Inference in skew generalized t-link models for clustered binary outcome via a parameter-expanded EM algorithm.

Binary Generalized Linear Mixed Model (GLMM) is the most common method used by researchers to analyze clustered binary data in biological and social sciences. The traditional approach to GLMMs causes substantial bias in estimates due to steady shape of logistic and normal distribution assumptions thereby resulting into wrong and misleading decisions. This study brings forward an approach governed by skew generalized t distributions that belong to a class of potentially skewed and heavy tailed distributions.

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

Background: In 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.

Modelling hotspots of the two dominant Rift Valley fever vectors (Aedes vexans and Culex poicilipes) in Barkédji, Sénégal.

Background: Climatic and environmental variables were used successfully by using models to predict Rift Valley fever (RVF) virus outbreaks in East Africa. However, these models are not replicable in the West African context due to a likely difference of the dynamic of the virus emergence. For these reasons specific models mainly oriented to the risk mapping have been developed.

Modelling the Abundances of Two Major Culicoides (Diptera: Ceratopogonidae) Species in the Niayes Area of Senegal.

In Senegal, considerable mortality in the equine population and hence major economic losses were caused by the African horse sickness (AHS) epizootic in 2007. Culicoides oxystoma and Culicoides imicola, known or suspected of being vectors of bluetongue and AHS viruses are two predominant species in the vicinity of horses and are present all year-round in Niayes area, Senegal. The aim of this study was to better understand the environmental and climatic drivers of the dynamics of these two species.

Statistical modeling of the abundance of vectors of West African Rift Valley fever in Barkédji, Senegal.

Rift Valley fever is an emerging mosquito-borne disease that represents a threat to human and animal health. The exophilic and exophagic behavior of the two main vector in West Africa (Aedes vexans and Culex poicilipes), adverse events post-vaccination, and lack of treatment, render ineffective the disease control. Therefore it is essential to develop an information system that facilitates decision-making and the implementation of adaptation strategies.

Seasonal dynamics of Culicoides (Diptera: Ceratopogonidae) biting midges, potential vectors of African horse sickness and bluetongue viruses in the Niayes area of Senegal.

Background: The African horse sickness epizootic in Senegal in 2007 caused considerable mortality in the equine population and hence major economic losses. The vectors involved in the transmission of this arbovirus have never been studied specifically in Senegal. This first study of the spatial and temporal dynamics of the Culicoides (Diptera: Ceratopogonidae) species, potential vectors of African horse sickness in Senegal, was conducted at five sites (Mbao, Parc Hann, Niague, Pout and Thies) in the Niayes area, which was affected by the outbreak.

Predicting in-hospital maternal mortality in Senegal and Mali.

Objective: We sought to identify predictors of in-hospital maternal mortality among women attending referral hospitals in Mali and Senegal.

Methods: We conducted a cross-sectional epidemiological survey using data from a cluster randomized controlled trial (QUARITE trial) in 46 referral hospitals in Mali and Senegal, during the pre-intervention period of the trial (from October 1st 2007 to October 1st 2008). We included 89,518 women who delivered in the 46 hospitals during this period.

Impact of changing drug treatment and malaria endemicity on the heritability of malaria phenotypes in a longitudinal family-based cohort study.

Despite considerable success of genome wide association (GWA) studies in identifying causal variants for many human diseases, their success in unraveling the genetic basis to complex diseases has been more mitigated. Pathogen population structure may impact upon the infectious phenotype, especially with the intense short-term selective pressure that drug treatment exerts on pathogens. Rigorous analysis that accounts for repeated measures and disentangles the influence of genetic and environmental factors must be performed.

An exhaustive, non-euclidean, non-parametric data mining tool for unraveling the complexity of biological systems--novel insights into malaria.

Complex, high-dimensional data sets pose significant analytical challenges in the post-genomic era. Such data sets are not exclusive to genetic analyses and are also pertinent to epidemiology. There has been considerable effort to develop hypothesis-free data mining and machine learning methodologies.