Search

Background In malaria endemic countries, control interventions are performed during the high malaria transmission season using epidemiological surveillance data. One such intervention, seasonal chemoprevention (SMC), consists of the monthly administration of antimalarial drugs to children under 5 years. This study proposes an anticipating approach for adapting the timing of SMC interventions in Mali and the number of rounds. Our primary objective was to select the best approach for anticipating the onset of the high transmission season in the different health districts of Mali based on epidemiological surveillance and rainfall data. Our secondary objective was to evaluate the number of malaria cases, hospitalisations, and deaths in children under 5 years that could be prevented in Mali using the selected approach and the additional cost associated.Method Confirmed malaria cases and weekly rainfall data were collected for the 75 health districts of Mali for the 2014-2019 period. The onset of the rainy season, the onset of the high transmission season, the lag between these two events and the duration of the high transmission season were determined for each health district. Two approaches for anticipating the onset of the high transmission season in 2019 were evaluated. Results In 2014-2019, the onset of the rainy season ranged from W17 April to W34 August and that of the high transmission season from W25 June to W40 September. The lag between these two events ranged from 5 to 12 weeks. The duration of the high transmission season ranged from 3 to 6 months. The best approach anticipated the onset of the high transmission season 2019 in June in 2 districts, July in 46 districts, August in 21 districts and September in 6 districts. Using this approach over the 2014-2019 period would have led to changing the timing of SMC interventions in 36 health districts and would have prevented 43,819 cases, 1,943 hospitalisations and 70 deaths in children under 5 years. The additional cost of using our proposed approach is less than 5% of the current approach. Conclusion Adapting the timing of SMC interventions using our proposed approach would improve the prevention of malaria cases, hospitalisations, and deaths for a reasonable additional cost.

Artisanal and small‐scale gold mining (ASGM) represents a significant economic activity for communities in developing countries. In southeastern Senegal, this activity has increased in recent years and has become the main source of income for the local population. However, it is also associated with negative environmental, social, and health impacts. Considering the recent development of ASGM in Senegal and the difficulties of the government in monitoring and regulating this activity, this article proposes a method for detecting and mapping ASGM sites in Senegal using Sentinel 2 data and the Google Earth Engine. Two artisanal mining sites in Senegal are selected to test this approach. Detection and mapping are achieved following a processing pipeline. Principal component analysis (PCA) is applied to determine the optimal period of the year for mapping. Separability and threshold (SEaTH) is used to determine the optimal bands or spectral indices to discriminate ASGM from other land use. Finally, automatic classification and mapping of the scenes are achieved with support vector machine (SVM) classifier. The results are then validated based on field observations. The PCA and examination of spectral signatures as a function of time indicate that the best period for discriminate ASGM sites against other types of land use is the end of dry season, when vegetation is minimal. The classification results are presented as a map with different categories of land use. This method could be applied to future Sentinel scenes to monitor the evolution of mining sites and may also be extrapolated to other relevant areas in the Sahel., Key Points artisanal and small‐scale gold miningSentinel 2Google Earth Engine

Background: According to the World Health Organization, there were more than 228 million cases of malaria globally in 2018, with 93% of cases occurring in Africa, in Mali, a 13% increase in the number of cases was observed between 2015 and 2018, this study aimed to evaluate the impact of meteorological and environmental factors on the geo-epidemiology of malaria in the health district of Dire, Mali. Methods: Meteorological and environmental variables were synthesized using principal component analysis and multiple correspondence analysis, the relationship between malaria incidence and synthetic indicators was determined using a multivariate general additive model, hotspots were detected by SaTScan. Results: Malaria incidence showed high inter and intra-annual variability, the period of high transmission lasted from September to February, health areas characterized by proximity to the river, propensity for flooding and high agricultural yield were the most at risk, with an incidence rate ratio of 2.21 with confidence intervals (95% CI: 1.85&ndash, 2.58), malaria incidence in Dire declined from 120 to 20 cases per 10,000 person-weeks between 2013 and 2017. Conclusion: The identification of areas and periods of high transmission can help improve malaria control strategies.

In recent years there has been an increasing use of satellite Earth observation (EO) data in dengue research, in particular the identification of landscape factors affecting dengue transmission. Summarizing landscape factors and satellite EO data sources, and making the information public are helpful for guiding future research and improving health decision-making. In this case, a review of the literature would appear to be an appropriate tool. However, this is not an easy-to-use tool. The review process mainly includes defining the topic, searching, screening at both title/abstract and full-text levels and data extraction that needs consistent knowledge from experts and is time-consuming and labor intensive. In this context, this study integrates the review process, text scoring, active learning (AL) mechanism, and bidirectional long short-term memory (BiLSTM) networks, and proposes a semi-supervised text classification framework that enables the efficient and accurate selection of the relevant articles. Specifically, text scoring and BiLSTM-based active learning were used to replace the title/abstract screening and full-text screening, respectively, which greatly reduces the human workload. In this study, 101 relevant articles were selected from 4 bibliographic databases, and a catalogue of essential dengue landscape factors was identified and divided into four categories: land use (LU), land cover (LC), topography and continuous land surface features. Moreover, various satellite EO sensors and products used for identifying landscape factors were tabulated. Finally, possible future directions of applying satellite EO data in dengue research in terms of landscape patterns, satellite sensors and deep learning were proposed. The proposed semi-supervised text classification framework was successfully applied in research evidence synthesis that could be easily applied to other topics, particularly in an interdisciplinary context.

