3
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Modelling temporal dynamics of Culicoides Latreille (Diptera: Ceratopogonidae) populations on Reunion Island (Indian Ocean), vectors of viruses of veterinary importance

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          Reunion Island regularly faces outbreaks of epizootic haemorrhagic disease (EHD) and bluetongue (BT), two viral diseases transmitted by haematophagous midges of the genus Culicoides (Diptera: Ceratopogonidae) to ruminants. To date, five species of Culicoides are recorded in Reunion Island in which the first two are proven vector species: Culicoides bolitinos, C. imicola, C. enderleini, C. grahamii and C. kibatiensis. Meteorological and environmental factors can severely constrain Culicoides populations and activities and thereby affect dispersion and intensity of transmission of Culicoides-borne viruses. The aim of this study was to describe and predict the temporal dynamics of all Culicoides species present in Reunion Island.

          Methods

          Between 2016 and 2018, 55 biweekly Culicoides catches using Onderstepoort Veterinary Institute traps were set up in 11 sites. A hurdle model (i.e. a presence/absence model combined with an abundance model) was developed for each species in order to determine meteorological and environmental drivers of presence and abundance of Culicoides.

          Results

          Abundance displayed very strong heterogeneity between sites. Average Culicoides catch per site per night ranged from 4 to 45,875 individuals. Culicoides imicola was dominant at low altitude and C. kibatiensis at high altitude. A marked seasonality was observed for the three other species with annual variations. Twelve groups of variables were tested. It was found that presence and/or abundance of all five Culicoides species were driven by common parameters: rain, temperature, vegetation index, forested environment and host density. Other parameters such as wind speed and farm building opening size governed abundance level of some species. In addition, Culicoides populations were also affected by meteorological parameters and/or vegetation index with different lags of time, suggesting an impact on immature stages. Taking into account all the parameters for the final hurdle model, the error rate by Normalized Root mean Square Error ranged from 4.4 to 8.5%.

          Conclusions

          To our knowledge, this is the first study to model Culicoides population dynamics in Reunion Island. In the absence of vaccination and vector control strategies, determining periods of high abundance of Culicoides is a crucial first step towards identifying periods at high risk of transmission for the two economically important viruses they transmit.

          Related collections

          Most cited references77

          • Record: found
          • Abstract: found
          • Article: not found

          Climate and vectorborne diseases.

          Climate change could significantly affect vectorborne disease in humans. Temperature, precipitation, humidity, and other climatic factors are known to affect the reproduction, development, behavior, and population dynamics of the arthropod vectors of these diseases. Climate also can affect the development of pathogens in vectors, as well as the population dynamics and ranges of the nonhuman vertebrate reservoirs of many vectorborne diseases. Whether climate changes increase or decrease the incidence of vectorborne diseases in humans will depend not only on the actual climatic conditions but also on local nonclimatic epidemiologic and ecologic factors. Predicting the relative impact of sustained climate change on vectorborne diseases is difficult and will require long-term studies that look not only at the effects of climate change but also at the contributions of other agents of global change such as increased trade and travel, demographic shifts, civil unrest, changes in land use, water availability, and other issues. Adapting to the effects of climate change will require the development of adequate response plans, enhancement of surveillance systems, and development of effective and locally appropriate strategies to control and prevent vectorborne diseases.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Is cross-validation valid for small-sample microarray classification?

            Microarray classification typically possesses two striking attributes: (1) classifier design and error estimation are based on remarkably small samples and (2) cross-validation error estimation is employed in the majority of the papers. Thus, it is necessary to have a quantifiable understanding of the behavior of cross-validation in the context of very small samples. An extensive simulation study has been performed comparing cross-validation, resubstitution and bootstrap estimation for three popular classification rules-linear discriminant analysis, 3-nearest-neighbor and decision trees (CART)-using both synthetic and real breast-cancer patient data. Comparison is via the distribution of differences between the estimated and true errors. Various statistics for the deviation distribution have been computed: mean (for estimator bias), variance (for estimator precision), root-mean square error (for composition of bias and variance) and quartile ranges, including outlier behavior. In general, while cross-validation error estimation is much less biased than resubstitution, it displays excessive variance, which makes individual estimates unreliable for small samples. Bootstrap methods provide improved performance relative to variance, but at a high computational cost and often with increased bias (albeit, much less than with resubstitution).
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Landscape epidemiology of vector-borne diseases.

