How to efficiently predict dengue incidence in Kuala Lumpur

Duc Nghia Pham, Tarique Aziz, Ali Kohan, Syahrul Nellis, Juraina Binti Abd Jamil, Jing Jing Khoo, Dickson Lukose, Sazaly AbuBakar, Abdul Sattar, Hong Hoe Ong

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)

Abstract

Mosquito-borne diseases are rapidly spreading in all regions of the world with an estimation of 2.5 billion people globally are at risk. The recent surge in dengue outbreaks has caused severe affliction to Malaysian society. Hence, the ability to predict a dengue outbreak and mitigate its damage and loss proactively is very critical. In this paper, we study the possibility of applying machine learning (ML) and deep learning (DL) approaches to predict the number of confirmed dengue fever (DF) cases in Kuala Lumpur. We identified several contribution factors correlate to a dengue outbreak. In addition to the two frequently used factors (daily mean temperature and daily rainfall), we also took into account the enhanced vegetation index (EVI), humidity and wind speed as input factors to our prediction engines. We collected and cleansed data on these factors and the daily DF incidents in Kuala Lumpur from 2002 to 2012. We then used these data to train and evaluate our 3 ML/DL models. Among the three models, GA-RNN was the best performer and achieved a MAE of 10.95 for DF incidence prediction.

Original languageEnglish
Title of host publicationProceedings of 2018 Fourth International Conference on Advances in Computing, Communication & Automation (ICACCA)
Subtitle of host publication26 – 28 October 2018 Taylor’s University Lakeside Campus Subang Jaya, Malaysia
EditorsThinagaran Perumal
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
Edition1st
ISBN (Electronic)9781538671672
DOIs
Publication statusPublished - 2018
Externally publishedYes
EventInternational Conference on Advances in Computing, Communication and Automation 2018 - Subang Jaya, Malaysia
Duration: 26 Oct 201828 Oct 2018
Conference number: 4th
https://ieeexplore.ieee.org/xpl/conhome/8766334/proceeding (Proceedings)

Conference

ConferenceInternational Conference on Advances in Computing, Communication and Automation 2018
Abbreviated titleICACCA 2018
CountryMalaysia
CitySubang Jaya
Period26/10/1828/10/18
Internet address

Keywords

  • deep learning
  • dengue outbreak prediction
  • machine learning

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