Climate change studies in recent decades have been based on Global Climate Models (GCMs), and the changes in the distribution of climatic regions over time extracted from these models can be represented using the Koppen climatic classification system, which predicts the global distribution of biomes based on monthly precipitation and average temperatures. In this study, the Koppen classification is used to evaluate the impacts of the melting of the Greenland and Antarctic Ice Sheets on GCM simulation results, on regional and global scales. To assess the impacts of accelerated ice sheet melting, an approach is utilized which is based on numerical simulations from the IPSL-CM5A-LR GCM; here, freshwater is introduced near the ice sheets and is superimposed on the RCP8.5 scenario. The changes in the distribution of the Koppen climatic regions under various scenarios (a historical run from observations, RCP8.5, and various examples of polar ice sheet melting) and comparisons between them reveal that major changes will occur on the global scale during the period 2041–2060. The analysis of group shifts within the Koppen classification system reveals that when freshwater from Greenland or Antarctica is introduced into the ocean, the inter-tropical belt undergoes greater change than it does under the RCP8.5 scenario. A focus on sub-group shifts within the Koppen classification system shows that changes in precipitation have major impacts on the climate in the Southern Hemisphere. Further, the changes are more drastic if the freshwater originates from Greenland than from Antarctica or from both locations. However, changes in temperature strongly impact the climate in the Northern Hemisphere and are significantly affected by the melting of the Greenland Ice Sheet. This study highlights the importance of considering ice sheet melting in the modeling of future global climate.

International audience; To date, there is no effective treatment to cure dengue fever, a mosquito-borne disease which has a major impact on human populations in tropical and subtropical regions. Although the characteristics of dengue infection are well known, factors associated with landscape are highly scale dependent in time and space, and therefore difficult to monitor. We propose here a mapping review based on 78 articles that study the relationships between landscape factors and urban dengue cases considering household, neighborhood and administrative levels. Landscape factors were retrieved from survey questionnaires, Geographic Information Systems (GIS), and remote sensing (RS) techniques. We structured these into groups composed of land cover, land use, and housing type and characteristics, as well as subgroups referring to construction material, urban typology, and infrastructure level. We mapped the co-occurrence networks associated with these Remote Sens. 2020, 12, 932 2 of 82 factors, and analyzed their relevance according to a three-valued interpretation (positive, negative, non significant). From a methodological perspective, coupling RS and GIS techniques with field surveys including entomological observations should be systematically considered, as none digital land use or land cover variables appears to be an univocal determinant of dengue occurrences. Remote sensing urban mapping is however of interest to provide a geographical frame to distribute human population and movement in relation to their activities in the city, and as spatialized input variables for epidemiological and entomological models.

Empirical studies of urban expansion have increased rapidly in recent decades worldwide. Previous studies mainly focused on cities in China, the United States or African countries, with Brazilian cities receiving less attention. Moreover, such studies are rare in purpose-built cities. Taking the urban expansion from scratch (1960) to urban agglomeration (2015) in the Federal District of Brazil (FDB) as an example, this study aims to quantify the magnitude, patterns, modes, types and efficiency of urban land expansion and attempts to reveal some implications within sustainable urban expansion thinking. Annual expansion, landscape metrics, local Moran’s I index, area weighted mean expansion index, and land-use efficiency were computed. The suitability of diffusion–coalescence theory and the impact of population growth and urban development policies on urban expansion were discussed. Urban land continuously expanded and became more fragmented during 1960–2015, which mainly occurred in SSW and WSW directions. Urban land evolved in a polycentric way. Edge expansion was identified as the stable contributor, and the importance of infilling and spontaneous growth alternated. Urban expansion in this region supported the diffusion–coalescence theory. Population growth promoted urban expansion, and the creation of peripheral urban nuclei and their development were associated with the urban expansion and the changes in urban land structure. This study adds new empirical evidence of urban expansion to Brazil urbanization, and compact urbanization, population control, and efficient urban land use should be considered in the future.

Em estudos sobre problemas de saúde, a obtenção de dados demográficos espacializados é essencial para a compreensão dos processos de transmissão e dispersão de doenças, bem como para o cálculo da taxa de incidência, que são fundamentais para as estratégias de controle e de prevenção. O desenvolvimento e a aplicação de métodos de estimativa populacional, em escala de detalhe, visando à obtenção do número da população, principalmente durante os anos intercensitários, podem contribuir para a análise da incidência de determinada doença. O objetivo do artigo é apresentar um método de estimativa da população, elaborado a partir de uma adaptação do método people in pixel (Cruz et al., 2007), podendo ser aplicado em unidades espaciais de diferentes resoluções. O método utiliza imagens de satélite e dados censitários. Aplicou-se a estimativa populacional anual das "zonas de saúde" (ZBS) do Distrito Federal - Brasil, entre 2007 e 2014, a fim de estudar a distribuição espacial das taxas de incidência de dengue ao longo do tempo. Este estudo permitiu identificar as áreas de alta transmissão de dengue e mostrar a variabilidade interanual dos padrões espaciais de transmissão da doença, o que pode contribuir para a definição de estratégias de controle da dengue no Distrito Federal. In health studies, obtaining spatialized demographic data is essential for understanding diseases transmission and dispersal processes, and for calculating the incidence rate as well, which are fundamental to control and prevention strategies. The development and application of population estimation methods, in a detailed scale, aiming to obtain population number, mainly during non-census years, may contribute to the analysis of the incidence of a particular disease. This paper aims to present a population estimation method, developed from an adaptation of the people in pixel method (Cruz et al., 2007), and can be applied in spatial units of different resolutions. The annual population estimation of the "health zones" (ZBS) of the Federal District of Brazil between 2007 and 2014 was applied in order to study the spatial distribution of dengue incidence rates over time. This study allowed to identify areas of high dengue transmission and show the interannual variability of spatial transmission patterns of the disease, which may contribute to the definition of dengue control strategies in the Federal District of Brazil. Dans les études épidémiologiques, l‘obtention de données démographiques spatialisées est essentielle pour la compréhension des processus de transmission et de dispersion des maladies, ainsi que pour le calcul du taux d’incidence, qui sont fondamentaux pour la définition des stratégies de contrôle et de prévention. Le développement et l'application de méthodes d'estimation de la population, pour les années inter-censitaires et/ou au sein d’unités spatiales, sont pertinentes et représentent un apport significatif dans le domaine de la santé publique. L’objectif de cet article est de présenter une méthode d’estimation de la population basée sur la méthode people in pixel (Cruz et al., 2007), qui peut être appliquée à des unités spatiales de différentes résolutions. Elle a été appliquée dans le District Fédéral du Brésil, pour l’estimation annuelle de la population des « zones de santé » entre 2007 et 2014, et ce, afin d’étudier la distribution spatiale des taux incidence de la dengue au cours du temps. Cette étude a permis d’identifier les zones à forte transmission de la dengue et de démontrer la variabilité inter-annuelle des patrons spatiaux de transmission de la maladie. Ces résultats contribuent ainsi à la définition de stratégie de contrôle de la dengue dans le District Fédéral.

