A framework for efficient model transformations

The reported productivity gains while using models and model transformations to develop entire systems, after almost a decade of experience applying model-driven approaches for system development, are already undeniable benefits of this approach. However, the slowness of higher-level, rule based model transformation languages hinders the applicability of this approach to industrial scales. Lower-level, and efficient, languages can be used but productivity and easy maintenance seize to exist. The abstraction penalty problem is not new, it also exists for high-level, object oriented languages but everyone is using them now. Why is not everyone using rule based model transformation languages then? In this thesis, we propose a framework, comprised of a language and its respective environment, designed to tackle the most performance critical operation of high-level model transformation languages: the pattern matching. This framework shows that it is possible to mitigate the performance penalty while still using high-level model transformation languages.

Determinants of mother-baby relationship evaluated during pregnancy

ABSTRACT Background Mental health promotion is supported by a strong body of knowledge and is a matter of public health with the potential of a large impact on society. Mental health promotion programs should be implemented as soon as possible in life, preferably starting during pregnancy. Programs should focus on malleable determinants, introducing strategies to reduce risk factors or their impact on mother and child, and also on strengthening protective factors to increase resilience. The ambition of early detecting risk situations requires the development and use of tools to assess risk, and the creation of a responsive network of services based in primary health care, especially maternal consultation during pregnancy and the first months of the born child. The number of risk factors and the way they interact and are buffered by protective factors are relevant for the final impact. Maternal-fetal attachment (MFA) is not yet a totally understood and well operationalized concept. Methodological problems limit the comparison of data as many studies used small size samples, had an exploratory character or used different selection criteria and different measures. There is still a lack of studies in high risk populations evaluating the consequences of a weak MFA. Instead, the available studies are not very conclusive, but suggest that social support, anxiety and depression, self-esteem and self-control and sense of coherence are correlated with MFA. MFA is also correlated with health practices during pregnancy, that influence pregnancy and baby outcomes. MFA seems a relevant concept for the future mother baby interaction, but more studies are needed to clarify the concept and its operationalization. Attachment is a strong scientific concept with multiple implications for future child development, personality and relationship with others. Secure attachment is considered an essential basis of good mental health, and promoting mother-baby interaction offers an excellent opportunity to intervention programmes targeted at enhancing mental health and well-being. Understanding the process of attachment and intervening to improve attachment requires a comprehension of more proximal factors, but also a broader approach that assesses the impact of more distal social conditions on attachment and how this social impact is mediated by family functioning and mother-baby interaction. Finally, it is essential to understand how this knowledge could be translated in effective mental health promoting interventions and measures that could reach large populations of pregnant mothers and families. Strengthening emotional availability (EA) seems to be a relevant approach to improve the mother-baby relationship. In this review we have offered evidence suggesting a range of determinants of mother-infant relationship, including age, marital relationship, social disadvantages, migration, parental psychiatric disorders and the situations of abuse or neglect. Based on this theoretical background we constructed a theoretical model that included proximal and distal factors, risk and protective factors, including variables related to the mother, the father, their social support and mother baby interaction from early pregnancy until six months after birth. We selected the Antenatal Psychosocial Health Assessment (ALPHA) for use as an instrument to detect psychosocial risk during pregnancy. Method Ninety two pregnant women were recruited from the Maternal Health Consultation in Primary Health Care (PHC) at Amadora. They had three moments of assessment: at T1 (until 12 weeks of pregnancy) they filed out a questionnaire that included socio-demographic data, ALPHA, Edinburgh post-natal Depression Scale (EDPS), General Health Questionnaire (GHQ) and Sense of Coherence (SOC); at T2 (after the 20th weeks of pregnancy) they answered EDPS, SOC and MFA Scale (MFAS), and finally at T3 (6 months after birth), they repeated EDPS and SOC, and their interaction with their babies was videotaped and later evaluated using EA Scales. A statistical analysis has been done using descriptive statistics, correlation analysis, univariate logistic regression and multiple linear regression. Results The study has increased our knowledge on this particular population living in a multicultural, suburb community. It allow us to identify specific groups with a higher level of psychosocial risk, such as single or divorced women, young couples, mothers with a low level of education and those who are depressed or have a low SOC. The hypothesis that psychosocial risk is directly correlated with MFAS and that MFA is directly correlated with EA was not confirmed, neither the correlation between prenatal psychosocial risk and mother-baby EA. The study identified depression as a relevant risk factor in pregnancy and its higher prevalence in single or divorced women, immigrants and in those who have a higher global psychosocial risk. Depressed women have a poor MFA, and a lower structuring capacity and a higher hostility to their babies. In average, depression seems to reduce among pregnant women in the second part of their pregnancy. The children of immigrant mothers show a lower level of responsiveness to their mothers what could be transmitted through depression, as immigrant mothers have a higher risk of depression in the beginning of pregnancy and six months after birth. Young mothers have a low MFA and are more intrusive. Women who have a higher level of education are more sensitive and their babies showed to be more responsive. Women who are or have been submitted to abuse were found to have a higher level of MFA but their babies are less responsive to them. The study highlights the relevance of SOC as a potential protective factor while it is strongly and negatively related with a wide range of risk factors and mental health outcomes especially depression before, during and after pregnancy. Conclusions ALPHA proved to be a valid, feasible and reliable instrument to Primary Health Care (PHC) that can be used as a total sum score. We could not prove the association between psychosocial risk factors and MFA, neither between MFA and EA, or between psychosocial risk and EA. Depression and SOC seems to have a clear and opposite relevance on this process. Pregnancy can be considered as a maturational process and an opportunity to change, where adaptation processes occur, buffering risk, decreasing depression and increasing SOC. Further research is necessary to better understand interactions between variables and also to clarify a better operationalization of MFA. We recommend the use of ALPHA, SOC and EDPS in early pregnancy as a way of identifying more vulnerable women that will require additional interventions and support in order to decrease risk. At political level we recommend the reinforcement of Immigrant integration and the increment of education in women. We recommend more focus in health care and public health in mental health condition and psychosocial risk of specific groups at high risk. In PHC special attention should be paid to pregnant women who are single or divorced, very young, low educated and to immigrant mothers. This study provides the basis for an intervention programme for this population, that aims to reduce broad spectrum risk factors and to promote Mental Health in women who become pregnant. Health and mental health policies should facilitate the implementation of the suggested measures.

