Ex-ante benefit-cost analysis of the elimination of a Glossina palpalis gambiensis population in the Niayes of Senegal

Background: In 2005, the Government of Senegal embarked on a campaign to eliminate a Glossina palpalis gambiensis population from the Niayes area (,1000 km2) under the umbrella of the Pan African Tsetse and Trypanosomosis Eradication Campaign (PATTEC). The project was considered an ecologically sound approach to intensify cattle production. The elimination strategy includes a suppression phase using insecticide impregnated targets and cattle, and an elimination phase using the sterile insect technique, necessary to eliminate tsetse in this area. Methodology/Principal Findings: Three main cattle farming systems were identified: a traditional system using trypanotolerant cattle and two ''improved'' systems using more productive cattle breeds focusing on milk and meat production. In improved farming systems herd size was 45% lower and annual cattle sales were J250 (s.d. 513) per head as compared to J74 (s.d. 38) per head in traditional farming systems (p,1023). Tsetse distribution significantly impacted the occurrence of these farming systems (p = 0.001), with 34% (s.d. 4%) and 6% (s.d. 4%) of improved systems in the tsetse-free and tsetse-infested areas, respectively. We calculated the potential increases of cattle sales as a result of tsetse elimination considering two scenarios, i.e. a conservative scenario with a 2% annual replacement rate from traditional to improved systems after elimination, and a more realistic scenario with an increased replacement rate of 10% five years after elimination. The final annual increase of cattle sales was estimated at ,J2800/km2 for a total cost of the elimination campaign reaching ,J6400/km2. Conclusion/Significance: Despite its high cost, the benefit-cost analysis indicated that the project was highly cost-effective, with Internal Rates of Return (IRR) of 9.8% and 19.1% and payback periods of 18 and 13 years for the two scenarios, respectively. In addition to an increase in farmers' income, the benefits of tsetse elimination include a reduction of grazing pressure on the ecosystems. (Résumé d'auteur

Mapping livestock movements in Sahelian Africa

In the dominant livestock systems of Sahelian countries herds have to move across territories. Their mobility is often a source of conflict with farmers in the areas crossed, and helps spread diseases such as Rift Valley Fever. Knowledge of the routes followed by herds is therefore core to guiding the implementation of preventive and control measures for transboundary animal diseases, land use planning and conflict management. However, the lack of quantitative data on livestock movements, together with the high temporal and spatial variability of herd movements, has so far hampered the production of fine resolution maps of animal movements. This paper proposes a general framework for mapping potential paths for livestock movements and identifying areas of high animal passage potential for those movements. The method consists in combining the information contained in livestock mobility networks with landscape connectivity, based on different mobility conductance layers. We illustrate our approach with a livestock mobility network in Senegal and Mauritania in the 2014 dry and wet seasons

Assessing the Risk of Occurrence of Bluetongue in Senegal

International audienceBluetongue is a non-contagious viral disease affecting small ruminants and cattle that can cause severe economic losses in the livestock sector. The virus is transmitted by certain species of the genus Culicoides and consequently, understanding their distribution is essential to enable the identification of high-risk transmission areas. In this work we use bioclimatic and environmental variables to predict vector abundance, and estimate spatial variations in the basic reproductive ratio R0. The resulting estimates were combined with livestock mobility and serological data to assess the risk of Bluetongue outbreaks in Senegal. The results show an increasing abundance of C. imicola, C. oxystoma, C. enderleini, and C. miombo from north to south. R0 < 1 for most areas of Senegal, whilst southern (Casamance) and southeastern (Kedougou and part of Tambacounda) agro-pastoral areas have the highest risk of outbreak (R0 = 2.7 and 2.9, respectively). The next higher risk areas are in the Senegal River Valley (R0 = 1.07), and the Atlantic coast zones. Seroprevalence rates, shown by cELISA, weren’t positively correlated with outbreak probability. Future works should include follow-up studies of competent vector abundancies and serological surveys based on the results of the risk analysis conducted here to optimize the national epidemiological surveillance system

Using species distribution models to optimize vector control in the framework of the tsetse eradication campaign in Senegal