That is a preprint of an article suubmitted at Applied Greography (20tf of March 19). Climate change studies in the last decades have been based on Global Climate Models (GCM), and the distribution of climatic regions over time extracted from these models can be represented using the Köppen climatic classification. The Köppen approach predicts the distribution of biomes worldwide on the basis of monthly precipitation and average temperature. This study aims to use the Köppen classification to evaluate the impact of the Greenland and Antarctic ice sheets melting on GCM simulation results at the regional and global scale. To assess the impact of accelerated ice-sheet melting, our approach is based on numerical simulations from the IPSL-CM5A-LR GCM with the introduction of freshwater near the ice sheets superimposed on the RCP8.5 scenario, leading to a global warming of +5°C. Static mapping of distribution changes in Köppen climatic regions under various scenarios (historical run from observations, RCP 8.5, and various cases of polar sheets melting) and comparisons between them reveal that major changes occur at the global scale for the period 2041-2060. A first level of analysis revealed that, when the input of freshwater originates from Greenland or Antarctica, the inter-tropical belt undergoes greater change than under the RCP8.5 scenario. A second level of analysis showed that changes in precipitation have major impacts on the southern hemisphere, with more drastic changes if the freshwater came from the Greenland ice sheet than if it came from Antarctica or from the combination of both. Changes in temperature, however, strongly impact the northern hemisphere, and are significantly affected by the melting of the Greenland ice sheet. This study highlights the importance of considering ice sheets melting in the modelling of future global climate.

Mediterranean coastal lagoons and their peripheral areas often provide a collection of habitats for many species, and they often face significant threats from anthropogenic activities. Diverse human activities in such areas directly affect the spatio-temporal dynamic of surface water and its ecological characteristics. Monitoring the surface water dynamic, and understanding the impact of human activities are of great significance for coastal lagoon conservation. The Regional Natural Park of Narbonne includes a typical Mediterranean lagoon complex where surface water dynamic and its potential link with local diverse human activities has not yet been studied. In this context, based on all the available Landsat images covering the study area during 2002–2016, this study identified the water and non-water classes for each satellite observation by comparing three widely used spectral indices (i.e., NDVI, NDWI and MNDWI) and using the Otsu method. The yearly water frequency index was then computed to present the spatio-temporal dynamic of surface water for each year, and three water dynamic scenarios were also identified for each year: permanent water (PW), non-permanent water (NPW) and non-water (NW). The spatial and inter-annual variation in the patterns of the three water scenarios were characterized by computing the landscape metrics at scenario-level quantifying area/edge, shape, aggregation and fragmentation. Finally, the quantitative link between different land use and land cover (LULC) types derived from the LULC maps of 2003, 2012 and 2015 and the surface water dynamic scenarios was established in each of the 300 m × 300 m grid cells covering the study area to determine the potential impact of human activities on the surface water dynamic. In terms of the inter-annual variation during 2002–2016, PW presented an overall stability, and NPW occupied only a small part of the water surface in each year and presented an inter-annual fluctuation. NPW had a smaller patch size, with lower connectivity degree and higher fragmentation degree. In terms of spatial variation during 2002–2016, NPW often occurred around PW, and its configurational features varied from place to place. Moreover, PW mostly corresponded to the natural lagoon, and salt marsh (as a part of lagoons), and NPW had a strong link with arable land (agricultural irrigation) and salt marsh (salt production), sand beach/dune, coastal wetlands and lagoon for the LULC maps of 2003, 2012 and 2015. However, more in-depth analysis is required for understanding the impact of sand beach/dune, coastal wetlands and lagoon on surface water dynamics. This study covers the long-term variations of surface water patterns in a Mediterranean lagoon complex having intense and diverse human activities, and the potential link between LULC types and the water dynamic scenarios was investigated on different dates. The results of the study should be useful for environmental management and protection of coastal lagoons.

PM2.5 severely affects human health. Remotely sensed (RS) data can be used to estimate PM2.5 concentrations and population exposure, and therefore to explain acute respiratory disorders. However, available global PM2.5 concentration forecast products derived from models assimilating RS data have not yet been exploited to generate early alerts for respiratory problems in Brazil. We investigated the feasibility of building such an early warning system. For this, PM2.5 concentrations on a 4-day horizon forecast were provided by the Copernicus Atmosphere Monitoring Service (CAMS) and compared with the number of severe acute respiratory disease (SARD) cases. Confounding effects of the meteorological conditions were considered by selecting the best linear regression models in terms of Akaike Information Criterion (AIC), with meteorological features and their two-way interactions as explanatory variables and PM2.5 concentrations and SARD cases, taken separately, as response variables. Pearson and Spearman correlation coefficients were then computed between the residuals of the models for PM2.5 concentration and SARD cases. The results show a clear tendency to positive correlations between PM2.5 and SARD in all regions of Brazil but the South one, with Spearman’s correlation coefficient reaching 0.52 (p < 0.01). Positive significant correlations were also found in the South region by previously correcting the effects of viral infections on the SARD case dynamics. The possibility of using CAMS global PM2.5 concentration forecast products to build an early warning system for pollution-related effects on human health in Brazil was therefore established. Further investigations should be performed to determine alert threshold(s) and possibly build combined risk indicators involving other risk factors for human respiratory diseases. This is of particular interest in Brazil, where the COVID-19 pandemic and biomass burning are occurring concomitantly, to help minimize the effects of PM emissions and implement mitigation actions within populations.

