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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]

Riverine tsetse transmit the parasites that cause the most prevalent form of human African trypanosomiasis, Gambian HAT. In response to the imperative for cheap and efficient tsetse control, insecticide-treated ‘tiny targets’ have been developed through refinement of tsetse attractants based on blue fabric panels. However, modern blue polyesters used for this purpose attract many less tsetse than traditional phthalogen blue cottons. Therefore, colour engineering polyesters for improved attractiveness has great potential for tiny target development. Because flies have markedly different photoreceptor spectral sensitivities from humans, and the responses of these photoreceptors provide the inputs to their visually guided behaviours, it is essential that polyester colour engineering be guided by fly photoreceptor-based explanations of tsetse attraction. To this end, tsetse attraction to differently coloured fabrics was recently modelled using the calculated excitations elicited in a generic set of fly photoreceptors as predictors. However, electrophysiological data from tsetse indicate the potential for modified spectral sensitivities versus the generic pattern, and processing of fly photoreceptor responses within segregated achromatic and chromatic channels has long been hypothesised. Thus, I constructed photoreceptor-based models explaining the attraction of G. f. fuscipes to differently coloured tiny targets recorded in a previously published investigation, under differing assumptions about tsetse spectral sensitivities and organisation of visual processing. Models separating photoreceptor responses into achromatic and chromatic channels explained attraction better than earlier models combining weighted photoreceptor responses in a single mechanism, regardless of the spectral sensitivities assumed. However, common principles for fabric colour engineering were evident across the complete set of models examined, and were consistent with earlier work. Tools for the calculation of fly photoreceptor excitations are available with this paper, and the ways in which these and photoreceptor-based models of attraction can provide colorimetric values for the engineering of more-attractively coloured polyester fabrics are discussed. [ABSTRACT FROM AUTHOR]

Objectives: We contribute a new methodological approach to the ongoing efforts towards evaluating public health surveillance. Specifically, we apply a descriptive framework, grounded in prospect theory (PT), for the evaluation of decisions on disease surveillance deployment. We focus on two attributes of any surveillance system: timeliness, and false positive rate (FPR). Methods: In a sample of 69 health professionals from a number of health related networks polled online, we elicited PT preferences, specifically respondents’ attitudes towards gains, losses and probabilities (i.e., if they overweight or underweight extreme probabilities) by means of a series of lotteries for either timeliness or FPR. Moreover, we estimated willingness to pay (WTP) for improvements in the two surveillance attributes. For contextualization, we apply our framework to rabies surveillance. Results: Our data reveal considerable probability weighting, both for gains and losses. In other words, respondents underestimate their chances of getting a good outcome in uncertain situations, and they overestimate their chances of bad outcomes. Moreover, there is convex utility for losses and loss aversion, that is, losses loom larger than gains of the same absolute magnitude to the respondents. We find no differences between the estimated parameters for timeliness and FPR. The median WTP is $7,250 per day gained in detection time and $30 per 1/10,000 reduction in FPR. Conclusion: Our results indicate that the biases described by PT are present among public health professionals, which highlights the need to incorporate a PT framework when eliciting their preferences for surveillance systems. [ABSTRACT FROM AUTHOR]

Background: Trachomatous trichiasis significantly reduces vision and health related quality of life (QoL). Although trichiasis surgery is widely performed to treat trichiasis, there is little data on the effect of surgery on QoL. We measured the impact of trichiasis surgery on vision and health related QoL in a longitudinal study from Amhara Region, Ethiopia. Methodology/Principal Findings: We recruited 1000 adult participants with trichiasis (cases) and 200 comparison participants, matched to every fifth trichiasis case on age (+/- two years), sex and location. Vision-related quality of life (VRQoL) and health-related quality of life (HRQoL) were measured using the WHO/PBD-VF20 and WHOQOL-BREF questionnaires respectively, at enrolment and 12 months after enrolment. All trichiasis cases received free standard trichiasis surgery immediately after enrolment. The mean difference in QoL scores between enrolment and follow-up for cases and comparison participants, and the difference-in-differences by baseline trichiasis status was analysed using random effects linear regression, the later adjusted for age, sex and socioeconomic status. At 12-months follow-up, data was collected from 980 (98%) and 198 (98%) trichiasis cases and comparison participants respectively. At this follow-up visit, VRQoL and HRQoL scores of trichiasis cases improved substantially in all subscales and domains by 19.1–42.0 points (p<0.0001) and 4.7–17.2 points (p<0.0001), respectively. In contrast, among the comparison participants, there was no evidence of improvement in VRQoL and HRQoL domain scores during follow-up. The improvement in VRQoL and HRQoL in cases was independent of the presence of visual acuity improvement at 12 months. Conclusions/Significance: Trichiasis surgery substantially improves both VRQoL and HRQoL regardless of visual acuity change. Unprecedented effort is needed to scale-up trichiasis surgical programmes not only to prevent the risk of sight loss but also to improve overall wellbeing and health perception of affected individuals. [ABSTRACT FROM AUTHOR]

Background: Access to laboratory diagnosis can be a challenge for individuals suspected of Buruli Ulcer (BU). Our objective was to develop a clinical score to assist clinicians working in resource-limited settings for BU diagnosis. Methododology/Principal Findings: Between 2011 and 2013, individuals presenting at Akonolinga District Hospital, Cameroon, were enrolled consecutively. Clinical data were collected prospectively. Based on a latent class model using laboratory test results (ZN, PCR, culture), patients were categorized into high, or low BU likelihood. Variables associated with a high BU likelihood in a multivariate logistic model were included in the Buruli score. Score cut-offs were chosen based on calculated predictive values. Of 325 patients with an ulcerative lesion, 51 (15.7%) had a high BU likelihood. The variables identified for the Buruli score were: characteristic smell (+3 points), yellow color (+2), female gender (+2), undermining (+1), green color (+1), lesion hyposensitivity (+1), pain at rest (-1), size >5cm (-1), locoregional adenopathy (-2), age above 20 up to 40 years (-3), or above 40 (-5). This score had AUC of 0.86 (95%CI 0.82–0.89), indicating good discrimination between infected and non-infected individuals. The cut-off to reasonably exclude BU was set at scores <0 (NPV 96.5%; 95%CI 93.0–98.6). The treatment threshold was set at a cut-off ≥4 (PPV 69.0%; 95%CI 49.2–84.7). Patients with intermediate BU probability needed to be tested by PCR. Conclusions/Significance: We developed a decisional algorithm based on a clinical score assessing BU probability. The Buruli score still requires further validation before it can be recommended for wide use. [ABSTRACT FROM AUTHOR]