International audienceTsetse flies are vectors of human and animal trypanosomoses in sub-Saharan Africa and are the target of the Pan African Tsetse and Trypanosomiasis Eradication Campaign (PATTEC). Glossina palpalis gambiensis (Diptera: Glossinidae) is a riverine species that is still present as an isolated metapopulation in the Niayes area of Senegal. It is targeted by a national eradication campaign combining a population reduction phase based on insecticide-treated targets (ITTs) and cattle and an eradication phase based on the sterile insect technique. In this study, we used species distribution models to optimize control operations. We compared the probability of the presence of G. p. gambiensis and habitat suitability using a regularized logistic regression and Maxent, respectively. Both models performed well, with an area under the curve of 0.89 and 0.92, respectively. Only the Maxent model predicted an expert-based classification of landscapes correctly. Maxent predictions were therefore used throughout the eradication campaign in the Niayes to make control operations more efficient in terms of deployment of ITTs, release density of sterile males, and location of monitoring traps used to assess program progress. We discuss how the models' results informed about the particular ecology of tsetse in the target area. Maxent predictions allowed optimizing efficiency and cost within our project, and might be useful for other tsetse control campaigns in the framework of the PATTEC and, more generally, other vector or insect pest control programs

Identification of drivers of Rift Valley fever after the 2013–14 outbreak in Senegal using serological data in small ruminants

International audienceRift Valley fever (RVF) is a mosquito-borne disease mostly affecting wild and domestic ruminants. It is widespread in Africa, with spillovers in the Arab Peninsula and the southwestern Indian Ocean. Although RVF has been circulating in West Africa for more than 30 years, its epidemiology is still not clearly understood. In 2013, an RVF outbreak hit Senegal in new areas that weren’t ever affected before. To assess the extent of the spread of RVF virus, a national serological survey was implemented in young small ruminants (6–18 months old), between November 2014 and January 2015 (after the rainy season) in 139 villages. Additionally, the drivers of this spread were identified. For this purpose, we used a beta-binomial ( B B ) logistic regression model. An Integrated Nested Laplace Approximation (INLA) approach was used to fit the spatial model. Lower cumulative rainfall, and higher accessibility were both associated with a higher RVFV seroprevalence. The spatial patterns of fitted RVFV seroprevalence pointed densely populated areas of western Senegal as being at higher risk of RVFV infection in small ruminants than rural or southeastern areas. Thus, because slaughtering infected animals and processing their fresh meat is an important RVFV transmission route for humans, more human populations might have been exposed to RVFV during the 2013–2014 outbreak than in previous outbreaks in Senegal

Comparison of the total costs of the project and increase in global cattle sales.

<p>The figures (Euros) concern the Niayes area over a period of 30 years after the beginning of the project. Cattle sales include meat and milk sales. S1 corresponds to the scenario with a constant 2% annual replacement rate from traditional trypanotolerant farming systems to improved farming systems, and S2 to the scenario with an accelerated replacement rate according to the sociology of innovation (see text for details).</p

Environmental and economic determinants of temporal dynamics of the ruminant movement network of Senegal

Abstract Our understanding of the drivers of the temporal dynamics of livestock mobility networks is currently limited, despite their significant implications for the surveillance and control of infectious diseases. We analyzed the effect of time-varying environmental and economic variables—biomass production, rainfall, livestock market prices, and religious calendar on long-distance movements of cattle and small ruminant herds in Senegal in the years 2014 and 2019. We used principal component analysis to explore the variation of the hypothesized explanatory variables in space and time and a generalized additive modelling approach to assess the effect of those variables on the likelihood of herd movement between pairs of administrative units. Contrary to environmental variables, the patterns of variation of market prices show significant differences across locations. The explanatory variables at origin had the highest contribution to the model deviance reduction. Biomass production and rainfall were found to affect the likelihood of herd movement for both species on at least 1 year. Market price at origin had a strong and consistent effect on the departure of small ruminant herds. Our study shows the potential benefits of regular monitoring of market prices for future efforts at forecasting livestock movements and associated sanitary risks

Breakdown of core components costs of the tsetse elimination project in the Niayes area.

<p>Breakdown of core components costs of the tsetse elimination project in the Niayes area.</p

Distribution of the costs by partner (left) and component (right).

<p>Distribution of the costs by partner (left) and component (right).</p