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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: Disclosure of HIV status is a critical gateway to HIV prevention. Despite many studies on this topic, there is a gap in knowledge regarding HIV status disclosure and risky sexual behavior in HIV-infected female migrant workers. The current study addressed this research gap, and focused on HIV-infected Shan female migrant workers in Northern Thailand. Methods: This study conducted in-depth interviews with 18 HIV-infected Shan female migrants (aged between 23–54 years old) and 29 healthcare workers in district hospitals in Chiang Mai. Content analysis was employed to identify particular themes related to HIV status disclosure, sexual risk behavior, and ART adherence. Results: We found that non-disclosure to husbands/partners was mostly related to fear of marital conflict and of losing social and financial support. Non-disclosure prevented Shan female migrant workers from negotiating condom use with their partners. Reasons for not disclosing to friends, family and other community members were mostly related to feared rejection and discrimination due to HIV-related stigma. Accounts of condomless sex in the context of HIV status disclosure suggest that gender norms and male dominance over women influenced decision-making for safe sex. Lastly, some female migrant workers perceived low risk of HIV transmission with good adherence to the ART. Conclusions: This study highlighted the complex challenges of HIV status disclosure among HIV-positive Shan female migrant workers and the link between disclosure/non-disclosure and condom use. There is a pressing need to create realistic disclosure mechanisms that take into account the socio-cultural barriers to disclosure including marital conflicts, stigma, and gender norms. Messages to encourage condom use should be delivered carefully so that knowledge of the HIV transmission reduction qualities of good ART adherence does not serve as a barrier to condom use. [ABSTRACT FROM AUTHOR]