and also reaping the benefits of their products and services. In September 2014, Wiser Health is the only decision-making technology that compares treatment options using evidence-based clinical outcomes, individual consumer preferences, insurance coverage, cost and popularity of the treatment among physicians and patients in the local community. 3.2

Related Findings

The development of cloud computing has brought about information technology infrastructure developments for enterprises especially in the aspects of health care delivery systems of developed nations therefore causing medicals records to shift from manual processing to electronic. [2] tried to devise a means on how to overcome barriers of effective health care delivery in Nigeria using Socalized medicine while [10] also devised a means on how to maintain web service process models for effective mobile health care delivery in Nigeria. This connotes that internet based health care systems are existing in West Africa but not effective and efficient. The major concerns of mobile health care service providers is security and privacy [11] which makes penetration of Big Data difficult for acceptance particularly in Africa. According to Odusote & Omoregbe, three models to curb security threats in health cloud were proposed though not implemented. [12] projected three schemes to enhance big data security because enterprises and health care providers need to ensure they have mechanisms in place which allows them to meet government compliancy regulations for data protection. First, Secure encryption technology in health information must be used to protect confidential data. Secondly, careful management of access to the cryptography keys which unlock the encrypted data must be put in place. Finally, Big Data Analytics can also secure Big Data by collecting all available digital evidence including raw packets, flow data and files, organizations can uncover advanced targeted attacks. At the heart of all these transformations is the rising cost of Electronic Health Care Services which is a global challenge but [13] concluded that the future of these services is to seek for expansion and interoperability. BDT is a very important factor in shaping the future of health care and at the same time preventing the spread of EVD in West Africa. BDTs in health care have not been fully implemented in developed countries owed to the challenges of access to necessary information. The power to access and analyse enormous data sets can improve ability to anticipate and treat illnesses [14]. According to Burg, aggregating massive amount of health data might sound easy but the challenge is maintaining patients integrity. EVD outbreak control in West Africa falls

110

F. Ayeni et al.

short of international disease control standards resulting from poor state of health care infrastructure, medical professionals and Information technology penetration. According to the [15], the driving push for big data is to create more value in health care. Advanced analytic approaches that use machine learning, predictive modelling, pattern detection, anomaly detection and other sophisticated techniques can successfully address the weaknesses of rule-based systems. West Africa’s major priority is to devise a means of making adequate use of Information Technology at our disposal to contain EVD that has savaged the entire region. Our interest is to develop a framework using an open source BDT tool to prove how BDT can be applied to the intervention and control schemes outlined by the WHO. 3.3

Economic Impact of EVD in West Africa

The Ebola epidemic continues to cripple the economies of Liberia, Guinea and Sierra Leone [16]. According to World Bank’s report which was released in December 2014, Sierra Leone’s Gross Domestic Product (GDP) growth was 11.3% before EVD outbreak in June 2014 and by December 2014 it had reduced to 4% which is about 60% decrease and if this persists, world bank projections shows that by October 2015, Sierra Leone’s GDP growth will be 1%. Guinea’s GDP growth was 4.5% before EVD outbreak in June 2014 and by December 2014 it had reduced to 0.5% and if this persists, world bank projections shows that by December 2015, Guinea’s GDP growth will be -0.2%. Liberia’s GDP growth was 5.9% before EVD outbreak in June 2014 and by December 2014 it had reduced to 2.2% and if this persists, world bank projections shows that by October 2015, Guinea’s GDP growth will be 1.0%. 3.4

Motivation of the Work Based on the Existing Methodologies

The motivation for this work is to fill the gap in the existing technologies by focusing more on West Africa which is prone to terminal diseases and also using information technology tools available at our disposal to reduce mortality rate in West Africa.

