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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 paper we present the design and implementation of 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 implement 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. Containers comprising eight of the most common breeding sites are detected 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 are 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. To delineate conditions under which the container density counts are indicative of larval counts, a number of factors affecting correlation with larval survey data are analyzed. We conclude that creation of container density maps from geotagged images is a promising approach to providing detailed risk maps at large scale. [ABSTRACT FROM AUTHOR]

Background: Soil-transmitted helminth (STH) infections remain highly endemic across the Philippines, and are believed to be important contributors to delayed cognitive development of school-aged children. Identification of communities where children are at risk of functional illiteracy is important for the attainment of Sustainable Development Goals target for literacy. We aimed to quantify the associations between the spatial variation of STH infections and functional literacy indicators adjusting for other important contributors, and identify priority areas in the Philippines in need of interventions. Methodology/Principal findings: We used data from 11,313 school-aged children on functional literacy indicators collected in 2008. Nested fixed-effects multinomial regression models were built to determine associations between STH endemicity and geographical distribution of functional literacy, adjusting for demographics, household level variables, and the prevalence of malaria. Bayesian multinomial geostatistical models were built to geographically predict the prevalence of each level of functional literacy. The number of school-aged children belonging to each of the functional literacy indicator classes was forecast for 2017. We estimated 4.20% of functional illiteracy burden among school-aged children in Mindanao might be averted by preventing T. trichiura infections. Areas predicted with the highest prevalence of functional illiteracy were observed in localised areas of the eastern region of the Visayas, and the south-eastern portion of Mindanao. Conclusions/Significance: The study demonstrates significant geographical variation in burden of functional illiteracy in school-aged children associated with STH infections suggesting that targeted helminth control could potentially promote the development of cognitive function of school-aged children in the Philippines. The benefits of a spatially targeted strategy should be tested by future studies. [ABSTRACT FROM AUTHOR]