The course is divided in 13 modules (see below). It intends for urban policy makers and practitioners, representatives of community initiatives and organizations, and anybody interested in big data applications for smart, healthier and sustainable cities.
This will be the occasion to learn from experts on big data, artificial intelligence and public health from Europe and the United States. Participants will work through the course on an individual basis and will be assessed via a comprehension and knowledge quiz at the end of each class. Successful completion of the course will be defined by the achievement of 70% correct answers in the final quiz that refer to all the taught classes.
For more information about the course check the PULSE Learning Platform here.
|Module 1||Introduction to Course The course is intended for urban policymakers, community groups, and those interested in big data applications for smart, sustainable cities.|
|Module 2||Public health: challenges for the cities This module addresses key public health challenges in cities with appropriate data and analytical tools.|
|Module 3||Social determinants for health This class aims to review the concept of health inequalities, it describes main theoretical approaches, from psychological material factors, or the ecosocial theory. Its review the Conceptual models of the social determinants of health, including the most recent model for Social Determinants of Health for Just Societies, PAHO, 2018. The next section presents some insight about health inequalities across gender. Social determinants of health are different for men and women; the majority of studies are based on men; frequently, the results of men and women are presented combined. Finally explains the WHO The Commission to Reduce Social Inequalities in Health.|
|Module 4||Module 4: Health in All Policies In this presentation, we begin by introducing and explaining the concept of Health in All Policies (HiAP), which can be understood as a cross-sector approach to policy decision-making and implementation that takes health implications into consideration. We then delineate the components of successful implementation of the HiAP approach, mechanisms that can be used for implementation, expected outcomes, and how HiAP can be strengthened with an aging lens. Finally, in an effort to demonstrate HiAP in action, we explain several case studies of HiAP approaches in New York: IMAGE, an interactive map of aging; Complete Streets; and a Downtown Revitalization Initiative.|
|Module 5||Inclusive Smart Cities This module introduces the concept of smart cities oriented on services to citizens and present specific use cases of Singapore as one of the leading existing smart cities initiatives in the world. It describes the key elements of the Singaporean Strategic National Project “Smart Nation” as an illustration global concept covering different aspects of smart cities (transportation, energy, cybersecurity.) with a special focus on health and wellbeing related to ageing people. The course presents different actions and programs (10.000 steps challenge, Moment of Life, the Smart Nation Sensor Platform, the National Digital Identity, the CODEX Data Exchange Architecture, etc.) led by the Singaporean government and agencies as part of the Smart Nation National Project. It also exposes the implication of PULSE project in this initiative through its pilot site in Singapore. A dedicated video of PULSE project in Singapore was produced and included in the course.|
|Module 6||Big data approach and opportunities for the society This module introduces the concept of Big Data, its characteristics and the advantages of the Big Data approach in dealing with immersive data. The course also presents a landscape of today’s different existing technological solutions that deal with data scale up. The second part of this module is dedicated to discuss the societal impact of Big Data. It presents a discussion on the Big Data impact on different domains and focuses on societal impact of Big Data through the PULSE project experience. The course also provides some other initiatives that use Big Data and shows their societal impact.|
|Module 7||Artificial Intelligence This class provides an overview about the potential impact that AI technologies can provide in healthcare. After having considered some recent AI applications, the risks of excessive expectations are discussed and an historical perspective is given. AI is then put in the context of healthcare organizations and decision making, and examples of current research projects, which take into account the multifaceted aspects of health, are given.|
|Module 8||Health risk models The topic of this lecture is health risk models that are models to predict the risk of a subject of developing future adverse outcomes. Health risk models can be used by general practitioners, clinicians, health departments and public health observatories to identify subjects at risk of developing a certain disease (e.g. diabetes or asthma) who may benefit from targeted interventions. In this lecture, the student will learn about the fundamental steps for building and evaluating health risk models. The lecture will also introduce some advanced methodologies for developing health risk models, such as survival analysis, Bayesian Networks and variable ranking techniques. The models developed in PULSE to predict the onset of type 2 diabetes and asthma will be considered as case studies throughout the lecture.|
|Module 9||Modelling Wellbeing The topic of this lecture is subjective wellbeing models that are models to predict the perceived wellbeing of a subject. In this lecture, the student will learn about the fundamental steps for building and evaluating these models: from the definition of wellbeing to the measures of it, from the build of a statistical model to the prediction of wellbeing at a subject- and community-level. The lecture will also detail the definition of sub-dimensions (i.e. different aspects) of the wellbeing and highlight the importance of their use to provide a multidimensional view of the problem. On a more technical side, the lectures also stress about the relevance of resampling techniques to avoid too optimistic evaluations of the quality of the models.|
|Module 10||Geographic information for health data The present course is about Geographic information for health data. It illustrates the various types of geographic information, the basic of a GIS (Geographic Information System) software program and some simple application of GIS science to health data, including time-dependent maps. Detailed open datasets are provided. Moreover, the course has a hands-on significant part, in which participants are driven to carry out the depicted operations with the open source QGIS program.|
|Module 11||Mobile technology and citizen participation The lecture gives an overview on the concept of participation with a focus on mobile technologies and the potential use in environmental and public health. The second part of the course presents different approaches to design mobile application as user centered design principles, persuasive design techniques and other relevant approaches to successfully develop interventions using mobile technology.|
|Module 12||How Interactive Visualizations of Big Data Can Better Inform Environmental Policy Data visualization is a key point to make data a real source of information especially for policy makers and health professionals. Currently there are available several tools, website and instrument that provides data, statistics and way to visualize and inquiry global geospatial databanks on air quality, air pollution and atmospheric phenomena. The lesson will give an overview of several of these instruments that are also used at an international level.|
|Module 13||Ethics and regulations Big Data refers to the enormous increase in volume, access to and automated use of information. It is thought to offer an unprecedented opportunity to improve both public health research and practice, but attention has been drawn to the technical challenges and ethical risks that come with it. We cannot expect to give simple answers to complex moral problems involving big data. Instead, this seminar will focus on general ethical issues and provide principles which are applicable to different circumstances, considering current and (possible) future uses of data. A non-comprehensive list of good practices for ethical Big Data collection and processing.|
PULSE is a H2020 project aimed to improve predictive analytics and risk detection in cities as well as influence lifestyles and behaviour. The project analyses the environmental and behavioural determinants of disease onset by focusing in particular on the link between air pollution and asthma, and between physical inactivity and Type 2 Diabetes. Moreover, the project is addressing community wellbeing and its relation with health outcomes. The final goal is to build extensible models and technologies to predict, mitigate and manage public health problems and implement a Public Health Observatory in each city which will serve as linked hub that utilises knowledge-driven processes and big data to shape intersectorial public policies and service provision.