Spatial Optimization Methods for Malaria Risk Mapping in Sub-Saharan African Cities Using Demographic and Health Surveys

Vector-borne diseases, such as malaria, are affected by the rapid urban growth and climate change in sub-Saharan Africa (SSA). In this context, intra-urban malaria risk maps act as a key decision-making tool for targeting malaria control interventions, especially in resource-limited settings. The Demographic and Health Surveys (DHS) provide a consistent malaria data source for mapping malaria risk at the national scale, but their use is limited at the intra-urban scale because survey cluster coordinates are randomly displaced for ethical reasons. In this research, we focus on predicting intra-urban malaria risk in SSA cities-Dakar, Dar es Salaam, Kampala and Ouagadougou-and investigate the use of spatial optimization methods to overcome the effect of DHS spatial displacement. We modeled malaria risk using a random forest regressor and remotely sensed covariates depicting the urban climate, the land cover and the land use, and we tested several spatial optimization approaches. The use of spatial optimization mitigated the effects of DHS spatial displacement on predictive performance. However, this comes at a higher computational cost, and the percentage of variance explained in our models remained low (around 30%-40%), which suggests that these methods cannot entirely overcome the limited quality of epidemiological data. Building on our results, we highlight potential adaptations to the DHS sampling strategy that would make them more reliable for predicting malaria risk at the intra-urban scale

The Multi-Satellite Environmental and Socioeconomic Predictors of Vector-Borne Diseases in African Cities:Malaria as an Example

Remote sensing has been used for decades to produce vector-borne disease risk maps aiming at better targeting control interventions. However, the coarse and climatic-driven nature of these maps largely hampered their use in the fight against malaria in highly heterogeneous African cities. Remote sensing now offers a large panel of data with the potential to greatly improve and refine malaria risk maps at the intra-urban scale. This research aims at testing the ability of different geospatial datasets exclusively derived from satellite sensors to predict malaria risk in two sub-Saharan African cities: Kampala (Uganda) and Dar es Salaam (Tanzania). Using random forest models, we predicted intra-urban malaria risk based on environmental and socioeconomic predictors using climatic, land cover and land use variables among others. The combination of these factors derived from different remote sensors showed the highest predictive power, particularly models including climatic, land cover and land use predictors. However, the predictive power remained quite low, which is suspected to be due to urban malaria complexity and malaria data limitations. While huge improvements have been made over the last decades in terms of remote sensing data acquisition and processing, the quantity and quality of epidemiological data are not yet sufficient to take full advantage of these improvements

Spatial Optimization Methods for Malaria Risk Mapping in Sub-Saharan African Cities Using Demographic and Health Surveys

Vector-borne diseases, such as malaria, are affected by the rapid urban growth and climate change in sub-Saharan Africa (SSA). In this context, intra-urban malaria risk maps act as a key decision-making tool for targeting malaria control interventions, especially in resource-limited settings. The Demographic and Health Surveys (DHS) provide a consistent malaria data source for mapping malaria risk at the national scale, but their use is limited at the intra-urban scale because survey cluster coordinates are randomly displaced for ethical reasons. In this research, we focus on predicting intra-urban malaria risk in SSA cities-Dakar, Dar es Salaam, Kampala and Ouagadougou-and investigate the use of spatial optimization methods to overcome the effect of DHS spatial displacement. We modeled malaria risk using a random forest regressor and remotely sensed covariates depicting the urban climate, the land cover and the land use, and we tested several spatial optimization approaches. The use of spatial optimization mitigated the effects of DHS spatial displacement on predictive performance. However, this comes at a higher computational cost, and the percentage of variance explained in our models remained low (around 30%-40%), which suggests that these methods cannot entirely overcome the limited quality of epidemiological data. Building on our results, we highlight potential adaptations to the DHS sampling strategy that would make them more reliable for predicting malaria risk at the intra-urban scale. Global climate change and rapid urbanization in sub-Saharan Africa (SSA) are likely to affect the epidemiology of vector-borne diseases such as malaria in urban and peri-urban areas. In this context, a better understanding of intra-urban malaria risk and its determinants has become even more urgent. Malaria risk has often been modeled at the national scale from Demographic and Health Surveys (DHS), which are periodically conducted in more than 90 developing countries. However, survey cluster coordinates in DHS are randomly displaced by up to 2 km in urban areas to protect respondent privacy, which reduces the accuracy of malaria models and risk maps at the intra-urban scale. In this study, we tested the potential of spatial optimization methods to overcome the effect of DHS displacement. We found that spatial optimization methods improved the performance of malaria models, but the improvement in performance is small for a higher computational cost. With these methods, we predicted malaria risk in several SSA cities (Dakar, Dar es Salaam, Kampala and Ouagadougou). We expect the quality and quantity of available data on malaria and other vector-borne diseases to improve in the future, which will certainly make these methods extremely useful in the fight against these diseases. We tested spatial optimization approaches to overcome the effect of cluster spatial displacement in Demographic and Health Surveys (DHS)Spatial optimization reduced the effect of displacement, but the percentage of variance explained in malaria models remained lowWe proposed potential adaptations to the DHS sampling strategy to better support the study of malaria risk at the intra-urban scale

