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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: Lead, mercury, cadmium and arsenic are the priority heavy metals of major public health concern in industrialized countries. Exposure to them can cause cognitive impairment and depressive disorders through an effect on Brain-derived neurotrophic factor (BDNF) which is an important biomarker of pregnancy. Despite a number of prior studies on heavy metals pollution, there is few of studies on the effect of heavy metals on BDNF during early pregnancy. This study aims to examine the association between maternal blood heavy metals concentrations and BDNF during the first trimester pregnancy among Myanmar migrants in Thailand. Methodology: This cross sectional study, a part of ongoing birth cohort was conducted at the antenatal care clinic from June to October 2018. A total of 108 with Myanmar migrant pregnancy with a single viable fetus of 0 to 14 gestation weeks who stayed within the industrial plant at least 3 months before were recruited. Socio-demographic characteristics and health behaviors were accessed using a self-report questionnaire. Maternal blood heavy metals (lead (Pb), mercury (Hg), cadmium (Cd) and arsenic (As)) were measured using an inductively coupled plasma mass spectrometer and plasma BDNF was measured using an enzyme-linked immunosorbent assay. Multivariate binary logistic regression were modeled to access the association. Results: Median (interquartile rank: IQR) concentrations were: BDNF (6.49 (1.79) μg/ml), Pb (2.77 (1.46) μg/dL), Hg (0.62 (0.54) μg/dL), Cd (0.93(0.86) μg/L) and As (0.40 (0.11) μg/dL) respectively. We categorized BDNF concentrations into high (> median) (n = 54) and low (≤ median) (n = 54) groups. After adjusting for potential confounders, high blood total arsenic concentration had 2.6-fold increased odds (aOR = 2.603, 95% CI: 1.178, 5.751) of low plasma BDNF level as compared with low blood total arsenic group. However, there was no significant association between BDNF and Pb, Hg and Cd. Conclusions: The present findings demonstrate higher blood total arsenic level were more likely to have lower BDNF in early pregnancy. Our study suggested that heavy metal could be worsen BDNF level which plays its important role on biological effect of maternal depressive disorder and newborn neurodevelopment. [ABSTRACT FROM AUTHOR]