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Open AccessArticle

Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System

School of Electrical Engineering and Computer Science, University of Bradford, Bradford BD7 1DP, UK
Manchester Metropolitan University Business School, Manchester Metropolitan University, Manchester M15 6BH, UK
Centre for Electronic Warfare, Information and Cyber, Cranfield University, Shrivenham SN6 8LA, UK
Faculty of Computer Science, University of Sunderland, St Peters Campus, Sunderland SR6 0DD, UK
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in DIGITALISATION FOR A SUSTAINABLE SOCIETY Embodied, Embedded, Networked, Gothenburg, Sweden, 12–16 June 2017.
Appl. Sci. 2017, 7(8), 836;
Received: 14 July 2017 / Revised: 8 August 2017 / Accepted: 9 August 2017 / Published: 17 August 2017
(This article belongs to the Special Issue Smart Healthcare)
Malaria, as one of the most serious infectious diseases causing public health problems in the world, affects about two-thirds of the world population, with estimated resultant deaths close to a million annually. The effects of this disease are much more profound in third world countries, which have very limited medical resources. When an intense outbreak occurs, most of these countries cannot cope with the high number of patients due to the lack of medicine, equipment and hospital facilities. The prevention or reduction of the risk factor of this disease is very challenging, especially in third world countries, due to poverty and economic insatiability. Technology can offer alternative solutions by providing early detection mechanisms that help to control the spread of the disease and allow the management of treatment facilities in advance to ensure a more timely health service, which can save thousands of lives. In this study, we have deployed an intelligent malaria outbreak early warning system, which is a mobile application that predicts malaria outbreak based on climatic factors using machine learning algorithms. The system will help hospitals, healthcare providers, and health organizations take precautions in time and utilize their resources in case of emergency. To our best knowledge, the system developed in this paper is the first publicly available application. Since confounding effects of climatic factors have a greater influence on the incidence of malaria, we have also conducted extensive research on exploring a new ecosystem model for the assessment of hidden ecological factors and identified three confounding factors that significantly influence the malaria incidence. Additionally, we deploy a smart healthcare application; this paper also makes a significant contribution by identifying hidden ecological factors of malaria. View Full-Text
Keywords: malaria; climatic factors; machine learning; prediction; mobile application; structural equation modelling; partial least squares model malaria; climatic factors; machine learning; prediction; mobile application; structural equation modelling; partial least squares model
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Modu, B.; Polovina, N.; Lan, Y.; Konur, S.; Asyhari, A.T.; Peng, Y. Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System. Appl. Sci. 2017, 7, 836.

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