Background: Many zoonotic infectious diseases have emerged and re-emerged over the last two decades. There has been a significant increase in vector-borne diseases due to climate variations that lead to environmental changes favoring the development and adaptation of vectors. This study was carried out to improve knowledge of the ecology of mosquito vectors involved in the transmission of Rift Valley fever virus (RVFV) in Senegal. Methods: An entomological survey was conducted in three Senegalese agro-systems, Senegal River Delta (SRD), Senegal River Valley (SRV) and Ferlo, during the rainy season (July to November) of 2014 and 2015. Mosquitoes were trapped using CDC light traps set at ten sites for two consecutive nights during each month of the rainy season, for a total of 200 night-traps. Ecological indices were calculated to characterize the different populations of RVFV mosquito vectors. Generalized linear models with mixed effects were used to assess the influence of climatic conditions on the abundance of RVFV mosquito vectors. Results: A total of 355,408 mosquitoes belonging to 7 genera and 35 species were captured in 200 night-traps. RVFV vectors represented 89.02% of the total, broken down as follows: Ae. vexans arabiensis (31.29%), Cx. poicilipes (0.6%), Cx. tritaeniorhynchus (33.09%) and Ma. uniformis (24.04%). Comparison of meteorological indices (rainfall, temperature, relative humidity), abundances and species diversity indicated that there were no significant differences between SRD and SRV (P = 0.36) while Ferlo showed significant differences with both (P < 0.001). Mosquito collection increased significantly with temperature for Ae. vexans arabiensis (P < 0.001), Cx. tritaeniorhynchus (P = 0.04) and Ma. uniformis (P = 0. 01), while Cx. poicilipes decreased (P = 0.003). Relative humidity was positively and significantly associated with the abundances of Ae. vexans arabiensis (P < 0.001), Cx. poicilipes (P = 0.01) and Cx. tritaeniorhynchus (P = 0.007). Rainfall had a positive and significant effect on the abundances of Ae. vexans arabiensis (P = 0.005). The type of biotope (temporary ponds, river or lake) around the trap points had a significant effect on the mosquito abundances (P < 0.001). Conclusions: In terms of species diversity, the SRD and SRV ecosystems are similar to each other and different from that of Ferlo. Meteorological indices and the type of biotope (river, lake or temporary pond) have significant effects on the abundance of RVFV mosquito vectors.
Mosquitoes are vectors of major pathogen agents worldwide. Population dynamics models are useful tools to understand and predict mosquito abundances in space and time. To be used as forecasting tools over large areas, such models could benefit from integrating remote sensing data that describe the meteorological and environmental conditions driving mosquito population dynamics. The main objective of this study is to assess a process-based modeling framework for mosquito population dynamics using satellite-derived meteorological estimates as input variables. A generic weather-driven model of mosquito population dynamics was applied to Rift Valley fever vector species in northern Senegal, with rainfall, temperature, and humidity as inputs. The model outputs using meteorological data from ground weather station vs satellite-based estimates are compared, using longitudinal mosquito trapping data for validation at local scale in three different ecosystems. Model predictions were consistent with field entomological data on adult abundance, with a better fit between predicted and observed abundances for the Sahelian Ferlo ecosystem, and for the models using in-situ weather data as input. Based on satellite-derived rainfall and temperature data, dynamic maps of three potential Rift Valley fever vector species were then produced at regional scale on a weekly basis. When direct weather measurements are sparse, these resulting maps should be used to support policy-makers in optimizing surveillance and control interventions of Rift Valley fever in Senegal.
Peste des Petits Ruminants (PPR) is a viral disease affecting domestic and small wild ruminants. Endemic in large parts of the world, PPR causes severe damages to animal production and household economies. In 2015, FAO and OIE launched a global eradication program (GCSE) based on vaccination campaigns. The success of GCSE shall depend on the implementation of vaccination campaigns, accounting for husbandry practices, mobility and the periodicity of small ruminants' population renewal. In Mauritania, PPR outbreaks occur annually despite ongoing annual vaccination campaigns since 2008. Here, we developed a mathematical model to assess the impact of four vaccination strategies (including the GSCE one), the importance of their timing of implementation and the usefulness of individual animal identification on the reduction of PPR burden. The model was calibrated on data collected through ad-hoc surveys about demographic dynamics, disease impact, and national seroprevalence using Monte Carlo Markov Chain procedure. Numerical simulations were used to estimate the number of averted deaths over the next 12 years. The model results showed that the GSCE strategy prevented the largest number of deaths (9.2 million vs. 6.2 for random strategy) and provided one of the highest economic returns among all strategies (Benefit-Cost Ratio around 16 vs. 7 for random strategy). According to its current cost, identification would be a viable investment that could reduce the number of vaccine doses to distribute by 20–60%. Whilst the implementation of the identification system is crucial for PPR control, its success depends also on a coordinated approach at the regional level.
BackgroundVector-borne diseases are among the leading causes of morbidity and mortality in humans and animals. In the Afrotropical region, some are transmitted by Culicoides, such as Akabane, bluetongue, epizootic haemorrhagic fever and African horse sickness viruses. Bluetongue virus infection has an enormous impact on ruminant production, due to its high morbidity and mortality rates.MethodsA nationwide Culicoides trapping campaign was organized at the end of the 2012 rainy season in Senegal. A Maximum Entropy approach (MaxEnt), Boosted Regression Tree (BRT) method and Ecological Niche Factor Analysis (ENFA) were used to develop a predictive spatial model for the distribution of Culicoides, using bio-climatic variables, livestock densities and altitude.ResultsThe altitude, maximum temperature of the warmest month, precipitation of the warmest quarter, mean temperature of the wettest quarter, temperature seasonality, precipitation of the wettest quarter and livestock density were among the most important factors to predict suitable habitats of Culicoides. Culicoides occurrences were, in most of the cases, positively correlated to precipitation variables and livestock densities; and negatively correlated to the altitude and temperature indices. The Niayes area and the Groundnut basin were the most suitable habitats predicted.ConclusionWe present ecological niche models for different Culicoides species, namely C. imicola, C. oxystoma, C. enderleini and C. miombo, potential vectors of bluetongue virus, on a nationwide scale in Senegal. Through our modelling approach, we were able to determine the effect of bioclimatic variables on Culicoides habitats and were able to generate maps for the occurrence of Culicoides species. This information will be helpful in developing risk maps for disease outbreaks.
We expose here a detailed spatially explicit model of aphid population dynamics at the scale of a whole country (Metropolitan France). It is based on convection-diffusion-reaction equations, driven by abiotic and biotic factors. The target species is the grain aphid, Sitobion avenae F., considering both its winged and apterous morphs. In this preliminary work, simulations for year 2004 (an outbreak case) produced realistic aphid densities, and showed that both spatial and temporal S. avenae population dynamics can be represented as an irregular wave of population peak densities from southwest to northeast of the country, driven by gradients or differences in temperature, wheat phenology, and wheat surfaces. This wave pattern fits well to our knowledge of S. avenae phenology. The effects of three insecticide spray regimes were simulated in five different sites and showed that insecticide sprays were ineffective in terms of yield increase after wheat flowering. After suitable validation, which will require some further years of observations, the model will be used to forecast aphid densities in real time at any date or growth stage of the crop anywhere in the country. It will be the backbone of a decision support system, forecasting yield losses at the level of a field. The model intends then to complete the punctual forecasting provided by older models by a comprehensive spatial view on a large area and leads to the diminution of insecticide sprayings in wheat crops.
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