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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: This paper presents a new agent-based model (ABM) for investigating T. b. rhodesiense human African trypanosomiasis (rHAT) disease dynamics, produced to aid a greater understanding of disease transmission, and essential for development of appropriate mitigation strategies. Methods: The ABM was developed to model rHAT incidence at a fine spatial scale along a 75 km transect in the Luangwa Valley, Zambia. The method offers a complementary approach to traditional compartmentalised modelling techniques, permitting incorporation of fine scale demographic data such as ethnicity, age and gender into the simulation. Results: Through identification of possible spatial, demographic and behavioural characteristics which may have differing implications for rHAT risk in the region, the ABM produced output that could not be readily generated by other techniques. On average there were 1.99 (S.E. 0.245) human infections and 1.83 (S.E. 0.183) cattle infections per 6 month period. The model output identified that the approximate incidence rate (per 1000 person-years) was lower amongst cattle owning households (0.079, S.E. 0.017), than those without cattle (0.134, S.E. 0.017). Immigrant tribes (e.g. Bemba I.R. = 0.353, S.E.0.155) and school-age children (e.g. 5–10 year old I.R. = 0.239, S.E. 0.041) were the most at-risk for acquiring infection. These findings have the potential to aid the targeting of future mitigation strategies. Conclusion: ABMs provide an alternative way of thinking about HAT and NTDs more generally, offering a solution to the investigation of local-scale questions, and which generate results that can be easily disseminated to those affected. The ABM can be used as a tool for scenario testing at an appropriate spatial scale to allow the design of logistically feasible mitigation strategies suggested by model output. This is of particular importance where resources are limited and management strategies are often pushed to the local scale. [ABSTRACT FROM AUTHOR]

Background: Vector control remains the primary method to prevent dengue infections. Environmental interventions represent sustainable and safe methods as there are limited risks of environmental contamination and toxicity. The objective of this study is to perform a systematic review and meta-analysis of the effectiveness of the following environmental methods for dengue vector control. Methodology/Principal findings: Following the PRISMA guidelines, a systematic literature search was conducted using the databases PubMed, EMBASE, LILACS, the Cochrane Library and Google Scholar. Quality assessment was done using the CONSORT 2010 checklist. For the meta-analysis the difference-in-differences (DID) and the difference-of-endlines (DOE) were calculated according to the Schmidt-Hunter method for the Breteau index (BI) and the pupae per person index (PPI). Nineteen studies were eligible for the systematic review, sixteen contributed data to the meta-analysis. The following methods were evaluated: (a) container covers with and without insecticides, (b) waste management and clean-up campaigns, and (c) elimination of breeding sites by rendering potential mosquito breeding sites unusable or by eliminating them. Study quality was highest for container covers with insecticides, followed by waste management without direct garbage collection and elimination of breeding places. Both, systematic review and meta-analysis, showed a weak effect of the interventions on larval populations, with no obvious differences between the results of each individual method. For the meta-analysis, both, container covers without insecticides (BI: DID -7.9, DOE -5) and waste management with direct garbage collection (BI: DID -8.83, DOE -6.2) achieved the strongest reductions for the BI, whereas for the PPI results were almost opposite, with container covers with insecticides (PPI: DID -0.83, DOE 0.09) and elimination of breeding places (PPI: DID -0.95, DOE -0.83) showing the strongest effects. Conclusions: Each of the investigated environmental methods showed some effectiveness in reducing larval and pupal densities of Aedes sp. mosquitoes. However, there is a need for more comparable high-quality studies at an adequate standard to strengthen this evidence. [ABSTRACT FROM AUTHOR]