              Landscape epidemiology describes how the temporal dynamics of host, vector, and pathogen populations interact spatially within a permissive environment to enable transmission. The spatially defined focus, or nidus, of transmission may be characterized by vegetation as well as by climate, latitude, elevation, and geology. The ecological complexity, dimensions, and temporal stability of the nidus are determined largely by pathogen natural history and vector bionomics. Host populations, transmission efficiency, and therefore pathogen amplification vary spatially, thereby creating a heterogeneous surface that may be defined by remote sensing and statistical tools. The current review describes the evolution of landscape epidemiology as a science and exemplifies selected aspects by contrasting the ecology of two different recent disease outbreaks in North America caused by West Nile virus, an explosive, highly virulent mosquito-borne virus producing ephemeral nidi, and Borrelia burgdorferi, a slowly amplifying chronic pathogen producing semipermanent nidi.
                Bookmark

                Author and article information

                Contributors
                yannick.grimaud@gds974.re
                helene.guis@cirad.fr
                frederic.chiroleu@cirad.fr
                floriane.boucher@cirad.fr
                annelise.tran@cirad.fr
                ignace.rakotoarivony@cirad.fr
                maxime.duhayon@cirad.fr
                catherine.cetre-sossah@cirad.fr
                olivier.esnault@gds974.re
                eric.cardinale@cirad.fr
                claire.garros@cirad.fr
                Journal
                Parasit Vectors
                Parasit Vectors
                Parasites & Vectors
                BioMed Central (London )
                1756-3305
                27 November 2019
                27 November 2019
                2019
                : 12
                : 562
                Affiliations
                [1 ]GDS Réunion, 1 rue du Père Hauck, 97418 La Plaine des Cafres, La Réunion France
                [2 ]University of Reunion Island, 15 avenue René Cassin, 97715 Sainte-Clotilde, La Réunion France
                [3 ]ISNI 0000 0001 2153 9871, GRID grid.8183.2, CIRAD, UMR ASTRE, ; 97490 Sainte-Clotilde, La Réunion France
                [4 ]CIRAD, UMR ASTRE, 101 Antananarivo, Madagascar
                [5 ]ISNI 0000 0001 2097 0141, GRID grid.121334.6, ASTRE, University of Montpellier, CIRAD, INRA, ; Montpellier, France
                [6 ]ISNI 0000 0004 0552 7303, GRID grid.418511.8, Epidemiology and clinical research unit, , Institut Pasteur of Madagascar, ; Antananarivo, Madagascar
                [7 ]FOFIFA DRZVP, Antananarivo, Madagascar
                [8 ]ISNI 0000 0001 2153 9871, GRID grid.8183.2, CIRAD, UMR PBVMT, ; 97410 Saint-Pierre, La Réunion France
                [9 ]ISNI 0000 0001 2153 9871, GRID grid.8183.2, CIRAD, UMR TETIS, ; 97490 Sainte-Clotilde, La Réunion France
                [10 ]ISNI 0000 0001 2097 0141, GRID grid.121334.6, TETIS, University of Montpellier, ; Montpellier, France
                [11 ]ISNI 0000 0001 2153 9871, GRID grid.8183.2, CIRAD, UMR ASTRE, ; 34398 Montpellier, France
                Article
                3812
                10.1186/s13071-019-3812-1
                6880491
                31775850
                76278f8b-b6c0-4dce-a816-fd9ec1b428ce
                © The Author(s) 2019

                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
                : 17 June 2019
                : 15 November 2019
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100007204, Centre de Coopération Internationale en Recherche Agronomique pour le Développement;
                Categories
                Research
                Custom metadata
                © The Author(s) 2019

                Parasitology
                culicoides,temporal dynamics,hurdle model,reunion island,bluetongue,epizootic hemorrhagic disease,indian ocean

                Comments

                Comment on this article