CDR-based location analytics & gender prediction from subscribers’ list of installed mobile applications

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsBig Data is big news in most industries, and telecommunication is no exception. Over the last decades, telecom operators experienced numerous changes in their business models, driven by technological innovations. Although, telecom operators have long had access to substantial bits of data, the scenario has radically evolved with the advent of smartphones, mobile broadband, rapid development of internet, growth of mobile services and Big Data Analytics capabilities (BDA). In today’s data intensive world of communications, tremendous amount of diverse type of data are generated by telecom, bringing both challenges and opportunities to the table. This present internship report summarises my contribution part of the Big Data & Advanced Analytics team of Vodafone Portugal with two research projects; The first one consisted in studying human mobility from cellular network-based data, considering the so-called Call Detail Records (CDR) as a core proxy to extract spatiotemporal density distribution at finer geospatial granularity levels. The second consisted in conducting an observational study of the predictability of mobile subscribers’ demographic traits from their installed mobile applications. The latter has the use-case of predicting the gender of mobile subscribers. Both research projects draw attention to the particular ubiquity aspect of connected mobile devices, being widely available and used all over the world.A área de Big Data é uma grande novidade para a maioria das empresas, incluindo as companhias de telecomunicação. Durante as últimas décadas, e graças às inovações tecnológicas, os operadores de telecomunicações viveram muitas mudanças nos seus modelos de atividade comercial. Embora as empresas de telecomunicação já tinham acesso a uma quantidade considerável de dados (bits), o cenário mudou por completo com a chegada dos smartphones, a banda larga, o rápido desenvolvimento de internet, um grande crescimento dos serviços móveis e o Big Data Analytics Capabilities (BDCA). A frenética realidade atual do mundo das comunicações, cria uma grande e diversa quantidade de dados, gerada pelas empresas de telefonia, supondo ao mesmo tempo novos desafios e oportunidades. No seguinte relatório de estágio, resume-se a minha contribuição à equipa de Big Data e Advanced Analytics de Vodafone com dois projetos de investigação: O primeiro projeto consistiu em estudar a mobilidade dos humanos baseando-se nos dados extraídos da rede móvel, considerando o chamado Call Detail Records (CDR) como principal variável para poder obter informação mais detalhada sobre a densidade espácio-temporal em níveis de granularidade. O segundo projeto é um estudo observacional sobre a previsibilidade das características demográficas dos utentes tendo em conta as aplicações instaladas nos seus telemóveis. O caso prático deste último pretende predizer o género dos clientes da rede móvel. Estes dois projetos de investigação pretendem chamar a atenção para a posição onipresente que ocupam os dispositivos móveis ligados à rede na nossa sociedade, estando disponíveis e sendo utilizados no mundo inteiro

A Data-driven Methodology Towards Mobility- and Traffic-related Big Spatiotemporal Data Frameworks