Détermination de la bio-origine des sulfures de fer présents dans les systèmes de corrosion anoxiques par analyse de la composition isotopique du soufre en nanoSIMS

International audienceCharacterisation and determination of the bio-origin of iron sulphides in anoxiccorrosion systems thanks to isotopic analyses by nanoSIMS. This study is devoted to the determinationof the bacterial or inorganic origin of iron sulphides formed during anoxic corrosion processesby sulfur isotopic composition analyses. Broadly, these analyses are realized by IRMS on dissolved sulphidesand sulphates. Yet in this study, surface local isotopic analyses are realized by nanoSIMS in orderto preserve the information about the corrosion products’ localization. Two kinds of samples are studied:a sample (CBCC) corroded during 13 months in controlled laboratory conditions; and one archeologicalsample corroded during about 2000 years in not very well known conditions. When the corrosion system isperfectly well known, as for the CBCC sample, the local isotopic analyses by nanoSIMS actually enable toconclude on the (bio-)origin of the iron sulphides observed. However, when the isotopic composition of thesulphate’s source is unknown, as for the archeological sample, the origin of iron sulphides cannot alwaysbe determined.Cette étude est consacrée à la détermination de l'origine biotique ou abiotique de sulfures de fer formés au cours de processus de corrosion anoxiques par l'analyse de la composition isotopique du soufre. Usuellement ces analyses sont réalisées par IRMS (Isotopic Ratio Mass Spectrometry) sur des sulfures et des sulfates préalablement dissous. Dans cette étude, en revanche, une nouvelle approche est utilisée, basée sur des analyses isotopiques locales de surface réalisées en nanoSIMS afin de conserver l'information sur la localisation des produits de corrosion. Deux types d'échantillons ont été étudiés : un échantillon (CBCC) corrodé pendant 13 mois en conditions contrôlées, et un échantillon archéologique corrodé pendant environ 2000 ans dans des conditions mal connues. Lorsque le système de corrosion est parfaitement connu, tel que le cas de l'échantillon CBCC, l'analyse isotopique locale par nanoSIMS permet effectivement de conclure sur la (bio-)origine des liserés de sulfures de fer formés. En revanche, lorsque la composition isotopique de la source de sulfates est inconnue, comme dans le cas de l'échantillon archéologique, il n'est pas toujours possible de conclure sur l'origine des sulfures de fer observés

The Multi-Satellite Environmental and Socioeconomic Predictors of Vector-Borne Diseases in African Cities: Malaria as an Example

Remote sensing has been used for decades to produce vector-borne disease risk maps aiming at better targeting control interventions. However, the coarse and climatic-driven nature of these maps largely hampered their use in the fight against malaria in highly heterogeneous African cities. Remote sensing now offers a large panel of data with the potential to greatly improve and refine malaria risk maps at the intra-urban scale. This research aims at testing the ability of different geospatial datasets exclusively derived from satellite sensors to predict malaria risk in two sub-Saharan African cities: Kampala (Uganda) and Dar es Salaam (Tanzania). Using random forest models, we predicted intra-urban malaria risk based on environmental and socioeconomic predictors using climatic, land cover and land use variables among others. The combination of these factors derived from different remote sensors showed the highest predictive power, particularly models including climatic, land cover and land use predictors. However, the predictive power remained quite low, which is suspected to be due to urban malaria complexity and malaria data limitations. While huge improvements have been made over the last decades in terms of remote sensing data acquisition and processing, the quantity and quality of epidemiological data are not yet sufficient to take full advantage of these improvements.SCOPUS: no.jinfo:eu-repo/semantics/publishe

Ceftriaxone and cefotaxime have similar effects on the intestinal microbiota in human volunteers treated by standard-dose regimens

International audienceBackgroundCeftriaxone has a higher biliary elimination than cefotaxime (40% vs 10%), which may result in a more pronounced impact on the intestinal microbiota.MethodsWe performed a monocenter, randomized open-labelled clinical trial in 22 healthy volunteers treated by intravenous ceftriaxone (1g/24hrs) or cefotaxime (1g/8hrs) for 3 days (ClinicalTrials.gov NCT02659033). We collected fecal samples for phenotypic analyses, 16S rRNA gene profiling and measurement of antibiotic concentration, and compared between groups the evolution of microbial counts and indices of bacterial diversity over time. Plasma samples were drawn at day 3 for pharmacokinetic analysis.ResultsEmergence of 3rd generation cephalosporin resistant Gram-negative enteric bacilli (Enterobacterales), Enterococcus spp., or noncommensal microorganisms were not significantly different between groups. Both antibiotics reduced the counts of total Gram-negative enteric bacilli and decreased bacterial diversity, without significant difference between groups. All but one volunteer from each group exhibited undetectable levels of antibiotic in feces. Plasma pharmacokinetic endpoints were not correlated to alteration of bacterial diversity of the gut.ConclusionsBoth antibiotics markedly impact the intestinal microbiota, without any significant difference when standard clinical doses were administered for 3 days. This might be related to similar daily amounts of antibiotics excreted through the bile using a clinical regimen

Comprendre et travailler avec les hommes ayant des relations sexuelles avec d'autres hommes : pour une prévention combinée et une prise en charge globale des IST et du VIH

International audienc