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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]

Introduction: Schistosomiasis is a chronic parasitic trematode disease that affects over 240 million people worldwide. The Schistosoma lifecycle is complex, involving transmission via specific intermediate-host freshwater snails. Predictive mathematical models of Schistosoma transmission have often chosen to simplify or ignore the details of environmental human-snail interaction in their analyses. Schistosome transmission models now aim to provide better precision for policy planning of elimination of transmission. This heightens the importance of including the environmental complexity of vector-pathogen interaction in order to make more accurate projections. Methodology and principal findings: We propose a nonlinear snail force of infection (FOI) that takes into account an intermediate larval stage (miracidium) and snail biology. We focused, in particular, on the effects of snail force of infection (FOI) on the impact of mass drug administration (MDA) in human communities. The proposed (modified) model was compared to a conventional model in terms of their predictions. A longitudinal dataset generated in Kenya field studies was used for model calibration and validation. For each sample community, we calibrated modified and conventional model systems, then used them to model outcomes for a range of MDA regimens. In most cases, the modified model predicted more vigorous post-MDA rebound, with faster relapse to baseline levels of infection. The effect was pronounced in higher risk communities. When compared to observed data, only the modified system was able to successfully predict persistent rebound of Schistosoma infection. Conclusion and significance: The observed impact of varying location-specific snail inputs sheds light on the diverse MDA response patterns noted in operational research on schistosomiasis control, such as the recent SCORE project. Efficiency of human-to-snail transmission is likely to be much higher than predicted by standard models, which, in practice, will make local elimination by implementation of MDA alone highly unlikely, even over a multi-decade period. [ABSTRACT FROM AUTHOR]

Internet-based surveillance methods for vector-borne diseases (VBDs) using “big data” sources such as Google, Twitter, and internet newswire scraping have recently been developed, yet reviews on such “digital disease detection” methods have focused on respiratory pathogens, particularly in high-income regions. Here, we present a narrative review of the literature that has examined the performance of internet-based biosurveillance for diseases caused by vector-borne viruses, parasites, and other pathogens, including Zika, dengue, other arthropod-borne viruses, malaria, leishmaniasis, and Lyme disease across a range of settings, including low- and middle-income countries. The fundamental features, advantages, and drawbacks of each internet big data source are presented for those with varying familiarity of “digital epidemiology.” We conclude with some of the challenges and future directions in using internet-based biosurveillance for the surveillance and control of VBD. [ABSTRACT FROM AUTHOR]

Background: The control or elimination of neglected tropical diseases has recently become the focus of increased interest and funding from international agencies through the donation of drugs. Resources are becoming available for the treatment of soil-transmitted helminth (STH) infection through school-based deworming strategies. However, little research has been conducted to assess the impact of STH treatment that could be used to guide the design of efficient elimination programs. Methodology: We construct and analyse an age-structured model of STH population dynamics under regular treatment. We investigate the potential for elimination with finite rounds of treatment, and how this depends on the value of the basic reproductive number R0 and treatment frequency. Principal findings: Analysis of the model indicates that its behaviour is determined by key parameter groupings describing the basic reproduction number and the fraction of it attributable to the treated group, the timescale of material in the environment and the frequency and efficacy of treatment. Mechanisms of sexual reproduction and persistence of infectious material in the environment are found to be much more important in the context of elimination than in the undisturbed baseline scenario. For a given rate of drug use, sexual reproduction dictates that less frequent, higher coverage treatment is more effective. For a given treatment coverage level, the lifespan of infectious material in the environment places a limit on the effectiveness of increased treatment frequency. Conclusions: Our work suggests that for models to capture the dynamics of parasite burdens in populations under regular treatment as elimination is approached, they need to include the effects of sexual reproduction among parasites and the dynamics infectious material in the reservoir. The interaction of these two mechanisms has a strong effect on optimum treatment strategies, both in terms of how frequently to treat and for how long. [ABSTRACT FROM AUTHOR]

The author explores the contributions of the African Programme for Onchocerciasis Control (APOC) for treating patients with onchocerciasis in 16 sub-Saharan countries including Cameroon, Congo and Ethiopia. Details on the updates health impact of the disease estimation based on disability weights from year 1995 to 2015 is discussed. The author also emphasizes the risk of skin disease in patients with onchocerciasis.

Background: Epidemics of meningococcal meningitis (MM) recurrently strike the African Meningitis Belt. This study aimed at investigating factors, still poorly understood, that influence annual incidence of MM serogroup A, the main etiologic agent over 2004–2010, at a fine spatial scale in Niger. Methodology/Principal Findings: To take into account data dependencies over space and time and control for unobserved confounding factors, we developed an explanatory Bayesian hierarchical model over 2004–2010 at the health centre catchment area (HCCA) level. The multivariate model revealed that both climatic and non-climatic factors were important for explaining spatio-temporal variations in incidence: mean relative humidity during November–June over the study region (posterior mean Incidence Rate Ratio (IRR) = 0.656, 95% Credible Interval (CI) 0.405–0.949) and occurrence of early rains in March in a HCCA (IRR = 0.353, 95% CI 0.239–0.502) were protective factors; a higher risk was associated with the percentage of neighbouring HCCAs having at least one MM A case during the same year (IRR = 2.365, 95% CI 2.078–2.695), the presence of a road crossing the HCCA (IRR = 1.743, 95% CI 1.173–2.474) and the occurrence of cases before 31 December in a HCCA (IRR = 6.801, 95% CI 4.004–10.910). At the study region level, higher annual incidence correlated with greater geographic spread and, to a lesser extent, with higher intensity of localized outbreaks. Conclusions: Based on these findings, we hypothesize that spatio-temporal variability of MM A incidence between years and HCCAs result from variations in the intensity or duration of the dry season climatic effects on disease risk, and is further impacted by factors of spatial contacts, representing facilitated pathogen transmission. Additional unexplained factors may contribute to the observed incidence patterns and should be further investigated. [ABSTRACT FROM AUTHOR]