Evidence map›Paper›PMID 29884209›Full record

ArticleParasites & vectors2018

Spatial distribution modelling of Culicoides (Diptera: Ceratopogonidae) biting midges, potential vectors of African horse sickness and bluetongue viruses in Senegal.

Maryam Diarra, Moussa Fall, Assane Gueye Fall, Aliou Diop, Renaud Lancelot, Momar Talla Seck, Ignace Rakotoarivony, Xavier Allène, Jérémy Bouyer, Hélène Guis

Abstract read
In one paragraph

Article in Parasites & vectors, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Maryam DiarraInstitutSénégalais de Recherches Agricoles, Laboratoire National de l'Elevage et de Recherches Vétérinaires, Dakar, Sénégal. myriem85@yahoo.fr.
Moussa FallInstitutSénégalais de Recherches Agricoles, Laboratoire National de l'Elevage et de Recherches Vétérinaires, Dakar, Sénégal.
Assane Gueye FallInstitutSénégalais de Recherches Agricoles, Laboratoire National de l'Elevage et de Recherches Vétérinaires, Dakar, Sénégal.
Aliou DiopUniversité Gaston Berger, Laboratoire d'Etudes et de Recherches en Statistiques et Développement, Saint-Louis, Sénégal.
Renaud LancelotCIRAD, ASTRE, Montpellier, France.
Momar Talla SeckInstitutSénégalais de Recherches Agricoles, Laboratoire National de l'Elevage et de Recherches Vétérinaires, Dakar, Sénégal.
Ignace RakotoarivonyCIRAD, ASTRE, Montpellier, France.
Xavier AllèneCIRAD, ASTRE, Montpellier, France.
Jérémy BouyerInstitutSénégalais de Recherches Agricoles, Laboratoire National de l'Elevage et de Recherches Vétérinaires, Dakar, Sénégal.
Hélène GuisCIRAD, ASTRE, Montpellier, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn Senegal, the last epidemic of African horse sickness (AHS) occurred in 2007. The western part of the country (the Niayes area) concentrates modern farms with exotic horses of high value and was highly affected during the 2007 outbreak that has started in the area. Several studies were initiated in the Niayes area in order to better characterize Culicoides diversity, ecology and the impact of environmental and climatic data on dynamics of proven and suspected vectors. The aims of this study are to better understand the spatial distribution and diversity of Culicoides in Senegal and to map their abundance throughout the country.

methodsCulicoides data were obtained through a nationwide trapping campaign organized in 2012. Two successive collection nights were carried out in 96 sites in 12 (of 14) regions of Senegal at the end of the rainy season (between September and October) using OVI (Onderstepoort Veterinary Institute) light traps. Three different modeling approaches were compared: the first consists in a spatial interpolation by ordinary kriging of Culicoides abundance data. The two others consist in analyzing the relation between Culicoides abundance and environmental and climatic data to model abundance and investigate the environmental suitability; and were carried out by implementing generalized linear models and random forest models.

resultsA total of 1,373,929 specimens of the genus Culicoides belonging to at least 32 different species were collected in 96 sites during the survey. According to the RF (random forest) models which provided better estimates of abundances than Generalized Linear Models (GLM) models, environmental and climatic variables that influence species abundance were identified. Culicoides imicola, C. enderleini and C. miombo were mostly driven by average rainfall and minimum and maximum normalized difference vegetation index. Abundance of C. oxystoma was mostly determined by average rainfall and day temperature. Culicoides bolitinos had a particular trend; the environmental and climatic variables above had a lesser impact on its abundance. RF model prediction maps for the first four species showed high abundance in southern Senegal and in the groundnut basin area, whereas C. bolitinos was present in southern Senegal, but in much lower abundance.

conclusionsEnvironmental and climatic variables of importance that influence the spatial distribution of species abundance were identified. It is now crucial to evaluate the vector competence of major species and then combine the vector densities with densities of horses to quantify the risk of transmission of AHS virus across the country.

Indexed as

African Horse SicknessAfrican Horse Sickness VirusAnimal DistributionAnimalsBluetongueBluetongue virusCeratopogonidaeEcosystemHorse DiseasesHorsesInsect VectorsModels, StatisticalSeasonsSenegalAfrican horse sicknessCulicoides vectorsEnvironmental and climatic dataGeneralized Linear ModelsMappingRandom forest modelsSenegalSpatial distribution

Identifiers

PMID29884209
PMCPMC5994048

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.