A distribuição dos mosquitos vetores da malária é controlada por vários fatores, como clima, tipos de cobertura do solo ou uso da terra, ou atividades humanas. Na região amazônica, persistem focos endêmicos de malária, particularmente na fronteira entre a Guiana Francesa e o Estado do Amapá no Brasil. Esta área, com mais de 300 km de extensão, apresenta muitas dificuldades na coleta de informações necessárias para a gestão da saúde das populações presentes. Neste tipo de contexto geográfico com poucos dados isolados e transfronteiriços, uma metodologia baseada na exploração de imagens de sensoriamento remoto é particularmente apropriada. Dados de sensoriamento remoto têm ampla cobertura espacial e uma grande variedade de resoluções espaciais. Aqui nós apresentamos uma abordagem combinando alta resolução e alta resolução espacial e dados de satélite óptico e RADAR. Nosso método possibilita mapear os diferentes tipos de cobertura do solo no contexto das fronteiras tropicais da Guiana Francesa - Brasil, caracterizado por um forte aproveitamento. Os resultados mostram que a complementaridade dos sensores ópticos e RADAR possibilita resolver, em grande parte, a questão da persistente nebulosidade. As classificações dos vários cobertura do solo obtidos permitem construir indicadores espacializados em relação aos espaços favoráveis à existência de criadouros e áreas de descanso do vetor da malária. Através de uma discussão sobre a aplicação de classificações da cobertura do solo compostas para a avaliação do risco de exposição à malária em várias escalas, este estudo de caso ilustra o interesse das abordagens de sensoriamento remoto por satélite para caracterizar os elementos da paisagem e a cobertura da terra, o que pode explicar a distribuição das populações de vetores em conexão com a transmissão de arbovírus. The distribution of mosquito vectors of malaria is controlled by various factors such as climate, land cover and land use, or human activities. In the Amazon region, endemic foci of malaria persist, particularly on the border between French Guiana and Amapa State in Brazil. This area, long by 300 km, presents many difficulties in collecting informations necessary for the health management of the local populations. In this type of geographical context, with few, isolated and cross-border data, a methodology based on the exploitation of remote sensing images is particularly appropriate. Remote sensing data have wide spatial coverage, and a large range of spatial resolutions. Here we present an approach combining high resolution and very high spatial resolution optical and SAR satellite data. Our method makes it possible to map the different types of land cover in the tropical border context French Guiana - Brazil, characterized by a persistent cloud cover. The results show that the complementarity of optical and RADAR sensors allows the production of multi-scale mapping of land use/land cover (LULC) independently from cloud cover. In particular, we produced LULC classifications to generate spatialized indicators and characterize the relationship between suitable habitats for malaria vectors and environmental factors. Through a discussion on the application of composite LULC classifications for the assessment of malaria exposure risk at various scales, this case study illustrates the interest of satellite remote sensing approaches to characterize landscape elements and land cover, which can account for the distribution of vector populations in connection with arboviruses transmission. La distribution des moustiques vecteurs du paludisme est contrôlée par divers facteurs comme le climat, les types d’occupation du sol, ou les activités humaines. Dans la région amazonienne, des foyers endémiques de paludisme subsistent, notamment à la frontière entre la Guyane française et l’état d’Amapa au Brésil. Cette zone, longue de plus de 300 km, présente de nombreuses difficultés dans la collecte des informations nécessaires à la gestion sanitaire des populations présentes. Dans ce type de contexte géographique bénéficiant de peu de données, isolé et transfrontalier, une méthodologie fondée sur l’exploitation d’images de télédétection est particulièrement adaptée. Les données de télédétection présentent une large couverture spatiale, et une gamme importante de résolutions spatiales. Nous présentons ici une approche combinant des données satellite optiques et RADAR à haute et très haute résolution spatiale. Notre méthode permet de cartographier les différents types d’occupation du sol dans le contexte tropical frontalier Guyane française – Brésil, caractérisé par un fort ennuagement. Les résultats montrent que la complémentarité des capteurs optiques et RADAR permet de résoudre en grande partie la question de l’ennuagement persistant. En particulier, les classifications des différentes occupations du sol obtenues permettent de construire des indicateurs spatialisés en lien avec les espaces favorables à l’existence de gîtes larvaires et de zones de repos du vecteur du paludisme. A travers une discussion sur l’application de ces classifications composites à l’estimation du risque d’exposition vectoriel considéré à différentes échelles, ce cas d’étude illustre l'intérêt d’approches de télédétection satellite pour caractériser des éléments du paysage et l’occupation des sols, susceptibles de rendre compte de la distribution des populations vectorielles en lien avec la transmission d’arbovirus.