4

Proposed Framework

There are over 50 existing open source tools for Big Data and the most interesting features of BDTs are that they have noSQL databases, presence of business intelligence tools , development tools and much more [17] .The very best known amongst these is the Hadoop MapReduce (HMR) which is spawning an entire industry of related services and products . It is a framework for managing big data by storing data on a large scale, organizing data so it can be accessible via a variety of different tools, it is also a set of tools that allows users to gain insights from the data. The HMR model is a BDT that has proven to be efficient over time in the business world such as Facebook, Twitter, ebay and google could also be applied to solve healthcare challenges most especially the EVD[18]. HMR model can be used to execute EVD case

Using Big Data Technology to Contain Current and Future Occurrence

111

management, surveillance and contract tracing and social mobilization. HMR is a software framework for easily writing applications which process vast amounts of data in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner [19]. Fig 4(b) shows a typical collaborative virtual BDT based EVD reference framework/architecture for implementing an internet technology based integration solution between locations in West Africa.

(a)

(b)

Fig. 1. (a) Context Diagram (b) Conceptual Framework

4.1

Context Diagram

Fig 1(a) shows a context diagram/architecture for implementing a BDT based Health care system. The context diagram is centered round the use of notification and monitoring systems which enables the provision of a number of services most especially mobile communication services and user adaptation services. 4.2

Conceptual Framework

The aggregated HMR BDT framework shows the communication among stakeholders and also the information technology infrastructures involved. • The database and the ECOWAS regulatory bodies ensure proper accreditation and authentication of registered victims and also ensure efficiency, proper record keeping and security of the database including better knowledge of possible outcomes. • The various remote locations indicated as discussed in the introduction and abstract shows that EVD based geographical areas can communicate with

112

F. Ayeni et al.

•

•

5

each other real-tim me regardless of their geographical locations, for examp mple, an EVD base stattion in Liberia, West Africa can connect to another E EVD base station in Guiinea, West Africa. International regullatory bodies such as the WHO can reach any station off his or her choice acro oss the internet using any internet enabled device thaat is connected to the central server which will be domicile in the cloud as shoown in Figure 3. i West Africa can communicate with each other via E EpiVarious Airports in demic clearance baarcode scanning.

Validation of the Model

The development of a systeem implementing new computer technologies that suppport big data in health care is a growing necessity just as continuity of care requires a cooperative environment. Th herefore, providing a secure and easy to implement ennvironment for medical appliccations will pose significant changes in current compuuter systems and networks. The study will adopted internet programming tools such as HTML, CSS, PH HP, JAVA Script and Dreamweeaver. These languages have been selected because of thheir open source nature, platform m independability and the wide acceptability they enjoy.

Fig. 2. Patients profile page which w displays patient account information and also doctor’s hoome page

Our designed system waas implemented, evaluated and interpreted as shown in F Figure 2. The sign up page prrovides a step-by-step account set-up process for patieents, healthcare workers or regullatory bodies. The homepage is displayed upon successful account login (patient and physician). The patient homepage displays a list of ssuggested physicians that may y be known to the logged in physician, enabling easy cconnection with other physicians. It also displays most recent unread messages from bboth patients and physicians. Th he patient homepage also displays a list of suggested phyysicians that might be close to o the patients’ location and shows most recent unread m messages from physicians. Con nnection displays physicians and patients that are alreeady connected. Also shows a liist of suggested colleagues for physicians as well as ssug-

Using Big Data Technology to Contain Current and Future Occurrence

113

gested physicians for patients. Messages show interactions between physicians and patients. Provides displays physician and patient account information such as patient contact, medical information, and medical history. The comparative analysis in the Table 1 further proves that our proposed system is flexible; requires less time to use; provides reliable and robust performance; quick response and follow-up. Table 1. A comparison of the proposed Big Data application with existing application Functionalities

Wiser Together

Proposed Application (Medibook)

Cost (payment plans)

