Mapping urban and peri-urban breeding habitats of Aedes mosquitoes using a fuzzy analytical hierarchical process based on climatic and physical parameters

Muhammad Shahzad Sarfraz, Nagesh K. Tripathi, Fazlay S. Faruque, Usama Ijaz Bajwa, Asanobu Kitamoto, Marc Souris
  • Muhammad Shahzad Sarfraz
    Department of Computer Science, National University of Computer and Emerging Sciences, Chiniot-Faisalabad Campus, Pakistan | shahzad.sarfraz@nu.edu.pk
  • Nagesh K. Tripathi
    Remote Sensing and GIS Field of Study, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani, Thailand
  • Fazlay S. Faruque
    GIS and Remote Sensing Program, University of Mississippi Medical Center, Jackson, United States
  • Usama Ijaz Bajwa
    Department of Computer Science, COMSATS Institute of Information Technology, Abbottabad, Pakistan
  • Asanobu Kitamoto
    Digital Content and Media Sciences Research Division, National Institute of Informatics (NII), Tokyo, Japan
  • Marc Souris
    Remote Sensing and GIS Field of Study, School of Engineering and Technology, Asian Institute of Technology, Pathum Thani, Thailand; Institut de Recherche pour le Dévelopement (IRD), Marseille, France

Abstract

The spread of dengue fever depends mainly on the availability of favourable breeding sites for its mosquito vectors around human dwellings. To investigate if the various factors influencing breeding habitats can be mapped from space, dengue indices, such as the container index, the house index and the Breteau index, were calculated from Ministry of Public health data collected three times annually in Phitsanulok, Thailand between 2009 and 2011. The most influential factors were found to be temperature, humidity, rainfall, population density, elevation and land cover. Models were worked out using parameters mostly derived from freely available satellite images and fuzzy logic software with parameter synchronisation and a predication algorithm based on data mining and the Decision Tree method. The models developed were found to be sufficiently flexible to accommodate additional parameters and sampling data that might improve prediction of favourable breeding hotspots. The algorithm applied can not only be used for the prediction of near real-time scenarios with respect to dengue, but can also be applied for monitoring other diseases influenced by environmental and climatic factors. The multi-criteria model presented is a cost-effective way of identifying outbreak hotspots and early warning systems lend themselves for development based on this strategy. The proposed approach demonstrates the successful utilisation of remotely sensed images to map mosquito breeding habitats.

Keywords

dengue fever, fuzzy analytic hierarchy process, larval density, data mining, climatic factors, health, geographical information system, Thailand

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Submitted: 2014-12-30 15:09:34
Published: 2014-12-01 00:00:00
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Copyright (c) 2014 Muhammad Shahzad Sarfraz, Nagesh K. Tripathi, Fazlay S. Faruque, Usama Ijaz Bajwa, Asanobu Kitamoto, Marc Souris

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