En prenant de la hauteur et en offrant une vue élargie des territoires, les images satellite et les photographies aériennes permettent d’appréhender les facteurs environnementaux qui peuvent conditionner la santé des populations. Depuis l’essor de la télédétection satellite à partir des années 1970, son usage dans le domaine de la santé a connu des succès modérés. Si son potentiel a été largement évoqué dans la littérature jusqu’aux années 2000, son apport opérationnel ne s’est pas imposé. Ce n’est que récemment que les applications en santé se sont enrichies et multipliées par le développement d’approches interdisciplinaires. Cet article vise à questionner les usages de la télédétection appliquée aux questions de santé selon les aspects techniques, historiques, et thématiques afin d’identifier les verrous. Il propose tout d’abord une vue didactique des principes théoriques, des données disponibles et des usages possibles. A travers une étude bibliographique, il offre un regard rétrospectif sur l’évolution de sa mise en œuvre dans le domaine la santé, qui permet de synthétiser les contributions actuelles : pour la cartographie opérationnelle, pour la surveillance et l’analyse des dynamiques des maladies infectieuses ou, dans une moindre de mesure, pour appréhender les vulnérabilités des populations comme les difficultés d’accès aux soins. Enfin, l’article conclut sur les limites, les recommandations et les perspectives pour optimiser ces usages dont les verrous sont souvent techniques et structurels mais pour lesquels des méthodologies doivent encore être développées. Ao fornecer uma visão ampla dos territórios, as imagens de sensoriamento remoto orbital e fotografias aéreas permitem analisar os fatores ambientais que podem condicionar a saúde das populações. Desde o surgimento do sensoriamento remoto orbital na década de 1970, seu uso no campo da saúde teve um sucesso moderado. Embora seu potencial tenha sido amplamente mencionado na literatura até a década de 2000, sua contribuição operacional era óbvia. Apenas recentemente as aplicações em saúde foram enriquecidas e multiplicadas pelo desenvolvimento de abordagens interdisciplinares. Este artigo tem como objetivo questionar os usos do sensoriamento remoto aplicados às questões de saúde, de acordo com os aspectos técnicos, históricos e temáticos, a fim de identificar os seus gargalos. Primeiramente propõe uma visão didática dos princípios teóricos, dados disponíveis e possíveis usos. Através de um estudo bibliográfico, oferece uma retrospectiva da evolução de sua implementação no campo da saúde, permitindo sintetizar as contribuições atuais: para o mapeamento operacional, para a vigilância e a análise da dinâmica de doenças infecciosas ou, em menor grau, para compreender as vulnerabilidades das populações, tais como as dificuldades de acesso aos serviços de saúde. Por fim, o artigo conclui sobre os limites, as recomendações e as perspectivas para otimizar esses usos, que muitas vezes são limitados por questões técnicas e estruturais e para as quais as metodologias ainda precisam ser desenvolvidas. By providing a wide view of territories, satellite images and aerial photographs allow to capture the environmental factors that can affect the health of populations. Since the development of satellite remote sensing in the 1970s, its use in the health field has had moderate success. Although its potential was widely mentioned in the literature until the 2000s, its operational contribution was not obvious. It is only recently that health applications have been enriched and multiplied by the development of interdisciplinary approaches. This article aims to question the uses of remote sensing applied to health issues according to technical, historical, and thematic aspects in order to identify the bottlenecks. First, it offers a didactic view of theoretical principles, available data and possible uses. Through a bibliographic study, it offers a retrospective look at the evolution of its implementation in the field of health, which allows synthesizing current contributions: for operational mapping, for surveillance and analysis of the dynamics of infectious diseases or, to a lesser extent, to apprehend the vulnerabilities of populations such as difficulties in accessing healthcare. Finally, the article concludes on the limits, recommendations and perspectives for optimizing these uses, which are often constrained by technical and structural issues and for which methodologies still need to be developed.

International audience

International audience; Identifying and assessing the relative effects of the numerous determinants of malaria transmission, at different spatial scales and resolutions, is of primary importance in defining control strategies and reaching the goal of the elimination of malaria. In this context, based on a knowledge-based model, a normalized landscape-based hazard index (NLHI) was established at a local scale, using a 10 m spatial resolution forest vs. non-forest map, landscape metrics and a spatial moving window. Such an index evaluates the contribution of landscape to the probability of human-malaria vector encounters, and thus to malaria transmission risk. Since the knowledge-based model is tailored to the entire Amazon region, such an index might be generalized at large scales for establishing a regional view of the landscape contribution to malaria transmission. Thus, this study uses an open large-scale land use and land cover dataset (i.e., the 30 m TerraClass maps) and proposes an automatic data-processing chain for implementing NLHI at large-scale. First, the impact of coarser spatial resolution (i.e., 30 m) on NLHI values was studied. Second, the data-processing chain was established using R language for customizing the spatial moving window and computing the landscape metrics and NLHI at large scale. This paper presents the results in the State of Amapá, Brazil. It offers the possibility of monitoring a significant determinant of malaria transmission at regional scale.

International audience; The decadal variability of rainfall and vegetation over West Africa have been studied over the last three decades, 1981-1990, 1991-2000 and 2001-2010 denoted as 1980s, 1990s and 2000s, respectively. Climate Research Unit (CRU) monthly precipitation and Normalized Difference Vegetation Index (NDVI) from the National Oceanic and Atmosphere Administration (NOAA), all covering the period 1981-2010 have been used. This study aimed to assess the changes in the land surface condition and the spatio-temporal distribution of rainfall over West Africa region. The relationship between rainfall and vegetation indices over this region was determined using Pearson’s correlation. Also, the decadal comparison between rainfall and NDVI over the region was based on the significant t-test and the Pearson’s correlation. Results showed that significant return to wet conditions is observed between decade 1980s and decade 1990s over West Africa, and also during decade 2000s with the exception of central Benin and the western Nigeria. Meanwhile, a regreening of the central Sahel and Sudano-Sahel regions is noted. From 1990s to 2000s, this regreening belt is located in the South and the coastal areas: the Guinea Coast, Sudano-Guinea and western Sahel regions. A northward displacement of this re-greening belt is also detected. Thus, a linear relationship occurs between rainfall and NDVI in the Sudanian savannah region, but it is not the case in the rest of West Africa. This may suggest that the re-growth of vegetation in the Sudanian savannah region may be linked to rainfall supplies. Therefore, re-greening over Sahel region in 1990s is related to rainfall recovery. However, this re-greening was not sustained in the decade 2000s due to a slight decrease in rainfall.

