Schistosomiasis risk mapping in the state of Minas Gerais, Brazil, using a decision tree approach, remote sensing data and sociological indicators

Schistosomiasis mansoni is not just a physical disease, but is related to social and behavioural factors as well. Snails of the Biomphalaria genus are an intermediate host for Schistosoma mansoni and infect humans through water. The objective of this study is to classify the risk of schistosomiasis in the state of Minas Gerais (MG). We focus on socioeconomic and demographic features, basic sanitation features, the presence of accumulated water bodies, dense vegetation in the summer and winter seasons and related terrain characteristics. We draw on the decision tree approach to infection risk modelling and mapping. The model robustness was properly verified. The main variables that were selected by the procedure included the terrain's water accumulation capacity, temperature extremes and the Human Development Index. In addition, the model was used to generate two maps, one that included risk classification for the entire of MG and another that included classification errors. The resulting map was 62.9% accurate.

Role of malnutrition and parasite infections in the spatial variation in children’s anaemia risk in northern Angola

Anaemia is known to have an impact on child development and mortality and is a severe public health problem in most countries in sub-Saharan Africa. We investigated the consistency between ecological and individual-level approaches to anaemia mapping by building spatial anaemia models for children aged ≤15 years using different modelling approaches. We aimed to (i) quantify the role of malnutrition, malaria, Schistosoma haematobium and soil-transmitted helminths (STHs) in anaemia endemicity; and (ii) develop a high resolution predictive risk map of anaemia for the municipality of Dande in northern Angola. We used parasitological survey data for children aged ≤15 years to build Bayesian geostatistical models of malaria (PfPR≤15), S. haematobium, Ascaris lumbricoides and Trichuris trichiura and predict small-scale spatial variations in these infections. Malnutrition, PfPR≤15, and S. haematobium infections were significantly associated with anaemia risk. An estimated 12.5%, 15.6% and 9.8% of anaemia cases could be averted by treating malnutrition, malaria and S. haematobium, respectively. Spatial clusters of high risk of anaemia (>86%) were identified. Using an individual-level approach to anaemia mapping at a small spatial scale, we found that anaemia in children aged ≤15 years is highly heterogeneous and that malnutrition and parasitic infections are important contributors to the spatial variation in anaemia risk. The results presented in this study can help inform the integration of the current provincial malaria control programme with ancillary micronutrient supplementation and control of neglected tropical diseases such as urogenital schistosomiasis and STH infections.

GIS capacity building for risk management to help developing countries:Case of climate change problem in Amhara rural region (Ethiopia)

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Advanced mapping of environmental data: Geostatistics, Machine Learning and Bayesian Maximum Entropy

This book combines geostatistics and global mapping systems to present an up-to-the-minute study of environmental data. Featuring numerous case studies, the reference covers model dependent (geostatistics) and data driven (machine learning algorithms) analysis techniques such as risk mapping, conditional stochastic simulations, descriptions of spatial uncertainty and variability, artificial neural networks (ANN) for spatial data, Bayesian maximum entropy (BME), and more.

On landslide risk, resilience and vulnerability of mountain communities in Central-Eastern Nepal

