Adaptive Area-Based Risk Model for Dengue Fever: Algorithm of Dynamic Spreading in Network

Wahjoe T. Sesulihatien, Yasushi Kiyoki

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

Abstract

Dengue fever is the fastest spreading communicable disease in the world. Spreading of virus is driven by increasing number of human moving. In many dengue-endemic countries, problem in dengue spreading is predicting infected area and determine perfect strategy to prevent the disease. Predicting infected area spot relates with pattern of human moving, while strategy to prevent is depend on vulnerability of area. In this paper we proposed an adaptive spreading model of area-disease based on human movement. This method combines an area-based mathematical model with discrete life-cycle of virus. The proposed method includes (1) state-space model of routine movement cycle, (2) algorithm of spreading, (3) prediction of the next infection area by graph relation, and (4) vulnerability value of suspected area. There are two important features in this method: real-time prediction of infected area and flexibility to adapt in the different situation. To perform the simulation we utilize real data of infected people in Surabaya in January 2011.The result shows that this method is suitable for near future prediction and easy to compensate time-varying changing. However, the accuracy needs to be improved.

Original languageEnglish
Title of host publicationInformation Modelling and Knowledge Bases XXVII
PublisherIOS Press
Pages1-13
Number of pages13
Volume280
ISBN (Print)9781614996101
DOIs
Publication statusPublished - 2016

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume280
ISSN (Print)09226389

Keywords

  • local-global spreading
  • Real time
  • routine human moving
  • state space

ASJC Scopus subject areas

  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Adaptive Area-Based Risk Model for Dengue Fever: Algorithm of Dynamic Spreading in Network'. Together they form a unique fingerprint.

Cite this