The current Amazon landscape consists of heterogeneous mosaics formed by interactions between the original forest and productive activities. Recognizing and quantifying the characteristics of these landscapes is essential for understanding agricultural production chains, assessing the impact of policies, and in planning future actions. Our main objective was to construct the regionalization of agricultural production for Rondonia State (Brazilian Amazon) at the municipal level. We adopted a decision tree approach, using land use maps derived from remote sensing data (PRODES and TerraClass) combined with socioeconomic data. The decision trees allowed us to allocate municipalities to one of five agricultural production systems: (i) coexistence of livestock production and intensive agriculture; (ii) semi-intensive beef and milk production; (iii) semi-intensive beef production; (iv) intensive beef and milk production, and; (v) intensive beef production. These production systems are, respectively, linked to mechanized agriculture (i), traditional cattle farming with low management, with (ii) or without (iii) a significant presence of dairy farming, and to more intensive livestock farming with (iv) or without (v) a significant presence of dairy farming. The municipalities and associated production systems were then characterized using a wide variety of quantitative metrics grouped into four dimensions: (i) agricultural production; (ii) economics; (iii) territorial configuration, and; (iv) social characteristics. We found that production systems linked to mechanized agriculture predominate in the south of the state, while intensive farming is mainly found in the center of the state. Semi-intensive livestock farming is mainly located close to the southwest frontier and in the north of the state, where human occupation of the territory is not fully consolidated. This distributional pattern reflects the origins of the agricultural production system of Rondonia. Moreover, the characterization of the production systems provides insights into the pattern of occupation of the Amazon and the socioeconomic consequences of continuing agricultural expansion.

International audience; These authors contributed equally to this work. Abstract. In areas of seasonal malaria transmission, the incidence rate of malaria infection is presumed to be near zero at the end of the dry season. Asymptomatic individuals may constitute a major parasite reservoir during this time. We conducted a longitudinal analysis of the spatio-temporal distribution of clinical malaria and asymptomatic parasitemia over time in a Malian town to highlight these malaria transmission dynamics. For a cohort of 300 rural children followed over 2009–2014, periodicity and phase shift between malaria and rainfall were determined by spectral analysis. Spatial risk clusters of clinical episodes or carriage were identified. A nested-case-control study was conducted to assess the parasite carriage factors. Malaria infection persisted over the entire year with seasonal peaks. High transmission periods began 2–3 months after the rains began. A cluster with a low risk of clinical malaria in the town center persisted in high and low transmission periods. Throughout 2009–2014, cluster locations did not vary from year to year. Asymptomatic and gametocyte carriage were persistent, even during low transmission periods. For high transmission periods, the ratio of asymptomatic to clinical cases was approximately 0.5, but was five times higher during low transmission periods. Clinical episodes at previous high transmission periods were a protective factor for asymptomatic carriage, but carrying parasites without symptoms at a previous high transmission period was a risk factor for asymptomatic carriage. Stable malaria transmission was associated with sustained asymptomatic carriage during dry seasons. Control strategies should target persistent low-level parasitemia clusters to interrupt transmission.

Background Even if rainfall and temperature are factors classically associated to malaria, little is known about other meteorological factors, their variability and combinations related to malaria, in association with river height variations. Furthermore, in suburban area, urbanization and growing population density should be assessed in relation to these environmental factors. The aim of this study was to assess the impact of combined environmental, meteorological and hydrological factors on malaria incidence through time in the context of urbanization. Methods Population observational data were prospectively collected. Clinical malaria was defined as the presence of parasites in addition to clinical symptoms. Meteorological and hydrological factors were measured daily. For each factors variation indices were estimated. Urbanization was yearly estimated assessing satellite imaging and field investigations. Principal component analysis was used for dimension reduction and factors combination. Lags between malaria incidences and the main components were assessed by cross-correlation functions. Generalized additive model was used to assess relative impact of different environmental components, taking into account lags, and modelling non-linear relationships. Change-point analysis was used to determine transmission periods within years. Results Malaria incidences were dominated by annual periodicity and varied through time without modification of the dynamic, with no impact of the urbanization. The main meteorological factor associated with malaria was a combination of evaporation, humidity and rainfall, with a lag of 3 months. The relationship between combined temperature factors showed a linear impact until reaching high temperatures limiting malaria incidence, with a lag 3.25 months. Height and variation of the river were related to malaria incidence (respectively 6 week lag and no lag). Conclusions The study emphasizes no decreasing trend of malaria incidence despite accurate access to care and control strategies in accordance to international recommendations. Furthermore, no decreasing trend was showed despite the urbanization of the area. Malaria transmission remain increase 3 months after the beginning of the dry season. Addition to evaporation versus humidity/rainfall, nonlinear relationship for temperature and river height and variations have to be taken into account when implementing malaria control programmes.