Probabilistic constraint reasoning with Monte Carlo integration to robot localization

This work studies the combination of safe and probabilistic reasoning through the hybridization of Monte Carlo integration techniques with continuous constraint programming. In continuous constraint programming there are variables ranging over continuous domains (represented as intervals) together with constraints over them (relations between variables) and the goal is to find values for those variables that satisfy all the constraints (consistent scenarios). Constraint programming “branch-and-prune” algorithms produce safe enclosures of all consistent scenarios. Special proposed algorithms for probabilistic constraint reasoning compute the probability of sets of consistent scenarios which imply the calculation of an integral over these sets (quadrature). In this work we propose to extend the “branch-and-prune” algorithms with Monte Carlo integration techniques to compute such probabilities. This approach can be useful in robotics for localization problems. Traditional approaches are based on probabilistic techniques that search the most likely scenario, which may not satisfy the model constraints. We show how to apply our approach in order to cope with this problem and provide functionality in real time.

A new MDA-SOA based framework for intercloud interoperability

Cloud computing has been one of the most important topics in Information Technology which aims to assure scalable and reliable on-demand services over the Internet. The expansion of the application scope of cloud services would require cooperation between clouds from different providers that have heterogeneous functionalities. This collaboration between different cloud vendors can provide better Quality of Services (QoS) at the lower price. However, current cloud systems have been developed without concerns of seamless cloud interconnection, and actually they do not support intercloud interoperability to enable collaboration between cloud service providers. Hence, the PhD work is motivated to address interoperability issue between cloud providers as a challenging research objective. This thesis proposes a new framework which supports inter-cloud interoperability in a heterogeneous computing resource cloud environment with the goal of dispatching the workload to the most effective clouds available at runtime. Analysing different methodologies that have been applied to resolve various problem scenarios related to interoperability lead us to exploit Model Driven Architecture (MDA) and Service Oriented Architecture (SOA) methods as appropriate approaches for our inter-cloud framework. Moreover, since distributing the operations in a cloud-based environment is a nondeterministic polynomial time (NP-complete) problem, a Genetic Algorithm (GA) based job scheduler proposed as a part of interoperability framework, offering workload migration with the best performance at the least cost. A new Agent Based Simulation (ABS) approach is proposed to model the inter-cloud environment with three types of agents: Cloud Subscriber agent, Cloud Provider agent, and Job agent. The ABS model is proposed to evaluate the proposed framework.

