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      Patterns of tsetse abundance and trypanosome infection rates among habitats of surveyed villages in Maasai steppe of northern Tanzania

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          Abstract

          Background

          Changes of land cover modify the characteristics of habitat, host-vector interaction and consequently infection rates of disease causing agents. In this paper, we report variations in tsetse distribution patterns, abundance and infection rates in relation to habitat types and age in the Maasai Steppe of northern Tanzania. In Africa, Tsetse-transmitted trypanosomiasis negatively impacted human life where about 40 million people are at risk of contracting the disease with dramatic socio-economical consequences, for instance, loss of livestock, animal productivity, and manpower.

          Methods

          We trapped tsetse flies in dry and wet seasons between October 2014 and May 2015 in selected habitats across four villages: Emboreet, Loiborsireet, Kimotorok and Oltukai adjacent to protected areas. Data collected include number and species of tsetse flies caught in baited traps, PCR identification of trypanosome species and extraction of monitored Normalized Difference Vegetation Index (NDVI) data from Moderate Resolution Imaging Spectrometer (MODIS).

          Results

          Our findings demonstrate the variation of tsetse fly species abundance and infection rates among habitats in surveyed villages in relation to NDVI and host abundance. Results have shown higher tsetse fly abundance in Acacia-swampy ecotone and riverine habitats for Emboreet and other villages, respectively. Tsetse abundance was inconsistent among habitats in different villages. Emboreet was highly infested with Glossina swynnertoni (68%) in ecotone and swampy habitats followed by G. morsitans (28%) and G. pallidipes (4%) in riverine habitat. In the remaining villages, the dominant tsetse fly species by 95% was G. pallidipes in all habitats. Trypanosoma vivax was the most prevalent species in all infected flies (95%) with few observations of co-infections (with T. congolense or T. brucei).

          Conclusions

          The findings of this study provide a framework to mapping hotspots of tsetse infestation and trypanosomiasis infection and enhance the communities to plan for effective control of trypanosomiasis.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s40249-017-0340-0) contains supplementary material, which is available to authorized users.

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          Most cited references43

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          R: A Language and Environment for Statistical Computing

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            Pathogenic landscapes: Interactions between land, people, disease vectors, and their animal hosts

            Background Landscape attributes influence spatial variations in disease risk or incidence. We present a review of the key findings from eight case studies that we conducted in Europe and West Africa on the impact of land changes on emerging or re-emerging vector-borne diseases and/or zoonoses. The case studies concern West Nile virus transmission in Senegal, tick-borne encephalitis incidence in Latvia, sandfly abundance in the French Pyrenees, Rift Valley Fever in the Ferlo (Senegal), West Nile Fever and the risk of malaria re-emergence in the Camargue, and rodent-borne Puumala hantavirus and Lyme borreliosis in Belgium. Results We identified general principles governing landscape epidemiology in these diverse disease systems and geographic regions. We formulated ten propositions that are related to landscape attributes, spatial patterns and habitat connectivity, pathways of pathogen transmission between vectors and hosts, scale issues, land use and ownership, and human behaviour associated with transmission cycles. Conclusions A static view of the "pathogenecity" of landscapes overlays maps of the spatial distribution of vectors and their habitats, animal hosts carrying specific pathogens and their habitat, and susceptible human hosts and their land use. A more dynamic view emphasizing the spatial and temporal interactions between these agents at multiple scales is more appropriate. We also highlight the complementarity of the modelling approaches used in our case studies. Integrated analyses at the landscape scale allows a better understanding of interactions between changes in ecosystems and climate, land use and human behaviour, and the ecology of vectors and animal hosts of infectious agents.
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              Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains

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                Author and article information

                Contributors
                anibarikingonyoka@gmail.com
                Journal
                Infect Dis Poverty
                Infect Dis Poverty
                Infectious Diseases of Poverty
                BioMed Central (London )
                2049-9957
                4 September 2017
                4 September 2017
                2017
                : 6
                : 126
                Affiliations
                [1 ]ISNI 0000 0004 0468 1595, GRID grid.451346.1, School of Life Sciences and Bioengineering, , Nelson Mandela African Institution of Science and Technology, ; P.O. Box 447, Arusha, Tanzania
                [2 ]ISNI 0000 0000 9428 8105, GRID grid.11887.37, Genome Sciences Center, Department of Microbiology, Parasitology and Immunology. College of Veterinary and Medical Sciences, , Sokoine University of Agriculture, ; Morogoro, Tanzania
                [3 ]ISNI 0000 0001 2097 4281, GRID grid.29857.31, Centre for Infectious Disease Dynamics, The Huck Institutes of the Life Sciences, , The Pennsylvania State University, ; University Park, USA
                [4 ]GRID grid.442459.a, Department of Conservation Biology, School of Biological Sciences, , University of Dodoma, ; Dodoma, Tanzania
                [5 ]GRID grid.442459.a, Department of Geography and Environmental studies, , University of Dodoma, ; Dodoma, Tanzania
                Author information
                http://orcid.org/0000-0002-0590-9833
                Article
                340
                10.1186/s40249-017-0340-0
                5582388
                28866983
                7d668619-0e6f-410c-ac97-1db6cd1f863d
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 13 March 2017
                : 26 July 2017
                Funding
                Funded by: World Health Organization Through TDR
                Award ID: B20280
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2017

                habitat variability,tsetse fly,host availability,infection rate,trypanosomes

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