Large-Scale Detailed Mapping of Dengue Vector Breeding Sites from Geo-Tagged Images

Targeted environmental and ecosystem management remain crucial in control of dengue. However, providing detailed environmental information on a large scale to effectively target dengue control efforts remains a challenge. An important piece of such information is the extent of the presence of potential dengue vector breeding sites, which consist primarily of open containers such as ceramic jars, buckets, old tires, and flowerpots. In this project we have implemented a pipeline to detect outdoor open containers which constitute potential dengue vector breeding sites from geotagged images and to create highly detailed container density maps at unprecedented scale. We have implemented the approach using Google Street View images which have the advantage of broad coverage and of often being two to three years old which allows correlation analyses of container counts against historical data from manual surveys. Our initial implementaiton detects containers comprising eight of the most common breeding sites in the images using convolutional neural network transfer learning. Over a test set of images the object recognition algorithm has an accuracy of 0.91 in terms of F-score. Container density counts are generated and displayed on a decision support dashboard. Analyses of the approach have been carried out over three provinces in Thailand. The container counts obtained agree well with container counts from available manual surveys. Multi-variate linear regression relating densities of the eight container types to larval survey data shows good prediction of larval index values with an R-squared of 0.674.

Work is continuing in a number of directions, including 1) combining the container counts with meteorological information to predict larval counts, 2) evaluating the added value of container founs in estimating the risk of dengue, and 3) extending the approach to handle other geo-tagged images such as from mobile platforms and drones.

PI: Prof. Dr. Peter Haddawy, Faculty of ICT, Mahidol Unviversity

Project Members

● Dr. Myat Su Yin, Faculty of ICT, Mahidol University
● Dr. Mores Prachyabrued, Faculty of ICT, Mahidol University
● Asst. Prof. Dr. Worapan Kusakunniran, Faculty of ICT, Mahidol University
● Asst. Prof. Dr. Saranath Lawpoolsri, Faculty of Tropical Medicine, Mahidol University
● Asst. Prof. Dr. Anuwat Wiratsudakul, Faculty of Veterinary Science, Mahidol University
● Dr. Yongjua Laosiritaworn, Ministry of Public Health of Thailand
● Dr. Dominique Bicout, VetArgro Sup, Lyon, France
● Prof. Dr. Johannes Schöning, Faculty of Informatics, University of Bremen

Duration: June 2017 – ongoing