International audience; Background : Many cities in developing countries experience an unplanned and rapid growth. Several studies have shown that the irregular urbanization and equipment of cities produce different health risks and uneven exposure to specific diseases. Consequently, health surveys within cities should be carried out at the micro-local scale and sampling methods should try to capture this urban diversity. Methods : This article describes the methodology used to develop a multi-stage sampling protocol to select a population for a demographic survey that investigates health disparities in the medium-sized city of Bobo-Dioulasso, Burkina Faso. It is based on the characterization of Bobo-Dioulasso city typology by taking into account the city heterogeneity, as determined by analysis of the built environment and of the distribution of urban infrastructures, such as healthcare structures or even water fountains, by photo-interpretation of aerial photographs and satellite images. Principal component analysis and hierarchical ascendant classification were then used to generate the city typology.Results : Five groups of spaces with specific profiles were identified according to a set of variables which could be considered as proxy indicators of health status. Within these five groups, four sub-spaces were randomly selected for the study. We were then able to survey 1045 households in all the selected sub-spaces. The pertinence of this approach is discussed regarding to classical sampling as random walk method for example. Conclusion : This urban space typology allowed to select a population living in areas representative of the uneven urbanization process, and to characterize its health status in regards to several indicators (nutritional status, communicable and non-communicable diseases, and anaemia). Although this method should be validated and compared with more established methods, it appears as an alternative in developing countries where geographic and population data are scarce.

La crue de 2012 a Niamey : un paroxysme du paradoxe du Sahel ? E n aout 2012, le fleuve Niger a connu une crue exceptionnelle dans le bassin du Niger moyen. Plusieurs episodes pluvieux parfois exceptionnels se sont succ ed e dans ce bassin et notamment sur le bassin-versant des affluents burkinab es. Elle a provoqu e de tr es graves inondations dans la r egion de Niamey. L'Autorit e du bassin du Niger (ABN) qui coordonne les activit es de suivi du fleuve, doit eclairer les d ecisions a prendre, tant au niveau r egional que dans les pays concern es. Le bilan humain est terrible puisque 81 personnes ont perdu la vie au Niger ; les d egâts mat eriels sont enormes. Cette crue fait

International audience; Local variation in the density of Anopheles mosquitoes and the risk of exposure to bites are essential to explain the spatial and temporal heterogeneities in the transmission of malaria. Vector distribution is driven by environmental factors. Based on variables derived from satellite imagery and meteorological observations, this study aimed to dynamically model and map the densities of Anopheles darlingi in the municipality of Saint-Georges de l’Oyapock (French Guiana). Longitudinal sampling sessions of An. darlingi densities were conducted between September 2012 and October 2014. Landscape and meteorological data were collected and processed to extract a panel of variables that were potentially related to An. darlingi ecology. Based on these data, a robust methodology was formed to estimate a statistical predictive model of the spatial-temporal variations in the densities of An. darlingi in Saint-Georges de l’Oyapock. The final cross-validated model integrated two landscape variables—dense forest surface and built surface—together with four meteorological variables related to rainfall, evapotranspiration, and the minimal and maximal temperatures. Extrapolation of the model allowed the generation of predictive weekly maps of An. darlingi densities at a resolution of 10-m. Our results supported the use of satellite imagery and meteorological data to predict malaria vector densities. Such fine-scale modeling approach might be a useful tool for health authorities to plan control strategies and social communication in a cost-effective, targeted, and timely manner.

International audience; Malaria remains a persistent health problem in the cross-border area between French Guiana and Brazil. Various factors like environmental changes, population movements, etc. significantly influence the malaria transmission. In such context, optical and SAR data were used for producing a precise land cover map, including the resting and breeding sites of malaria vectors and human habitats. This study is a foundation for quantifying the degree of exposure of human populations to malaria vectors and thus for estimating and monitoring the malaria transmission risk.

International audience; Soil typologies are characterized by different distribution and circulation of water, associated with specific water chemical properties, and thus potentially affects the distribution and density of malaria vectors in the Amazon. Based on a conceptual model of tropical soil evolution and distribution, the curvature of watershed slopes is a key indicator of soil typology. The average curvature of each subwatershed in our study area was identified using 30 m resolution SRTM data. PALSAR image with a spatial resolution of 12.5 m was used to implement a land cover map, including water and non-water surfaces. The non-water surface was then integrated with the average curvature of subwatersheds for soil typologies mapping. An indirect approach of soil typologies prediction was proposed, which is complementary with the classical soil classification methods.

International audience; Malaria remains one of the most common vector-borne diseases in the world and the definition of novel control strategies can benefit from the modeling of transmission processes. However, data-driven models are often difficult to build, as data are very often incomplete, heterogeneous in nature and in quality, and/or biased. In this context, a knowledge-based approach is proposed to build a robust and general landscape-based hazard index for malaria transmission that is tailored to the Amazonian region. A partial knowledge-based model of the risk of malaria transmission in the Amazonian region, based on landscape features and extracted from a systematic literature review, was used. Spatialization of the model was obtained by generating land use and land cover maps of the cross-border area between French Guiana and Brazil, followed by computing and combining landscape metrics to build a set of normalized landscape-based hazard indices. An empirical selection of the best index was performed by comparing the indices in terms of adequacy with the knowledge-based model, intelligibility and correlation with P. falciparum incidence rates. The selected index is easy to interpret and successfully represents the current knowledge about the role played by landscape patterns in malaria transmission within the study area. It was significantly associated with P. falciparum incidence rates, using the Pearson and Spearman correlation coefficients (up to 0.79 and 0.75, respectively; p-value < 0.001), and the linear regression coefficient of determination (reaching 0.63; p-values < 0.001). This study establishes a spatial knowledge-driven, landscape-based hazard malaria index using remote sensing that can be easily produced on a regular basis and might be useful for malaria prediction, surveillance, and control. Keywords: remote sensing; land use and land cover; landscape metric; knowledge-based hazard modeling; malaria; cross-border area between French Guiana and Brazil