Executive SummaryIn Nepal, landslides are one of the major natural hazards after epidemics, killing over 100 persons per year. However, this figure is an underreported reflection of the actual impact that landslides have on livelihoods and food security in rural Nepal. With predictions of more intense rainfall patterns, landslide occurrence in the Himalayas is likely to increase and continue to be one of the major impediments to development. Due to the remoteness of many localities and lack of resources, responsibilities for disaster preparedness and response in mountain areas usually lie with the communities themselves. Everyday life is full of risk in mountains of Nepal. This is why mountain populations, as well as other populations living in harsh conditions have developed a number of coping strategies for dealing with adverse situations. Perhaps due to the dispersed and remote nature of landslides in Nepal, there have been few studies on vulnerability, coping- and mitigation strategies of landslide affected populations. There are also few recommendations available to guide authorities and populations how to reduce losses due to landslides in Nepal, and even less so, how to operationalize resilience and vulnerability.Many policy makers, international donors, NGOs and national authorities are currently asking what investments are needed to increase the so-called 'resilience' of mountain populations to deal with climate risks. However, mountain populations are already quite resilient to seasonal fluctuations, temperature variations, rainfall patterns and market prices. In spite of their resilience, they continue to live in places at risk due to high vulnerability caused by structural inequalities: access to land, resources, markets, education. This interdisciplinary thesis examines the concept of resilience by questioning its usefulness and validity as the current goal of international development and disaster risk reduction policies, its conceptual limitations and its possible scope of action. The goal of this study is two-fold: to better define and distinguish factors and relationships between resilience, vulnerability, capacities and risk; and to test and improve a participatory methodology for evaluating landslide risk that can serve as a guidance tool for improving community-based disaster risk reduction. The objective is to develop a simple methodology that can be used by NGOs, local authorities and communities to reduce losses from landslides.Through its six case studies in Central-Eastern Nepal, this study explores the relation between resilience, vulnerability and landslide risk based on interdisciplinary methods, including geological assessments of landslides, semi-structured interviews, focus groups and participatory risk mapping. For comparison, the study sites were chosen in Tehrathum, Sunsari and Dolakha Districts of Central/Eastern Nepal, to reflect a variety of landslide types, from chronic to acute, and a variety of communities, from very marginalized to very high status. The study uses the Sustainable Livelihoods Approach as its conceptual basis, which is based on the notion that access and rights to resources (natural, human/institutional, economic, environmental, physical) are the basis for coping with adversity, such as landslides. The study is also intended as a contribution to the growing literature and practices on Community Based Disaster Risk Reduction specifically adapted to landslide- prone areas.In addition to the six case studies, results include an indicator based methodology for assessing and measuring vulnerability and resilience, a composite risk assessment methodology, a typology of coping strategies and risk perceptions and a thorough analysis of the relation between risk, vulnerability and resilience. The methodology forassessing vulnerability, resilience and risk is relatively cost-effective and replicable in a low-data environment. Perhaps the major finding is that resilience is a process that defines a community's (or system's) capacity to rebound following adversity but it does not necessarily reduce vulnerability or risk, which requires addressing more structural issues related to poverty. Therefore, conclusions include a critical view of resilience as a main goal of international development and disaster risk reduction policies. It is a useful concept in the context of recovery after a disaster but it needs to be addressed in parallel with vulnerability and risk.This research was funded by an interdisciplinary grant (#26083591) from the Swiss National Science Foundation for the period 2009-2011 and a seed grant from the Faculty of Geosciences and Environment at the University of Lausanne in 2008.Résumé en françaisAu Népal, les glissements de terrain sont un des aléas les plus dévastateurs après les épidémies, causant 100 morts par an. Pourtant, ce chiffre est une sous-estimation de l'impact réel