4
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Seroprevalence of Toxoplasma gondii in commensal rodents sampled across Senegal, West Africa Translated title: Séroprévalence de Toxoplasma gondii chez les rongeurs commensaux au Sénégal, Afrique de l’Ouest

      Read this article at

      ScienceOpenPublisherPMC
      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

          Risks related to Toxoplasma gondii infection in humans remain poorly known in Senegal. Although rodent surveys could help to assess the circulation of T. gondii, they have seldom been set up in sub-Saharan Africa. The aim of this study was to examine Toxoplasma seroprevalence in rodents from villages and towns across Senegal. Rodents were sampled in 40 localities using a standardised trapping protocol. Detection of T. gondii antibodies was performed on 1205 rodents, using a modified agglutination test (MAT) technique. Seroprevalence data were analysed depending on geography, the local rodent community, and individual characteristics of the rodent hosts. We found 44 seropositive rodents from four different species ( Mastomys erythroleucus, Mastomys natalensis, Mus musculus domesticus, Rattus rattus). Toxoplasma seroprevalence was low, averaging 4% in the localities. Higher Toxoplasma seroprevalence (up to 24%) was found in northern Senegal, a region known to be the heart of pastoral herding in the country.

          Translated abstract

          Les risques liés à l’infection par Toxoplasma gondii chez l’homme restent mal connus au Sénégal. Bien que les rongeurs soient considérés comme des marqueurs pertinents de la circulation de T. gondii, peu d’études ont ciblé ces hôtes en Afrique sub-saharienne. Le but de cette étude était d’étudier la séroprévalence de Toxoplasma chez les rongeurs des villes et des villages du Sénégal. Les rongeurs ont été échantillonnés dans 40 localités avec un protocole de piégeage standardisé. La détection des anticorps contre T. gondii a été faite sur 1205 rongeurs avec un test d’agglutination modifié. Les données de séroprévalence ont été analysées en fonction de la géographie, de la communauté locale de rongeurs et de caractéristiques individuelles du rongeur. Nous avons trouvé 44 individus séropositifs, de quatre espèces différentes ( Mastomys erythroleucus, Mastomys natalensis, Mus musculus domesticus, Rattus rattus). La séroprévalence moyenne est faible, de l’ordre de 4 % au sein des localités. Les plus fortes séroprévalences (jusqu’à 24 %) ont été observées dans le Nord du Sénégal, une région connue pour être le cœur de l’élevage pastoral dans le pays.

          Related collections

          Most cited references 36

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

          Spatial epidemiology: an emerging (or re-emerging) discipline.

          Spatial epidemiology is the study of spatial variation in disease risk or incidence. Several ecological processes can result in strong spatial patterns of such risk or incidence: for example, pathogen dispersal might be highly localized, vectors or reservoirs for pathogens might be spatially restricted, or susceptible hosts might be clumped. Here, we briefly describe approaches to spatial epidemiology that are spatially implicit, such as metapopulation models of disease transmission, and then focus on research in spatial epidemiology that is spatially explicit, such as the creation of risk maps for particular geographical areas. Although the spatial dynamics of infectious diseases are the subject of intensive study, the impacts of landscape structure on epidemiological processes have so far been neglected. The few studies that demonstrate how landscape composition (types of elements) and configuration (spatial positions of those elements) influence disease risk or incidence suggest that a true integration of landscape ecology with epidemiology will be fruitful.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Measurement of Diversity

             E. SIMPSON (1949)
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found
              Is Open Access

              Fitting Linear Mixed-Effects Models using lme4

              Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer.
                Bookmark

                Author and article information

                Journal
                Parasite
                Parasite
                parasite
                Parasite
                EDP Sciences
                1252-607X
                1776-1042
                2018
                16 July 2018
                : 25
                : ( publisher-idID: parasite/2018/01 )
                Affiliations
                [1 ] CBGP, IRD, CIRAD, INRA, Montpellier SupAgro, Univ. Montpellier 755 avenue du campus Agropolis 34988 Montferrier-sur-Lez cedex France
                [2 ] BIOPASS, CBGP-IRD, ISRA, UCAD Campus de Bel-Air BP 1386 Dakar CP 18524 Senegal
                [3 ] UMR-S 1094, Neuroépidémiologie Tropicale, INSERM, Univ. Limoges 2 rue du Dr Marcland 87025 Limoges France
                [4 ] LPED, IRD, Aix Marseille Univ., Centre St Charles case 10, 3 place Victor Hugo CS 80249 13331 Marseille cedex 03 France
                Author notes
                [* ]Corresponding author: carine.brouat@ 123456ird.fr
                Article
                parasite180021 10.1051/parasite/2018036
                10.1051/parasite/2018036
                6050035
                30016257
                © C. Brouat et al., published by EDP Sciences, 2018

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                Page count
                Figures: 1, Tables: 1, Equations: 0, References: 33, Pages: 6
                Categories
                Research Article

                rodents, africa, senegal, seroprevalence, toxoplasma gondii

                Comments

                Comment on this article