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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]

The aim of this study was to investigate the running pace and step characteristics among various competitive-level 400-m hurdlers through inter-group and intra-athlete comparisons. We analysed spatiotemporal data involving the split time, mean step length (SL) and mean step frequency (SF) for 13 male world-class and 14 male national-level 400-m hurdlers. We analysed 16.5 ± 3.9 races for each world-class hurdler and 19.8 ± 6.0 races for each national-level hurdler (the total number of analysed runs was 491) using publicly available television and internet broadcasts. Inter-group comparisons showed that both first- and latter-halves split times of the world-class hurdlers were significantly shorter than those of the national-level hurdlers. In the latter-half phase, no significant differences of SL and SF were observed between the world-class and national level hurdlers. Intra-athlete comparisons showed that no athletes favoured only first-half phase in terms of running speed in short finish times. In contrast, finish times of all hurdlers were sensitive to running speed in the latter-half phase. In the latter half of the race, 18 of the 27 hurdlers were identified as being SF reliant during speed enhancements; running speed of the other 9 hurdlers were also sensitive to high SF. In conclusions, important findings regarding high performance in inter-group comparisons do not always corresponded with those in intra-athlete comparisons. All athletes and coaches should first prioritize maintaining high running speeds in the latter half of 400-m hurdles rather than in the first half of the race. [ABSTRACT FROM AUTHOR]