Towards an algorithmic skeleton framework for programming the Intel R Xeon PhiTM processor

The Intel R Xeon PhiTM is the first processor based on Intel’s MIC (Many Integrated Cores) architecture. It is a co-processor specially tailored for data-parallel computations, whose basic architectural design is similar to the ones of GPUs (Graphics Processing Units), leveraging the use of many integrated low computational cores to perform parallel computations. The main novelty of the MIC architecture, relatively to GPUs, is its compatibility with the Intel x86 architecture. This enables the use of many of the tools commonly available for the parallel programming of x86-based architectures, which may lead to a smaller learning curve. However, programming the Xeon Phi still entails aspects intrinsic to accelerator-based computing, in general, and to the MIC architecture, in particular. In this thesis we advocate the use of algorithmic skeletons for programming the Xeon Phi. Algorithmic skeletons abstract the complexity inherent to parallel programming, hiding details such as resource management, parallel decomposition, inter-execution flow communication, thus removing these concerns from the programmer’s mind. In this context, the goal of the thesis is to lay the foundations for the development of a simple but powerful and efficient skeleton framework for the programming of the Xeon Phi processor. For this purpose we build upon Marrow, an existing framework for the orchestration of OpenCLTM computations in multi-GPU and CPU environments. We extend Marrow to execute both OpenCL and C++ parallel computations on the Xeon Phi. We evaluate the newly developed framework, several well-known benchmarks, like Saxpy and N-Body, will be used to compare, not only its performance to the existing framework when executing on the co-processor, but also to assess the performance on the Xeon Phi versus a multi-GPU environment.

Creation of a web application for smart park system and parking prediction study for system integration

In the last few years, we have observed an exponential increasing of the information systems, and parking information is one more example of them. The needs of obtaining reliable and updated information of parking slots availability are very important in the goal of traffic reduction. Also parking slot prediction is a new topic that has already started to be applied. San Francisco in America and Santander in Spain are examples of such projects carried out to obtain this kind of information. The aim of this thesis is the study and evaluation of methodologies for parking slot prediction and the integration in a web application, where all kind of users will be able to know the current parking status and also future status according to parking model predictions. The source of the data is ancillary in this work but it needs to be understood anyway to understand the parking behaviour. Actually, there are many modelling techniques used for this purpose such as time series analysis, decision trees, neural networks and clustering. In this work, the author explains the best techniques at this work, analyzes the result and points out the advantages and disadvantages of each one. The model will learn the periodic and seasonal patterns of the parking status behaviour, and with this knowledge it can predict future status values given a date. The data used comes from the Smart Park Ontinyent and it is about parking occupancy status together with timestamps and it is stored in a database. After data acquisition, data analysis and pre-processing was needed for model implementations. The first test done was with the boosting ensemble classifier, employed over a set of decision trees, created with C5.0 algorithm from a set of training samples, to assign a prediction value to each object. In addition to the predictions, this work has got measurements error that indicates the reliability of the outcome predictions being correct. The second test was done using the function fitting seasonal exponential smoothing tbats model. Finally as the last test, it has been tried a model that is actually a combination of the previous two models, just to see the result of this combination. The results were quite good for all of them, having error averages of 6.2, 6.6 and 5.4 in vacancies predictions for the three models respectively. This means from a parking of 47 places a 10% average error in parking slot predictions. This result could be even better with longer data available. In order to make this kind of information visible and reachable from everyone having a device with internet connection, a web application was made for this purpose. Beside the data displaying, this application also offers different functions to improve the task of searching for parking. The new functions, apart from parking prediction, were: - Park distances from user location. It provides all the distances to user current location to the different parks in the city. - Geocoding. The service for matching a literal description or an address to a concrete location. - Geolocation. The service for positioning the user. - Parking list panel. This is not a service neither a function, is just a better visualization and better handling of the information.

