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

Abstract

Rift 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 ([Formula: see text]) 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.

Fig 1. Observed RVFV seroprevalence in small ruminants in sampling locations after the rainy season 2014 in Senegal.

(A) Spatial distribution; (B) Marginal distribution; (C) Distribution of smallest distances between sampling locations (primary source of the map: http://www.diva-gis.org/datadown).

Fig 2. Logit of RVFV seroprevalence (solid line)—and 95% pointwise confidence interval (dashed lines), in small ruminants after the rainy season 2014 in Senegal, according to a spline function of the number of dry spells during the rainy season 2014, and conditionally on the distance to the nearest surface water.

Fig 3. Logit of RVFV seroprevalence (solid line)—and 95% pointwise confidence interval (dashed lines), in small ruminants after the rainy season 2014 in Senegal, according to a spline function of its quantitative predictors.

Codes for predictors: `cattle`log of scaled cattle density at sampling locations; `lcd`least-cost distance between the sampling locations and the centroid of the nearest municipality connected with southern Mauritania via incoming ruminant trade; `logdrat`log ratio of small ruminant to cattle densities at sampling locations; `logTrav2`log of travel time needed to cross a one-km pixel at sampling locations; `minlst`minimum night land surface temperature recorded at sampling locations during the rainy season 2014; `nevents`number of dry spells at sampling locations during the rainy season 2014; `rfe`cumulative rainfall at sampling locations in 2014, in Senegal.

Fig 4. Posterior mean (circle) and 95% distribution interval (rod) of the predictor parameters for the DIC-best beta-binomial logistic regression model of RVFV seroprevalence in small ruminants after the 2013–2014 RVF outbreak in Senegal.

The dashed, red vertical line shows the null hypothesis for the statistical test of these parameters (α = 0.05): it was rejected when this line fell out of the 95% distribution interval. Labels for the parameters code: ‘rfe2’ cumulative rainfall, ‘nevents2’: number of dry spells during the rainy season 2014, ‘nevents2:ref2’ interaction between these two covariates, ‘Cattle’ cattle density, ‘logdrat’ log ratio of small ruminant to cattle densities, ‘lcd2’ distance to the nearest municipality centroïd linked with southern Mauritania via ruminant incoming trade, ‘logTrav2’ log of travel time need to cross a one-km pixel.

Fig 5

RVFV seroprevalence in small ruminants after the 2013–2014 RVF outbreak in Senegal (A) and its coefficient of variation (B) fitted by the DIC-best spatial beta-binomial logistic regression model (primary source of the map: http://www.diva-gis.org/datadown).