This study estimated the number of infected children under the age of 5 years at different geographical scales in Senegal and produced the first parasitaemia risk map in the country using contemporary data collected under the nationally representative malaria survey of 2008/2009. Geostatistical Zero-Inflated Binomial models were developed and Bayesian variable selection methods for spatially correlated data were employed to build a predictive model and assess the effectiveness of the ITN intervention adjusting for climatic and socio-economic confounders.
A large number of zeros was observed when modeling the number of infected children under the age of 5 years, probably due to the fact that the survey was carried out at the beginning of the dry season, when transmission starts to decrease. To address the issue of sparsity a ZIB model was derived. Model validation revealed that the ZIB model had higher predictive ability than the Binomial analogue suggesting that, when a large number of zeros occurs in the data, a ZIB model should be considered. Since malaria research is focused on elimination and eradication of the disease, it is expected that forthcoming surveys will include a large number of locations with zero prevalence and the ZIB models would provide a suitable alternative to the standard Binomial ones for geostatistical modeling.
Geostatistical variable selection is an important topic in malaria mapping. The predictive ability of a model depends on the covariates included in the multivariate regression setting. Modeling approaches in malaria mapping treat selection of predictors separately than the geostatistical model fit. Variable selection is often based on regression models that ignore spatial correlation, leading to wrong estimates of covariates effects and their significance. Geostatistical variable selection not only identifies the best set of predictors but builds parsimonious models with the best predictive ability 
. In addition, it can be used to avoid overfitting due to the inclusion of unnecessary predictors or random effects. In this work, we have employed three Bayesian variable selection methods within a geostatistical model formulation. The climatic model with the highest posterior probability selected by the three methods included the following combination of covariates: night LST, NDVI and area type. Altitude in Senegal presents very little variation throughout the country therefore it was not considered as a potential predictor of malaria transmission in the variable selection procedure.
As mentioned above, maps showing the distribution of malaria risk in Senegal can be found in 
as part of efforts in mapping malaria risk at regional and continental level using historical data. Nevertheless, compilations of historical data obtained from surveys, heterogeneous in the age groups involved and the seasons considered, require methods for standardizing risk estimates into a common scale for mapping purposes. Different statistical methods have been employed; the work by Gemperli and colleagues 
, for instance, made use of the Garki transmission model to take into account the heterogeneity in the surveys. The model developed by Pull and Grab 
was instead employed by the MAP project 
, standardizing age-groups to produce a world map of Plasmodium falciparum malaria endemicity. The parasitaemia risk map presented in this paper, has been estimated from a contemporary survey and shows similar patterns to the one obtained from previous efforts 
, especially in the Southern and Eastern part of Senegal, at the border with Mali where the risk is higher. However, Gemperli et al. 
predicted a lower risk in the Central part of the country and higher in the urban areas of Dakar and Saint-Louis, as well as throughout the Sahelian region. In terms of absolute values, those results are uniformly higher than the current ones, due to the fact that the SMIS was carried out at the beginning of the low transmission season. The predicted pattern of malaria produced by the more recent work by Gosoniu et al. 
is more consistent with the map we generated, however the absolute values are still far from our estimates. The map of Senegal from the MAP project 
does not show any relevant variations or geographical differences in the intensity of malaria risk throughout the country. For logistic reasons the survey took place at the start of the dry season, thus projections from our model are likely to underestimate the burden during the highest transmission season.
Furthermore, the differences between observed and population adjusted risk estimates are mainly due to low prevalence observed in highly populated areas. The urban area of Dakar, for example, is the most populated one, and the majority of surveys were carried out in that area although the parasitaemia risk is very low.
Geostatistical variable selection enabled the assessment of the effect on parastaemia risk of different ITN coverage indicators after taking into account climatic factors and socio-economic disparities. Recent work by 
proposed a number of ITN coverage measures related to the ownership and use of nets at individual or household level. Five indicators have been assessed in the study and only one suggested a reduction in malaria risk with increasing coverage. This may explain the lack of relation between ITN coverage and malaria risk in similar analyses of MIS data. The Senegal data revealed that the presence of at least one ITN per every two household members reduced the odds of parasitaemia by 86%. In a recent analysis in Tanzania 
, ownership of at least one ITN was the only indicator assessed, showing no protective effect. On the other hand, the analysis of Zambia MIS 2005 
measured ITN coverage by the ownership of at least one bednet per household and found a preventive effect on malaria risk. Gosoniu et al. 
reported a reduction in risk for areas having at least 0.2 ITNs per person, a measure similar to the one presented in this paper. Different indicators of ITN coverage were considered in a spatial analysis of the Liberia MIS data 
, however none of them was associated with a reduction in the infection risk.
The model does not include some known risk factors for malaria such as maternal education, proximity to health services as this information was not readily available from the MIS data. It is however interesting to collect this data and include them in future MIS analyses aiming to assess ITN effects on parasitaemia.
This study found that the malaria risk in children less than five years old increases with age. Infants had the lowest risk. The risk rises especially after the age of two and levels off in older children. Similar results were observed in other low endemic settings.
All the results presented in the paper are based on the estimation of parasitaemia prevalence using the blood smear test. Malaria prevalence estimated using the RTDs was almost twice as high as the one based on the microscopy results. This confirms earlier findings suggesting that RDTs might present a large number of false positives when used in field conditions probably due to high temperatures during storage and transport as well as poor training on RDTs use.
In the model formulation, a linear relation between the parasitaemia odds and the environmental covariates was assumed. Geostatistical variable selection could be used to determine the best functional form that describes the above relation. Furthermore, a stationary geostatistical model was fitted assuming that spatial correlation depends only on the distance between locations irrespective of the locations themselves. This assumption may not be true when there are unobserved factors, such as health system performance, that vary across the country. The relation between climatic predictors and malaria may differ as well among the ecological zones.
Future control interventions can be planned and implemented by decision-makers according to the priority of the areas. A better resource allocation and health management can be achieved by monitoring the impact of prevention and control activities. The produced map and estimates generated in this study can be considered as baseline for comparisons with future national surveys to evaluate the effectiveness and progress of on-going intervention programmes as well as the changes of the parasitaemia risk over space and time.