59 research outputs found

    The Dominant Mosquito Vectors of Human Malaria in India

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    Characterization Of Anopheline Species Composition Along The Bhutan-India Border Region

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    Bhutan is aggressively embarking on a path towards malaria elimination. Despite substantial progress, Bhutan remains vulnerable to imported malaria. The majority of cases are in Sarpang district, which shares a border with the state of Assam in India. However, the anopheline species responsible for autochthonous malaria transmission have not been well characterized. Therefore, a comparison of the Anopheles species in Sarpang was made with published records of anopheline mosquitoes in neighboring Assam. An assessment of Anopheles species composition was undertaken from June to July 2014 in four Sarpang villages adjacent to the Sarpang-Assam border. Five sampling methods were employed: (1) human landing catches, (2) cattle-baited catches, (3) CDC light traps, (4) indoor resting catches and (5) resting boxes. Female anopheline mosquitoes were identified to species using a morphological key. These results were compared to published literature on anopheline ecology and vectorial roles in Assam. The two suspected malaria vectors in Bhutan, Anopheles culicifacies (n=189) and An. pseudowillmori (n=205), were abundant in the Sarpang villages. However, in Assam, only An. culicifacies species B, a relatively incompetent vector, has been documented. In contrast, the primary malaria vectors of Assam, An. minimus and An. baimaii, were absent in the Sarpang collections. If An. culicifacies is not a competent vector in Sarpang, the other recovered species – An. pseudowillmori and An. maculatus – may be the responsible vectors for malaria transmission in Sarpang. Nonetheless, molecular methods are required to identify members of several sibling species complex in this region; however, adequate equipment and additional training of personnel will be necessary to address this difficulty

    Delimiting Cryptic Morphological Variation among Human Malaria Vector Species using Convolutional Neural Networks

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    Deep learning is a powerful approach for distinguishing classes of images, and there is a growing interest in applying these methods to delimit species, particularly in the identification of mosquito vectors. Visual identification of mosquito species is the foundation of mosquito-borne disease surveillance and management, but can be hindered by cryptic morphological variation in mosquito vector species complexes such as the malaria-transmitting Anopheles gambiaecomplex. We sought to apply Convolutional Neural Networks (CNNs) to images of mosquitoes as a proof-of-concept to determine the feasibility of automatic classification of mosquito sex, genus, species, and strains using whole-body, 2D images of mosquitoes. We introduce a library of 1, 709 images of adult mosquitoes collected from 16 colonies of mosquito vector species and strains originating from five geographic regions, with 4 cryptic species not readily distinguishable morphologically even by trained medical entomologists. We present a methodology for image processing, data augmentation, and training and validation of a CNN. Our best CNN configuration achieved high prediction accuracies of 96.96% for species identification and 98.48% for sex. Our results demonstrate that CNNs can delimit species with cryptic morphological variation, 2 strains of a single species, and specimens from a single colony stored using two different methods. We present visualizations of the CNN feature space and predictions for interpretation of our results, and we further discuss applications of our findings for future applications in malaria mosquito surveillance

    Ecology of Larval Habitats

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    Impact of dams and irrigation schemes in Anopheline (Diptera: Culicidae) bionomics and malaria epidemiology

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    Irrigation schemes and dams have posed a great concern on public health systems of several countries, mainly in the tropics. The focus of the present review is to elucidate the different ways how these human interventions may have an effect on population dynamics of anopheline mosquitoes and hence, how local malaria transmission patterns may be changed. We discuss different studies within the three main tropical and sub-tropical regions (namely Africa, Asia and the Pacific and the Americas). Factors such as pre-human impact malaria epidemiological patterns, control measures, demographic movements, human behaviour and local Anopheles bionomics would determine if the implementation of an irrigation scheme or a dam will have negative effects on human health. Some examples of successful implementation of control measures in such settings are presented. The use of Geographic Information System as a powerful tool to assist on the study and control of malaria in these scenarios is also highlighted.Intervenções humanas como projetos de irrigação e usinas hidrelétricas, tem se transformado em graves problemas de saúde em muitos países, especialmente naqueles localizados nos trópicos. No presente artigo discutimos os efeitos que essas intervenções causam a dinâmica populacional dos anofelinos e nos padrões de transmissão de malaria. Foram revisados estudos feitos nas três principais regiões geográficas dos trópicos e sub-trópicos (África, Ásia e o Pacífico e Américas). Constatamos que os padrões da transmissão da malária antes da introdução dos empreendimentos, as medidas de controle, os movimentos demográficos, os padrões comportamentais das comunidades humanas e a bionomia dos anofelinos locais determinarão se o estabelecimento de campos de irrigação e/ou usinas hidrelétricas podem influenciar negativamente na saúde das pessoas. São apresentados exemplos de medidas de controle bem sucedidas nesses cenários. A utilização de Sistemas de Informação Geográfico tem sido destacada como uma importante ferramenta para subsidiar o estudo e controle da malária em áreas sob impacto ambiental

    Environmental Management for Malaria Control in the East Asia and Pacific (EAP) Region

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    Pursuant to Rule 24 (j), R. Utah S. Ct., defendant LDS Social Services hereby submits the following supplemental authority for Point III of its Respondents\u27 Brief, pertaining specifically to the validity of the presumption of abandonment in U.C.A. § 78-30- 4(3)(c)

    Spatial Analysis of Land Cover Determinants of Malaria Incidence in the Ashanti Region, Ghana

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    Malaria belongs to the infectious diseases with the highest morbidity and mortality worldwide. As a vector-borne disease malaria distribution is strongly influenced by environmental factors. The aim of this study was to investigate the association between malaria risk and different land cover classes by using high-resolution multispectral Ikonos images and Poisson regression analyses. The association of malaria incidence with land cover around 12 villages in the Ashanti Region, Ghana, was assessed in 1,988 children <15 years of age. The median malaria incidence was 85.7 per 1,000 inhabitants and year (range 28.4–272.7). Swampy areas and banana/plantain production in the proximity of villages were strong predictors of a high malaria incidence. An increase of 10% of swampy area coverage in the 2 km radius around a village led to a 43% higher incidence (relative risk [RR] = 1.43, p<0.001). Each 10% increase of area with banana/plantain production around a village tripled the risk for malaria (RR = 3.25, p<0.001). An increase in forested area of 10% was associated with a 47% decrease of malaria incidence (RR = 0.53, p = 0.029)
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