Integration of a mobile autonomous robot in a surveillance multi-agent system

This dissertation aims to guarantee the integration of a mobile autonomous robot equipped with many sensors in a multi-agent distributed and georeferenced surveillance system. The integration of a mobile autonomous robot in this system leads to new features that will be available to clients of surveillance system may use. These features may be of two types: using the robot as an agent that will act in the environment or by using the robot as a mobile set of sensors. As an agent in the system, the robot can move to certain locations when alerts are received, in order to acknowledge the underlying events or take to action in order to assist in resolving this event. As a sensor platform in the system, it is possible to access information that is read from the sensors of the robot and access complementary measurements to the ones taken by other sensors in the multi-agent system. To integrate this mobile robot in an effective way it is necessary to extend the current multi-agent system architecture to make the connection between the two systems and to integrate the functionalities provided by the robot into the multi-agent system.

Methodological framework for detection of harmonisation breaking in service environments

As the complexity of markets and the dynamicity of systems evolve, the need for interoperable systems capable of strengthening enterprise communication effectiveness increases. This is particularly significant when it comes to collaborative enterprise networks, like manufacturing supply chains, where several companies work, communicate, and depend on each other, in order to achieve a specific goal. Once interoperability is achieved, that is once all network parties are able to communicate with and understand each other, organisations are able to exchange information along a stable environment that follows agreed laws. However, as markets adapt to new requirements and demands, an evolutionary behaviour is triggered giving space to interoperability problems, thus disrupting the sustainability of interoperability and raising the need to develop monitoring activities capable of detecting and preventing unexpected behaviour. This work seeks to contribute to the development of monitoring techniques for interoperable SOA-based enterprise networks. It focuses on the automatic detection of harmonisation breaking events during real-time communications, and strives to develop and propose a methodological approach to handle these disruptions with minimal or no human intervention, hence providing existing service-based networks with the ability to detect and promptly react to interoperability issues.

Assessment of integration of the leprosy program into primary health care in Aracaju, state of Sergipe, Brazil

INTRODUCTION: The aim of this study was to assess the epidemiological and operational characteristics of the Leprosy Program before and after its integration into the Primary healthcare Services of the municipality of Aracaju-Sergipe, Brazil. METHODS: Data were drawn from the national database. The study periods were divided into preintegration (1996-2000) and postintegration (2001-2007). Annual rates of epidemiological detection were calculated. Frequency data on clinico-epidemiological variables of cases detected and treated for the two periods were compared using the Chi-squared (χ2) test adopting a 5% level of significance. RESULTS: Rates of detection overall, and in subjects younger than 15 years, were greater for the postintegration period and were higher than rates recorded for Brazil as a whole during the same periods. A total of 780 and 1,469 cases were registered during the preintegration and postintegration periods, respectively. Observations for the postintegration period were as follows: I) a higher proportion of cases with disability grade assessed at diagnosis, with increase of 60.9% to 78.8% (p < 0.001), and at end of treatment, from 41.4% to 44.4% (p < 0.023); II) an increase in proportion of cases detected by contact examination, from 2.1% to 4.1% (p < 0.001); and III) a lower level of treatment default with a decrease from 5.64 to 3.35 (p < 0.008). Only 34% of cases registered from 2001 to 2007 were examined. CONCLUSIONS: The shift observed in rates of detection overall, and in subjects younger than 15 years, during the postintegration period indicate an increased level of health care access. The fall in number of patients abandoning treatment indicates greater adherence to treatment. However, previous shortcomings in key actions, pivotal to attaining the outcomes and impact envisaged for the program, persisted in the postintegration period.

Simplified wind turbine models for wind energy integration into power systems

This paper analyses the boundaries of simplified wind turbine models used to represent the behavior of wind turbines in order to conduct power system stability studies. Based on experimental measurements, the response of recent simplified (also known as generic) wind turbine models that are currently being developed by the International Standard IEC 61400-27 is compared to complex detailed models elaborated by wind turbine manufacturers. This International Standard, whose Technical Committee was convened in October 2009, is focused on defining generic simulation models for both wind turbines (Part 1) and wind farms (Part 2). The results of this work provide an improved understanding of the usability of generic models for conducting power system simulations.

