Visualization of Dengue Incidences using Expectation Maximization (EM) Algorithm

Nirbhay Mathur, Vijanth S. Asirvadam, Sarat C. Dass, Balvinder Singh Gill

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

The aim of this study was to use the geographical information system (GIS) to visualize the dengue incidences on a weekly basis in Selangor, Malaysia. Along with the prediction modeling on data using centroid model and distribution model based on K-means and Expectation Maximization (EM) algorithms respectively. The results show that weekly hotspot were mainly concentrated in the central part of Petaling district of Selangor. R-GIS(R software) and clustering algorithm were used for year 2014 with several weeks to develop the relation between the visualization and prediction of reported incidences. The results are validated for a small region (Petaling district of Selangor state) in Malaysia and they showed vulnerability hotspot in visualizing the dengue incidences. Thus, the proposed method is able to localize the nature of dengue incidence which can further be utilized for vector disease controlled process.
Original languageEnglish
Title of host publication2016 6th International Conference on Intelligent and Advanced Systems (ICIAS)
PublisherIEEE
ISBN (Electronic)9781509008452
DOIs
Publication statusPublished - 19 Jan 2017

Keywords

  • Dengue
  • GIS
  • clustering algorithm
  • weekly prediction
  • K-means
  • EM algorithm

Cite this

Mathur, N., Asirvadam, V. S., Dass, S. C., & Gill, B. S. (2017). Visualization of Dengue Incidences using Expectation Maximization (EM) Algorithm. In 2016 6th International Conference on Intelligent and Advanced Systems (ICIAS) [7824047] IEEE. https://doi.org/10.1109/ICIAS.2016.7824047