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

Nduwayezu Maurice; Satyabrata Aicha; Han Suk Young; Kim Jung Eon; Kim Hoon; Park Junseok; Hwang Won-Joo

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저자정보: Nduwayezu Maurice (Inje University) Satyabrata Aicha (Inje University) Han Suk Young (Inje University) Kim Jung Eon (Inje University Ilsan Paik Hospital) Kim Hoon (Inje University Ilsan Paik Hospital) Park Junseok (Inje University Ilsan Paik Hospital) Hwang Won-Joo (Inje University)

저널정보: 한국멀티미디어학회 멀티미디어학회논문지 멀티미디어학회논문지 제22권 제5호

발행연도: 2019.5

수록면: 588 - 600 (13page)

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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.

#Classification Algorithms #Malaria and Precipitation #Pearson Correlation Coefficient #Twitter Data

참고문헌 (23)

참고문헌 신청

World Health Organization / 2018 / World Malaria Report

C. W. Gitonga / 2010 / Implementing School Malaria Surveys in Kenya : Towards a National Surveillance System / Malaria Journal 9(1):306

주문일 / 2016 / 생체신호 습득과 건강 모니터링을 위한스마트 헬스케어 의복 개발 / 멀티미디어학회논문지 19(5):808~817

J. Boit / 2018 / Malaria Surveillance System using Social Media / Proceedings of the 13th Midwest Association for Information Systems (MWAIS) Conference

R. Xuriguera / 2012 / Using Twitter as a Source of Information for Time Series Prediction

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