International audience; Connaître la distribution des espèces vectrices de zoonoses est fondamentale pour comprendre les dynamiques épidémiologiques et caractériser les zones à risque. Cette distribution peut être appréhendée à différentes échelles : régionalement, l'aire de répartition de ces espèces définit les limites d'occurrence de la transmission d'agents pathogènes et, localement, leurs niches déterminent l'aléa. Cette dernière échelle requiert une connaissance très précise des milieux qui peut être acquise par l'analyse d'images satellite à très haute résolution spatiale. Dans le cadre d'un projet de recherche sur la leptospirose à La Réunion (LeptOI), cette étude propose de mesurer le potentiel d'utilisation d'une image Pléiades (produit rééchantillonné à 50 cm) pour caractériser la distribution locale de différents petits mammifères terrestres autour de l'Etang de Saint-Paul. Trois campagnes de piégeage ont permis la capture de 134 animaux appartenant à quatre des cinq espèces présentes à La Réunion (Rattus rattus, R. norvegicus, Suncus murinus et Tenrec ecaudatus). Une analyse orientée-objet de l'image Pléiades a permis de discriminer dix classes d'occupation des sols avec une précision totale de 83,6%. Des indices paysagers ont été calculés à partir de cette classification autour de chacun des pièges (distances les plus courtes aux classes d'occupation, densité de contours et pourcentages de surface des classes au sein de zones tampon de 50 et de 100 mètres). Trois zones (« humide », « anthropisée » et « sèche ») sont clairement discriminées par ces variables, qui pourront être utilisées comme déterminants écologiques de différents profils épidémiologiques : la zone humide étant propice à la leptospirose et la zone anthropisée étant un lieu de cooccurrence des quatre espèces et ainsi potentiellement une zone d'échanges de pathogènes. Abstract Knowing the distribution of vectors of zoonoses is fundamental to understand epidemiological dynamics and characterize risk areas. This distribution can be assessed at different scales: regionally, the range of these species defines the limits of occurrence of pathogen transmission and, locally, niches determine the hazard. This latter scale requires a very precise knowledge of the areas that can be gained through the analysis of very high spatial resolution satellite images. As part of a research project on leptospirosis in La Réunion (LeptOI), this study proposes to measure the potential of a Pleiades image (product resampled to to 50 cm) to characterize the local distribution of various small terrestrial mammals in Saint-Paul swamp area. Three trapping campaigns have led to the capture of 134 animals belonging to four of the five species occurring in Reunion Island (Rattus rattus, R. norvegicus, Suncus murinus and Tenrec ecaudatus). An object-based image analysis of the Pleiades image allowed discriminating ten land use classes with a total accuracy of 83.6%. Landscape indices were calculated from the classification around each trap (shortest distances to each land use class, edge densities and proportion of each patch within buffers defined by a 50 meters or a 100 meters radius). Three areas ("wet", "urbanized" and "dry") are clearly discriminated by these variables, which can be used as ecological determinants of different epidemiological profiles. The wetland area is known as suitable to leptospirosis whereas the urbanized area is a place of co-occurrence of the four species and thus potentially favorable to the exchange of pathogens.

Made available in DSpace on 2019-09-12T16:53:45Z (GMT). No. of bitstreams: 0 Previous issue date: 2004 This work presents results which help to understand the behaviour of the Normalized Difference Vegetation Index (NDVI) anomalies over the South America continent during the two strongest El Nino events of the last century (1982-1983 and 1997-1998). The data used are parts of a long-term series ( July 1981 to December 1999) of calibrated NDVI data derived from National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) datasets. Special emphasis has been given to the analysis of the response of the major Brazilian vegetation types. This paper introduces an approach that enhances NDVI anomalies relative to the long-term climatology of the region. We find a negative NDVI anomaly for most of the region during the 1982-1983 event, whereas for the 1997-1998 event positive NDVI anomalies were observed over most regions. Only the 'Nordeste' region showed a similar vegetation response for both events. We identify three possible factors that may play a role in the different NDVI responses to the two El Nino events. Firstly, poor intercalibration of sensors may account for some, but not all of the differences. Secondly the response of the vegetation may depend upon the climate conditions prior to the El Nino events. Thirdly, the difference in the onset date and the duration of the mature phase of the two El Nino events, associated with very different Atlantic surface temperatures are shown to have dynamical consequences which may impact upon the vegetation. ACA IAE CTA, Div Ciencias Atmosfer, Inst Aeronaut & Espaco, Ctr Tecn Aerosp, BR-12228904 Sao Jose Dos Campos, SP, Brazil; Inst Nacl Pesquisas Espaciais, BR-12201970 Sao Jose Dos Campos, Brazil; Universidade de Taubaté (Unitau), Dept Ciencias Agr; Inst Rech Dev, Lab Etude Tranferts Hydrol & Environm, F-38041 Grenoble 9, France; Agcy Nacl Energia Eletr, BR-70830030 Brasilia, DF, Brazil; Inst Rech Dev, BR-71619970 Brasilia, DF, Brazil; Inst Rech Dev, Lab Etude Tranferts Hydrol & Environm, Grenoble, France

International audience; BACKGROUND: Sampling design is a key issue when establishing species inventories and characterizing habitats within highly heterogeneous landscapes. Sampling efforts in such environments may be constrained and many field studies only rely on subjective and/or qualitative approaches to design collection strategy. The region of Cacao, in French Guiana, provides an excellent study site to understand the presence and abundance of Anopheles mosquitoes, their species dynamics and the transmission risk of malaria across various environments. We propose an objective methodology to define a stratified sampling design. Following thorough environmental characterization, a factorial analysis of mixed groups allows the data to be reduced and non-collinear principal components to be identified while balancing the influences of the different environmental factors. Such components defined new variables which could then be used in a robust k-means clustering procedure. Then, we identified five clusters that corresponded to our sampling strata and selected sampling sites in each stratum. RESULTS: We validated our method by comparing the species overlap of entomological collections from selected sites and the environmental similarities of the same sites. The Morisita index was significantly correlated (Pearson linear correlation) with environmental similarity based on i) the balanced environmental variable groups considered jointly (p = 0.001) and ii) land cover/use (p-value