Use of a Mixture Statistical Model in Studying Malaria Vectors Density

Abstract

Vector control is a major step in the process of malaria control and elimination. This requires vector counts and appropriate statistical analyses of these counts. However, vector counts are often overdispersed. A non-parametric mixture of Poisson model (NPMP) is proposed to allow for overdispersion and better describe vector distribution. Mosquito collections using the Human Landing Catches as well as collection of environmental and climatic data were carried out from January to December 2009 in 28 villages in Southern Benin. A NPMP regression model with “village” as random effect is used to test statistical correlations between malaria vectors density and environmental and climatic factors. Furthermore, the villages were ranked using the latent classes derived from the NPMP model. Based on this classification of the villages, the impacts of four vector control strategies implemented in the villages were compared. Vector counts were highly variable and overdispersed with important proportion of zeros (75%). The NPMP model had a good aptitude to predict the observed values and showed that: i) proximity to freshwater body, market gardening, and high levels of rain were associated with high vector density; ii) water conveyance, cattle breeding, vegetation index were associated with low vector density. The 28 villages could then be ranked according to the mean vector number as estimated by the random part of the model after adjustment on all covariates. The NPMP model made it possible to describe the distribution of the vector across the study area. The villages were ranked according to the mean vector density after taking into account the most important covariates. This study demonstrates the necessity and possibility of adapting methods of vector counting and sampling to each setting.

Spatio-temporal analysis of abundances of three malaria vector species in southern Benin using zero-truncated models

“…Counts of vectors are often characterized by an excess of zeros due to the absence of vectors at some locations or during some periods of time [ 7 ]. From a statistical point of view, these data can be considered as zero-inflated when the number of zeros is higher than that expected under the Poisson or negative binomial distribution assumption.…”

Modeling the seasonality of Anopheles gambiae s.s. biting rates in a South Benin sanitary zone

“…A previous study of the same area (Ouidah-Kpomassè -Tori, South Benin) was able to classify 28 villages according to mean vector biting rate intensity, given various environmental factors. 16 The present work presents another classification based on changes in vector biting rate profile around the year. The first classification was found to be linked to water conveyance, market gardening, and livestock breeding.…”

“…In the context of malaria, it allowed the classification of specific zones according to vector biting rate profiles. This and previous classifications according to mean biting rate intensity 16 are complementary indicators that may be targeted by healthcare organizations or epidemiological surveillance teams in regions where malaria is still a major public health problem. Conjointly, these indicators allow the mobilization of area-and season-specific malaria control measures.…”

“…Trans R Soc Trop Med Hyg 2014; 108: 237-243 doi:10.1093/trstmh/tru027 Advance Access publication 21 February 2014 A previous study 16 demonstrated spatial variability in mean biting rate intensity by major malaria vectors (i.e., An. coluzzii, An.…”

Data-driven and interpretable machine-learning modeling to explore the fine-scale environmental determinants of malaria vectors biting rates in rural Burkina Faso

“…From a statistical point of view, most algorithmic machine learning models, although nonparametric, hardly cope with zero-inflated negative binomial response variables (50,51), which are typically found in insect count data such as mosquitoes biting rates (52). An alternative approach to model such data is the hurdle model that considers the data responding to two processes: one causing zero versus non-zero and the second process explaining the non-zero counts (53).…”