Mapping of Schistosomiasis and Soil-Transmitted Helminthiasis in the Regions of Centre, East and West Cameroon

Schistosomiasis and soil-transmitted helminthiasis (STH) are a major public health problem in Cameroon. The national control strategy of these diseases was based on historical data collected 25 years ago, which might be outdated in some situations due to several factors including control activities, improved or degraded sanitation and hygiene, socio-economic improvement and disease transmission dynamics. To help planning, improving control strategies and evaluation of control activities, there was a need to update the distribution of schistosomiasis and STH. We conducted parasitological surveys in three regions of Cameroon, i.e. Centre, East and West. Our results showed a significant decrease of STH infection prevalence and intensities in all these three regions, in comparison to previous mapping data, with an overall decline of prevalence from 81.1–93% to 10.5–46.6%. These results show the positive impact of annual deworming campaigns, and illustrate the progressive success of the national programme for the control of schistosomiasis and STH in Cameroon. Furthermore, our results showed an increase of the number of high transmission foci of schistosomiasis, and allowed identifying new health districts requiring mass treatment with praziquantel, and those where deworming should be reinforced

Spectroscopic research on nitrous acid: the ultra-violet spectrum and its interpretation

editorial reviewe

Isomérie rotationnelle dans l'acide nitreux et les nitrites d'alkyle

peer reviewe

The infra-red spectrum and structure of nitrous anhydride

editorial reviewe

Spectroscopic research on nitrous acid: The infra-red spectrum of gaseous nitrous acid

editorial reviewe

Bayesian data fusion applied to water table spatial mapping

Water table elevations are usually sampled in space using piezometric measurements that are unfortunately expensive to obtain and are thus scarce over space. Most of the time, piezometric data are sparsely distributed over large areas, thus providing limited direct information about the level of the corresponding water table. As a consequence, there is a real need for approaches that are able at the same time to (1) provide spatial predictions at unsampled locations and (2) enable the user to account for all potentially available secondary information sources that are in some way related to water table elevations. In this paper, a recently developed Bayesian data fusion (BDF) framework is applied to the problem of water table spatial mapping. After a brief presentation of the underlying theory, specific assumptions are made and discussed to account for a digital elevation model and for the geometry of a corresponding river network. On the basis of a data set for the Dijle basin in the north part of Belgium, the suggested model is then implemented and results are compared to those of standard techniques such as ordinary kriging and cokriging. Respective accuracies and precisions of these estimators are finally evaluated using a ‘‘leave-one-out’’ cross-validation procedure. Although the BDF methodology was illustrated here for the integration of only two secondary information sources (namely, a digital elevation model and the geometry of a river network), the method can be applied for incorporating an arbitrary number of secondary information sources, thus opening new avenues for the important topic of data integration in a spatial mapping context

Optimal estuarine sediment monitoring network design with simulated annealing

An objective function based on geostatistical variance reduction, constrained to the reproduction of the probability distribution functions of selected physical and chemical sediment variables, is applied to the selection of the best set of compliance monitoring stations in the Sado river estuary in Portugal. These stations were to be selected from a large set of sampling stations from a prior field campaign. Simulated annealing was chosen to solve the optimisation function model. Both the combinatorial problem structure and the resulting candidate sediment monitoring networks are discussed, and the optimal dimension and spatial distribution are proposed. An optimal network of sixty stations was obtained from an original 153-station sampling campaign.http://www.sciencedirect.com/science/article/B6WJ7-4GX6J7S-8/1/a856112ea58d8f9d6bf0b51627b5e2a