de l'effet des glissements sur les moyens de subsistance et la sécurité alimentaire au Népal. Avec des prévisions de pluies plus intenses, l'occurrence des glissements dans les Himalayas augmente et présente un obstacle au développement. Du fait de l'éloignement et du manque de ressources dans les montagnes au Népal, la responsabilité de la préparation et la réponse aux catastrophes se trouve chez les communautés elles-mêmes. Le risque fait partie de la vie quotidienne dans les montagnes du Népal. C'est pourquoi les populations montagnardes, comme d'autres populations vivant dans des milieux contraignants, ont développé des stratégies pour faire face aux situations défavorables. Peu d'études existent sur la vulnérabilité, ceci étant probablement dû à l'éloignement et pourtant, les stratégies d'adaptation et de mitigation des populations touchées par des glissements au Népal existent.Beaucoup de décideurs politiques, bailleurs de fonds, ONG et autorités nationales se demandent quels investissements sont nécessaires afin d'augmenter la 'resilience' des populations de montagne pour faire face aux changements climatiques. Pourtant, ces populations sont déjà résilientes aux fluctuations des saisons, des variations de température, des pluies et des prix des marchés. En dépit de leur résilience, ils continuent de vivre dans des endroits à fort risque à cause des vulnérabilités créées par les inégalités structurelles : l'accès à la terre, aux ressources, aux marchés et à l'éducation. Cette thèse interdisciplinaire examine le concept de la résilience en mettant en cause son utilité et sa validité en tant que but actuel des politiques internationales de développement et de réduction des risques, ainsi que ses limitations conceptuelles et ses possibles champs d'action. Le but de cette étude est double : mieux définir et distinguer les facteurs et relations entre la résilience, la vulnérabilité, les capacités et le risque ; Et tester et améliorer une méthode participative pour évaluer le risque des glissements qui peut servir en tant qu'outil indicatif pour améliorer la réduction des risques des communautés. Le but est de développer une méthodologie simple qui peut être utilisée par des ONG, autorités locales et communautés pour réduire les pertes dues aux glissements.A travers les études de cas au centre-est du Népal, cette étude explore le rapport entre la résilience, la vulnérabilité et les glissements basée sur des méthodes interdisciplinaires ; Y sont inclus des évaluations géologiques des glissements, des entretiens semi-dirigés, des discussions de groupes et des cartes de risques participatives. Pour la comparaison, les zones d'études ont été sélectionnées dans les districts de Tehrathum, Sunsari et Dolakha dans le centre-est du Népal, afin de refléter différents types de glissements, de chroniques à urgents, ainsi que différentes communautés, variant de très marginalisées à très haut statut. Pour son cadre conceptuel, cette étude s'appuie sur l'approche de moyens de subsistance durable, qui est basée sur les notions d'accès et de droit aux ressources (naturelles, humaines/institutionnelles, économiques, environnementales, physiques) et qui sont le minimum pour faire face à des situations difficiles, comme des glissements. Cette étude se veut aussi une contribution à la littérature et aux pratiques en croissantes sur la réduction des risques communautaires, spécifiquement adaptées aux zones affectées par des glissements.En plus des six études de cas, les résultats incluent une méthodologie basée sur des indicateurs pour évaluer et mesurer la vulnérabilité et la résilience, une méthodologie sur le risque composé, une typologie de stratégies d'adaptation et perceptions des risques ainsi qu'une analyse fondamentale de la relation entre risque, vulnérabilité et résilience. Les méthodologies pour l'évaluation de la vulnérabilité, de la résilience et du risque sont relativement peu coûteuses et reproductibles dans des endroits avec peu de données disponibles. Le résultat probablement le plus pertinent est que la résilience est un processus qui définit la capacité d'une communauté (ou d'un système) à rebondir suite à une situation défavorable, mais qui ne réduit pas forcement la vulnérabilité ou le risque, et qui requiert une approche plus fondamentale s'adressant aux questions de pauvreté. Les conclusions incluent une vue critique de la résilience comme but principal des politiques internationales de développement et de réduction des risques. C'est un concept utile dans le contexte de la récupération après une catastrophe mais il doit être pris en compte au même titre que la vulnérabilité et le risque.Cette recherche a été financée par un fonds interdisciplinaire (#26083591) du Fonds National Suisse pour la période 2009-2011 et un fonds de préparation de recherches par la Faculté des Géosciences et Environnement à l'Université de Lausanne en 2008.