Expensive

Relatively Cheap

Covered Health Areas

6

20

Ease of Locating Physi- No cian GPS technology required

Yes

Yes

No

Strictly designed for No reporting emergencies in real-time

Yes

Social Networking No Principles embedded

Yes

Yes

Yes

User Acceptance

6

Conclusion and Future Work

This study has strengthened and explored the need to better improve communication and information flow among stakeholders in the West African health care industry and the framework gives a pointer that BDT will definitely help contain current and future occurrence of EVD and other epidemic diseases in West Africa. In any software/tool development, Maintainability will always be a process and performing this task usually comes with heavy cost [20]. Future research works tends to show a detailed maintainability model. The implementation of the system will also incorporate a more sophisticated security measure to prevent third party or cyber criminals from hacking into the system. An open source Apache HMR application for Big Data Databases which have no SQL will be integrated to the system. To address the security issue, encrypted authentications will be put in place to grant only role-based access to records. BDT in West Africa will also be evaluated by comparing it to the existing systems in the USA and other developed countries.

114

F. Ayeni et al.

References 1. Mell, P., Grance, T.: The NIST definition of cloud computing. National Institute of Standards and Technology 53(6), 1–7 (2009) 2. Ayeni, F., Misra, S.: Overcoming barriers of effective health care delivery and electronic health records in nigeria using socialized medicine. In: 2014 IEEE International Conference on Electronics Computer and Computation (ICECCO), pp. 1–4 (2014) 3. Pearson, S., Shen, Y., Mowbray, M.: A privacy manager for cloud computing. In: Jaatun, M.G., Zhao, G., Rong, C. (eds.) Cloud Computing. LNCS, vol. 5931, pp. 90–106. Springer, Heidelberg (2009) 4. Bughin, J., Chui, M., Manyika, J.: Clouds, Big Data and Smart Assets: Ten-tech Enabled Business Trends to Watch (2010). http://www.mckinseyquarterly.com (retrieved) 5. Villars, R., Olofson, C., Eastwood, M.: Big Data: what it is and Why you should Care. IDC (2011). http://www.idc.com (retrieved) 6. Groves, P., Kayyali, B., Knott, D., Van Kuiken, S.: The Big Data Revolution in Health Care: Accelerating Value and Innovation (2013). http://www.mckinseyquarterly.com (retrieved) 7. World Health Organization: EVD Implications of Introduction in the Americas (2014) 8. Minnesota Deapartment of Health. http://health.state.mn.us 9. World Health Organization Regional Office for Africa: Contract Tracing During an Outbreak of Ebola Disease (2014) 10. Olayinka, O., Misra, S., Olaniyi, M., Ahmed. A.: Towards an iterative maintainability web service model for effective mobile healthcare delivery. In: 2013 IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON), pp. 177–181 (2013) 11. Odusote, B., Omoregbe, N.: Towards A Well-Secured Electronic Health Record in the Health Cloud. Journal of Computing 5(1), 13–17 (2013) 12. Advancetech: Enhancing Big Data Security (2013). http://www.advantech.com/nc (retrieved) 13. Benson, M.B., Cole, A.: Hospital information systems in Nigeria: a review of literature. The Journal of Global Health Care Systems 1(3), 3–26 (2011) 14. Burg, N.: How Big Data will help save Health Care (2014). http://www.forbes.com (retrieved) 15. Harvard Business Review: How Big Data Impacts Health Care (2014). http://www.hbr.org (retrieved) 16. The World Bank Group: Update on the Economic Impact of the 2014 Ebola Epidemic on Liberia, Sierra Leone and Guinea (2014) 17. Harvey, C: 50 open source tools for big data (2012). http://www.datamation.com/datacenter 18. Schouten, P.: Big Data in Health Care Solving Provider Revenue Leakage with Advanced Analytics. Health care financial management association (2013). http://www.hfma.org (retrieved) 19. Chen, H., Chiang, R., Storey, V.: Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly 36(4), 1165–1188 (2012) 20. Omogbadegun, O., Adegboyega, A.: Framework for overcoming barriers of complementary and alternative medicine acceptance into conventional healthcare system. International Journal of Medicinal Plants and Alternative Medicine 1(7), 118–136 (2013)