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http://dx.doi.org/10.9717/kmms.2019.22.5.588

Malaria Epidemic Prediction Model by Using Twitter Data and Precipitation Volume in Nigeria  

Nduwayezu, Maurice (Dept. of Information and Communication Systems, Inje University)
Satyabrata, Aicha (Institute of Digital Anti-Aging Healthcare (IDA), Inje University)
Han, Suk Young (Institute of Digital Anti-Aging Healthcare (IDA), Inje University)
Kim, Jung Eon (Dept. of Emergency Medicine, Inje University, Ilsan Paik Hospital)
Kim, Hoon (Dept. of Emergency Medicine, Inje University, Ilsan Paik Hospital)
Park, Junseok (Dept. of Emergency Medecine, Inje University, Ilsan Paik Hospital)
Hwang, Won-Joo (Dept. of Electronics, Telecommunications, Mechanical & Automotive Engineering, Inje University)
Publication Information
Journal of Korea Multimedia Society / v.22, no.5, 2019 , pp. 588-600 More about this Journal
Abstract
Each year Malaria affects over 200 million people worldwide. Particularly, African continent is highly hit by this disease. According to many researches, this continent is ideal for Anopheles mosquitoes which transmit Malaria parasites to thrive. Rainfall volume is one of the major factor favoring the development of these Anopheles in the tropical Sub-Sahara Africa (SSA). However, the surveillance, monitoring and reporting of this epidemic is still poor and bureaucratic only. In our paper, we proposed a method to fast monitor and report Malaria instances by using Social Network Systems (SNS) and precipitation volume in Nigeria. We used Twitter search Application Programming Interface (API) to live-stream Twitter messages mentioning Malaria, preprocessed those Tweets and classified them into Malaria cases in Nigeria by using Support Vector Machine (SVM) classification algorithm and compared those Malaria cases with average precipitation volume. The comparison yielded a correlation of 0.75 between Malaria cases recorded by using Twitter and average precipitations in Nigeria. To ensure the certainty of our classification algorithm, we used an oversampling technique and eliminated the imbalance in our training Tweets.
Keywords
Classification Algorithms; Malaria and Precipitation; Pearson Correlation Coefficient; and Twitter Data;
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Times Cited By KSCI : 1  (Citation Analysis)
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