Performance improvement in mediterranean operations using risk management analysis : Case study of Containerships Group Ltd

Emerging markets of Northern Africa and Turkey provide growth opportunities for logistics service companies in the middle of low growth environment of European Union. The purpose of this research is to explore and analyze the risk factors in container shipping industry and third party logistics (3PL) services. The research empirically examined the risk factors, which are related within the interaction between these two parties in emerging markets of Mediterranean area. The previous studies have provided a valuable insight into the operational risks faced by container shipping industries. However, most of these studies have focused on one or several operational risk factors from a single point of view, and no studies have inclusively examined the possible operational risks faced in the container shipping industry from dual perspective of 3PL provider and its customers. A questionnaire has been deployed to collect related data; and the impacts of the risks were then be assessed and ranked using the method of risk mapping. Respondents were located in Turkey, Algeria, Tunisia, and Libya. Research presents the most important risk factors identified, and compares them between 3PL provider and its customers. The research also provide some risk mitigation strategies for the key risk factors, and tried to figure out a common risk picture, which guides the managers in both sides to have a better decisions and as a result, improve the performance of the container shipping operations. Challenge during project execution time was that customers identified vast amount of more risks than what was the case with logistics service operator.

Dengue transmission risk maps of the continental United States

Dengue fever is a strictly human and non-human primate disease characterized by a high fever, thrombocytopenia, retro-orbital pain, and severe joint and muscle pain. Over 40% of the world population is at risk. Recent re-emergence of dengue outbreaks in Texas and Florida following the re-introduction of competent Aedes mosquito vectors in the United States have raised growing concerns about the potential for increased occurrences of dengue fever outbreaks throughout the southern United States. Current deficiencies in vector control, active surveillance and awareness among medical practitioners may contribute to a delay in recognizing and controlling a dengue virus outbreak. Previous studies have shown links between low-income census tracts, high population density, and dengue fever within the United States. Areas of low-income and high population density that correlate with the distribution of Aedes mosquitoes result in higher potential for outbreaks. In this retrospective ecologic study, nine maps were generated to model U.S. census tracts’ potential to sustain dengue virus transmission if the virus was introduced into the area. Variables in the model included presence of a competent vector in the county and census tract percent poverty and population density. Thirty states, 1,188 counties, and 34,705 census tracts were included in the analysis. Among counties with Aedes mosquito infestation, the census tracts were ranked high, medium, and low risk potential for sustained transmission of the virus. High risk census tracts were identified as areas having the vector, ≥20% poverty, and ≥500 persons per square mile. Census tracts with either ≥20% poverty or ≥500 persons per square mile and have the vector present are considered moderate risk. Census tracts that have the vector present but have <20% poverty and <500 persons per square mile are considered low risk. Furthermore, counties were characterized as moderate risk if 50% or more of the census tracts in that county were rated high or moderate risk, and high risk if 25% or greater were rated high risk. Extreme risk counties, which were primarily concentrated in Texas and Mississippi, were considered having 50% or greater of the census tracts ranked as high risk. Mapping of geographic areas with potential to sustain dengue virus transmission will support surveillance efforts and assist medical personnel in recognizing potential cases. ^

Gerenciamento participativo em saúde do trabalhador: uma experiência na atividade de controle de vetores

Este artigo apresenta a experiência de implantação de um sistema de gestão em Saúde do Trabalhador implantado na Superintendencia de Controle de Endemias (SUCEN), no período de 1998 a 2002, que operava na atividade de controle químico de vetores no Estado de São Paulo. OBJETIVO: Descrever o sistema de gestão participativa, as ações desenvolvidas e os principais resultados alcançados. MÉTODO: Relato da experiência vivenciada pela equipe usando abordagem qualitativa, análise de documentos e apresentação de dados quantitativos. RESULTADOS: Foram eleitas 11 Comissões de Saúde e Trabalho (COMSAT's) que em conjunto com a equipe técnica iniciaram a identificação dos riscos e de propostas para prevenção e controle dos riscos no trabalho. O mapeamento de riscos resultou em 650 recomendações, 45,7% das quais foram executadas. Foram identificadas como doenças relacionadas ao trabalho: reações alérgicas aos pesticidas, lesões por esforços repetitivos, distúrbios auditivos e patologias de coluna vertebral. Participaram dos cursos básicos de saúde do trabalhador 1.003 servidores (76,3% do total de servidores), sendo que 90,8% dos participantes os consideraram ótimos ou bons. CONCLUSÕES: O sistema de gerenciamento participativo coloca em prática os princípios de gestão democrática do Sistema Único de Saúde (SUS); incorpora, por meio do mapeamento de riscos, o saber do trabalhador; inclui os trabalhadores como sujeitos do processo de negociação e mudanças; pratica o direito à informação. As COMSAT's revelaram-se espaços adequados para a negociação das melhorias nas condições de trabalho. A aprovação do sistema de gestão culminou na validação legal por meio de um acordo tripartite assinado em março de 2002.

Advanced geostatistical and machine-learning models for spatial data analysis of radioactively contaminated regions

Radioactive soil-contamination mapping and risk assessment is a vital issue for decision makers. Traditional approaches for mapping the spatial concentration of radionuclides employ various regression-based models, which usually provide a single-value prediction realization accompanied (in some cases) by estimation error. Such approaches do not provide the capability for rigorous uncertainty quantification or probabilistic mapping. Machine learning is a recent and fast-developing approach based on learning patterns and information from data. Artificial neural networks for prediction mapping have been especially powerful in combination with spatial statistics. A data-driven approach provides the opportunity to integrate additional relevant information about spatial phenomena into a prediction model for more accurate spatial estimates and associated uncertainty. Machine-learning algorithms can also be used for a wider spectrum of problems than before: classification, probability density estimation, and so forth. Stochastic simulations are used to model spatial variability and uncertainty. Unlike regression models, they provide multiple realizations of a particular spatial pattern that allow uncertainty and risk quantification. This paper reviews the most recent methods of spatial data analysis, prediction, and risk mapping, based on machine learning and stochastic simulations in comparison with more traditional regression models. The radioactive fallout from the Chernobyl Nuclear Power Plant accident is used to illustrate the application of the models for prediction and classification problems. This fallout is a unique case study that provides the challenging task of analyzing huge amounts of data ('hard' direct measurements, as well as supplementary information and expert estimates) and solving particular decision-oriented problems.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.