Barriers preventing the successful integration of mental health services into Primary Health Care in Nigeria : a mixed methods approach

RESUMO: A Nigéria tem uma população estimada em cerca de 170 milhões de pessoas. O número de profissionais de saúde mental é muito diminuto, contando apenas com 150 psiquiatras o que perfaz aproximadamente um rácio de psiquiatra: população de mais de 1:1 milhão de pessoas. O Plano Nacional de Saúde Mental de 1991 reconheceu esta insuficiência e recomendou a integração dos serviços de saúde mental nos cuidados de saúde primários (CSP). Depois de mais de duas décadas, essa política não foi ainda implementada. Este estudo teve como objetivos mapear a estrutura organizacional dos serviços de saúde mental da Nigéria, e explorar os desafios e barreiras que impedem a integração bem-sucedida dos serviços de saúde mental nos cuidados de saúde primários, isto segundo a perspectiva dos profissionais dos cuidados de saúde primários. Com este objetivo, desenvolveu-se um estudo exploratório sequencial e utilizou-se um modelo misto para a recolha de dados. A aplicação em simultâneo de abordagens qualitativas e quantitativas permitiram compreender os problemas relacionados com a integração dos serviços de saúde mental nos CSP na Nigéria. No estudo qualitativo inicial, foram realizadas entrevistas com listagens abertas a 30 profissionais dos CSP, seguidas de dois grupos focais com profissionais dos CSP de duas zonas governamentais do estado de Oyo de forma a obter uma visão global das perspectivas destes profissionais locais sobre os desafios e barreiras que impedem uma integração bem-sucedida dos serviços de saúde mental nos CSP. Subsequentemente, foram realizadas entrevistas com quatro pessoas-chave, especificamente coordenadores e especialistas em saúde mental. Os resultados do estudo qualitativo foram utilizados para desenvolver um questionário para análise quantitativa das opiniões de uma amostra maior e mais representativa dos profissionais dos CSP do Estado de Oyo, bem como de duas zonas governamentais locais do Estado de Osun. As barreiras mais comummente identificadas a partir deste estudo incluem o estigma e os preconceitos sobre a doença mental, a formação inadequada dos profissionais dos CPS sobre saúde mental, a perceção pela equipa dos CSP de baixa prioridade de ação do Governo, o medo da agressão e violência pela equipa dos CSP, bem como a falta de disponibilidade de fármacos. As recomendações para superar estes desafios incluem a melhoria sustentada dos esforços da advocacia à saúde mental que vise uma maior valorização e apoio governamental, a formação e treino organizados dos profissionais dos cuidados primários, a criação de redes de referência e de apoio com instituições terciárias adjacentes, e o engajamento da comunidade para melhorar o acesso aos serviços e à reabilitação, pelas pessoas com doença mental. Estes resultados fornecem indicações úteis sobre a perceção das barreiras para a integração bem sucedida dos serviços de saúde mental nos CSP, enquanto se recomenda uma abordagem holística e abrangente. Esta informação pode orientar as futuras tentativas de implementação da integração dos serviços de saúde mental nos cuidados primários na Nigéria.------------ABSTRACT: Nigeria has an estimated population of about 170 million people but the number of mental health professionals is very small, with about 150 psychiatrists. This roughly translates to a psychiatrist:population ratio of more than 1:1 million people. The National Mental Health Policy of 1991 recognized this deficiency and recommended the integration of mental health into primary health care (PHC) delivery system. After more than two decades, this policy has yet to be implemented. This study aimed to map out the organizational structure of the mental health systems in Nigeria, and to explore the challenges and barriers preventing the successful integration of mental health into primary health care, from the perspective of the primary health care workers. A mixed methods exploratory sequential study design was employed, which entails the use of sequential timing in the combined methods of data collection. A combination of qualitative and uantitative approaches in sequence, were utilized to understand the problems of mental health services integration into PHC in Nigeria. The initial qualitative phase utilized free listing interviews with 30 PHC workers, followed by two focus group discussions with primary care workers from two Local Government Areas (LGA) of Oyo State to gain useful insight into the local perspectives of PHC workers about the challenges and barriers preventing successful integration of mental health care services into PHC. Subsequently, 4 key informant interviews with PHC co-ordinators and mental health experts were carried out. The findings from the qualitative study were utilized to develop a quantitative study questionnaire to understand the opinions of a larger and more representative sample of PHC staff in two more LGAs of Oyo State, as well as 2 LGAs from Osun State. The common barriers identified from this study include stigma and misconceptions about mental illness, inadequate training of PHC staff about mental health, low government priority, fear of aggression and violence by the PHC staff, as well as non-availability of medications. Recommendations for overcoming these challenges include improved and sustained efforts at mental health advocacy to gain governmental attention and support, organized training and retraining for primary care staff, establishment of referral and supportive networks with neighbouring tertiary facilities and community engagement to improve service utilization and rehabilitation of mentally ill persons. These findings provide useful insight into the barriers to the successful integration of mental health into PHC, while recommending a holistic and comprehensive approach. This information can guide future attempts to implement the integration of mental health into primary care in Nigeria.