Human population is increasing at unprecedented rates, particularly in urban areas. This increase, along with the rise of a more economically empowered middle class, brings new and complex challenges to the mobility of people within urban areas. To tackle such challenges, transportation and mobility authorities and operators are trying to adopt innovative Big Data-driven Mobility- and Traffic-related solutions. Such solutions will help decision-making processes that aim to ease the load on an already overloaded transport infrastructure. The information collected from day-to-day mobility and traffic can help to mitigate some of such mobility challenges in urban areas. Road infrastructure and traffic management operators (RITMOs) face several limitations to effectively extract value from the exponentially growing volumes of mobility- and traffic-related Big Spatiotemporal Data (MobiTrafficBD) that are being acquired and gathered. Research about the topics of Big Data, Spatiotemporal Data and specially MobiTrafficBD is scattered, and existing literature does not offer a concrete, common methodological approach to setup, configure, deploy and use a complete Big Data-based framework to manage the lifecycle of mobility-related spatiotemporal data, mainly focused on geo-referenced time series (GRTS) and spatiotemporal events (ST Events), extract value from it and support decision-making processes of RITMOs. This doctoral thesis proposes a data-driven, prescriptive methodological approach towards the design, development and deployment of MobiTrafficBD Frameworks focused on GRTS and ST Events. Besides a thorough literature review on Spatiotemporal Data, Big Data and the merging of these two fields through MobiTraffiBD, the methodological approach comprises a set of general characteristics, technical requirements, logical components, data flows and technological infrastructure models, as well as guidelines and best practices that aim to guide researchers, practitioners and stakeholders, such as RITMOs, throughout the design, development and deployment phases of any MobiTrafficBD Framework. This work is intended to be a supporting methodological guide, based on widely used Reference Architectures and guidelines for Big Data, but enriched with inherent characteristics and concerns brought about by Big Spatiotemporal Data, such as in the case of GRTS and ST Events. The proposed methodology was evaluated and demonstrated in various real-world use cases that deployed MobiTrafficBD-based Data Management, Processing, Analytics and Visualisation methods, tools and technologies, under the umbrella of several research projects funded by the European Commission and the Portuguese Government.A população humana cresce a um ritmo sem precedentes, particularmente nas áreas urbanas. Este aumento, aliado ao robustecimento de uma classe média com maior poder económico, introduzem novos e complexos desafios na mobilidade de pessoas em áreas urbanas. Para abordar estes desafios, autoridades e operadores de transportes e mobilidade estão a adotar soluções inovadoras no domínio dos sistemas de Dados em Larga Escala nos domínios da Mobilidade e Tráfego. Estas soluções irão apoiar os processos de decisão com o intuito de libertar uma infraestrutura de estradas e transportes já sobrecarregada. A informação colecionada da mobilidade diária e da utilização da infraestrutura de estradas pode ajudar na mitigação de alguns dos desafios da mobilidade urbana. Os operadores de gestão de trânsito e de infraestruturas de estradas (em inglês, road infrastructure and traffic management operators — RITMOs) estão limitados no que toca a extrair valor de um sempre crescente volume de Dados Espaciotemporais em Larga Escala no domínio da Mobilidade e Tráfego (em inglês, Mobility- and Traffic-related Big Spatiotemporal Data —MobiTrafficBD) que estão a ser colecionados e recolhidos. Os trabalhos de investigação sobre os tópicos de Big Data, Dados Espaciotemporais e, especialmente, de MobiTrafficBD, estão dispersos, e a literatura existente não oferece uma metodologia comum e concreta para preparar, configurar, implementar e usar uma plataforma (framework) baseada em tecnologias Big Data para gerir o ciclo de vida de dados espaciotemporais em larga escala, com ênfase nas série temporais georreferenciadas (em inglês, geo-referenced time series — GRTS) e eventos espacio- temporais (em inglês, spatiotemporal events — ST Events), extrair valor destes dados e apoiar os RITMOs nos seus processos de decisão. Esta dissertação doutoral propõe uma metodologia prescritiva orientada a dados, para o design, desenvolvimento e implementação de plataformas de MobiTrafficBD, focadas em GRTS e ST Events. Além de uma revisão de literatura completa nas áreas de Dados Espaciotemporais, Big Data e na junção destas áreas através do conceito de MobiTrafficBD, a metodologia proposta contem um conjunto de características gerais, requisitos técnicos, componentes lógicos, fluxos de dados e modelos de infraestrutura tecnológica, bem como diretrizes e boas práticas para investigadores, profissionais e outras partes interessadas, como RITMOs, com o objetivo de guiá-los pelas fases de design, desenvolvimento e implementação de qualquer pla- taforma MobiTrafficBD. Este trabalho deve ser visto como um guia metodológico de suporte, baseado em Arqui- teturas de Referência e diretrizes amplamente utilizadas, mas enriquecido com as característi- cas e assuntos implícitos relacionados com Dados Espaciotemporais em Larga Escala, como no caso de GRTS e ST Events. A metodologia proposta foi avaliada e demonstrada em vários cenários reais no âmbito de projetos de investigação financiados pela Comissão Europeia e pelo Governo português, nos quais foram implementados métodos, ferramentas e tecnologias nas áreas de Gestão de Dados, Processamento de Dados e Ciência e Visualização de Dados em plataformas MobiTrafficB

Modelling spatial processes of infectious diseases

Human movement plays a key role in the spread of infectious diseases, leading to spatial heterogeneities in disease transmission. An understanding of the causes of these heterogeneities is important in the design, application, and evaluation of public health interventions. In this thesis, we developed a range of statistical models to elucidate spatial dependencies of infection patterns in different populations, and embed existing mobility models within a principled statistical framework. We applied a spatio-temporal generalized linear mixed model to include both climate and non-climate effects on malaria incidence in Malawi while implicitly accounting for spatial dependency and the role of human movement. We further developed methods for real-time assessment of an epidemic by adding spatial information in the calculation of reproductive numbers to account for spatial heterogeneities. A detailed review of mobility models and their use in infectious disease modelling was performed to identify current gaps and opportunities in the field. Finally, a model describing the rate at which human social contact is made in different locations was developed to identify individual-level differences in mobility. The implications for understanding epidemic process and informing control are discussed. With increasing availability of fine-scale mobility data, studying and understanding mobility patterns and their relationship with infectious disease spread will play a key role in developing efficient surveillance and control of emerging and re-emerging diseases