DYNAMOD – A dynamic agent based modelling framework for digital businesses

Digital Businesses have become a major driver for economic growth and have seen an explosion of new startups. At the same time, it also includes mature enterprises that have become global giants in a relatively short period of time. Digital Businesses have unique characteristics that make the running and management of a Digital Business much different from traditional offline businesses. Digital businesses respond to online users who are highly interconnected and networked. This enables a rapid flow of word of mouth, at a pace far greater than ever envisioned when dealing with traditional products and services. The relatively low cost of incremental user addition has led to a variety of innovation in pricing of digital products, including various forms of free and freemium pricing models. This thesis explores the unique characteristics and complexities of Digital Businesses and its implications on the design of Digital Business Models and Revenue Models. The thesis proposes an Agent Based Modeling Framework that can be used to develop Simulation Models that simulate the complex dynamics of Digital Businesses and the user interactions between users of a digital product. Such Simulation models can be used for a variety of purposes such as simple forecasting, analysing the impact of market disturbances, analysing the impact of changes in pricing models and optimising the pricing for maximum revenue generation or a balance between growth in usage and revenue generation. These models can be developed for a mature enterprise with a large historical record of user growth rate as well as for early stage enterprises without much historical data. Through three case studies, the thesis demonstrates the applicability of the Framework and its potential applications.

Knowledge management framework based on brain models and human physiology

The life of humans and most living beings depend on sensation and perception for the best assessment of the surrounding world. Sensorial organs acquire a variety of stimuli that are interpreted and integrated in our brain for immediate use or stored in memory for later recall. Among the reasoning aspects, a person has to decide what to do with available information. Emotions are classifiers of collected information, assigning a personal meaning to objects, events and individuals, making part of our own identity. Emotions play a decisive role in cognitive processes as reasoning, decision and memory by assigning relevance to collected information. The access to pervasive computing devices, empowered by the ability to sense and perceive the world, provides new forms of acquiring and integrating information. But prior to data assessment on its usefulness, systems must capture and ensure that data is properly managed for diverse possible goals. Portable and wearable devices are now able to gather and store information, from the environment and from our body, using cloud based services and Internet connections. Systems limitations in handling sensorial data, compared with our sensorial capabilities constitute an identified problem. Another problem is the lack of interoperability between humans and devices, as they do not properly understand human’s emotional states and human needs. Addressing those problems is a motivation for the present research work. The mission hereby assumed is to include sensorial and physiological data into a Framework that will be able to manage collected data towards human cognitive functions, supported by a new data model. By learning from selected human functional and behavioural models and reasoning over collected data, the Framework aims at providing evaluation on a person’s emotional state, for empowering human centric applications, along with the capability of storing episodic information on a person’s life with physiologic indicators on emotional states to be used by new generation applications.

Integration of speech-processing technologies into Activobank's client interaction process

This dissertation analyzes the possibilities of utilizing speech-processing technologies to transform the user experience of ActivoBank’s customers while using remote banking solutions. The technologies are examined through different criteria to determine if they support the bank’s goals and strategy and whether they should be incorporated in the bank’s offering. These criteria include the alignment with ActivoBank’s values, the suitability of the technology providers, the benefits these technologies entail, potential risks, appeal to the customers and impact on customer satisfaction. The analysis suggests that ActivoBank might not be in a position to adopt these technologies at this point in time.

Analysis of the detection coefficient for the association between leprosy and pregnancy in the integration region of Carajas, State of Para, Brazil

Introduction The association between leprosy and pregnancy is currently poorly understood and has been linked to serious clinical consequences. Methods A retrospective study between 2007 and 2009 was performed in the integration region of Carajás, Brazil on a population of pregnant lepers, with non-lepers of ages 12-49 years serving as the reference population. Results Twenty-nine pregnant lepers were studied during the study period. The detection rates (DRs) for the studied association were 4.7 in 2007, 9.4 in 2008, and 4.3 in 2009. Conclusions The Carajás region presented a medium pattern of endemicity during most of the study period, with a high DR found in 2008.