ABSTRACT BOOK 50th World Conference on Lung Health of the International Union Against Tuberculosis and Lung Disease (The Union)

The International Journal of Tuberculosis and Lung Disease is an official journal of The Union. The Journal’s main aim is the continuing education of physicians and other health personnel, and the dissemination of the most up-to-date infor mation in the field of tuberculosis and lung health. It publishes original articles and commissioned reviews not only on the clinical and biological and epidemiological aspects, but also—and more importantly—on community aspects: fundamental research and the elaboration, implementation and assessment of field projects and action programmes for tuberculosis control and the promo tion of lung health. The Journal welcomes articles submitted on all aspects of lung health, including public health-related issues such as training programmes, cost-benefit analysis, legislation, epidemiology, intervention studies and health systems research

Unmet goals of tracking: within-track heterogeneity of students' expectations for

Educational systems are often characterized by some form(s) of ability grouping, like tracking. Although substantial variation in the implementation of these practices exists, it is always the aim to improve teaching efficiency by creating homogeneous groups of students in terms of capabilities and performances as well as expected pathways. If students’ expected pathways (university, graduate school, or working) are in line with the goals of tracking, one might presume that these expectations are rather homogeneous within tracks and heterogeneous between tracks. In Flanders (the northern region of Belgium), the educational system consists of four tracks. Many students start out in the most prestigious, academic track. If they fail to gain the necessary credentials, they move to the less esteemed technical and vocational tracks. Therefore, the educational system has been called a 'cascade system'. We presume that this cascade system creates homogeneous expectations in the academic track, though heterogeneous expectations in the technical and vocational tracks. We use data from the International Study of City Youth (ISCY), gathered during the 2013-2014 school year from 2354 pupils of the tenth grade across 30 secondary schools in the city of Ghent, Flanders. Preliminary results suggest that the technical and vocational tracks show more heterogeneity in student’s expectations than the academic track. If tracking does not fulfill the desired goals in some tracks, tracking practices should be questioned as tracking occurs along social and ethnic lines, causing social inequality

Emerg Infect Dis

Emerging Infectious Diseases is providing access to these abstracts on behalf of the ICEID 2022 program committee (http://www.iceid.org), which performed peer review. ICEID is organized by the Centers for Disease Control and Prevention and Task Force for Global Health, Inc.Emerging Infectious Diseases has not edited or proofread these materials and is not responsible for inaccuracies or omissions. All information is subject to change. Comments and corrections should be brought to the attention of the authors.Suggested citation: Authors. Title [abstract]. International Conference on Emerging Infectious Diseases 2022 poster and oral presentation abstracts. Emerg Infect Dis. 2022 Sep [date cited]. http://www.cdc.gov/EID/pdfs/ICEID2022.pdf2022PMC94238981187

Promoting Statistical Practice and Collaboration in Developing Countries

"Rarely, but just often enough to rebuild hope, something happens to confound my pessimism about the recent unprecedented happenings in the world. This book is the most recent instance, and I think that all its readers will join me in rejoicing at the good it seeks to do. It is an example of the kind of international comity and collaboration that we could and should undertake to solve various societal problems. This book is a beautiful example of the power of the possible. [It] provides a blueprint for how the LISA 2020 model can be replicated in other fields. Civil engineers, or accountants, or nurses, or any other profession could follow this outline to share expertise and build capacity and promote progress in other countries. It also contains some tutorials for statistical literacy across several fields. The details would change, of course, but ideas are durable, and the generalizations seem pretty straightforward. This book shows every other profession where and how to stand in order to move the world. I urge every researcher to get a copy!" —David Banks from the Foreword Promoting Statistical Practice and Collaboration in Developing Countries provides new insights into the current issues and opportunities in international statistics education, statistical consulting, and collaboration, particularly in developing countries around the world. The book addresses the topics discussed in individual chapters from the perspectives of the historical context, the present state, and future directions of statistical training and practice, so that readers may fully understand the challenges and opportunities in the field of statistics and data science, especially in developing countries. Features • Reference point on statistical practice in developing countries for researchers, scholars, students, and practitioners • Comprehensive source of state-of-the-art knowledge on creating statistical collaboration laboratories within the field of data science and statistics • Collection of innovative statistical teaching and learning techniques in developing countries Each chapter consists of independent case study contributions on a particular theme that are developed with a common structure and format. The common goal across the chapters is to enhance the exchange of diverse educational and action-oriented information among our intended audiences, which include practitioners, researchers, students, and statistics educators in developing countries