The first and core objective of City4Age is to enable Ambient Assisted Cities or Age-friendly Cities, where the urban communities of elderly people living in Smart Cities are provided with a range of ICT tools and services that - in a completely unobtrusive manner - will improve the early detection of risks related to cognitive impairments and frailty while they are at home or in the move within the city. The second objective is to provide a range of associated tools and services which - with the appropriate interventions - will mitigate the detected risks. The final objective of C4A is to define a model which will provide sustainability and extensibility to the offered services and tools by addressing the unmet needs of the elderly population in terms of (i) detecting risks related to other health type problems, (ii) stimulating and providing incentives to remain active, involved and engaged, (iii) creating an ecosystem for multi-sided market by matching needs and their fulfillments, (iv) contributing to the design and operation of the ultimate Age-friendly City, where the city itself provides support for detecting risks and providing interventions to those affected by mild cognitive impairment (MCI) and frailty. To achieve these objectives City4Age builds on: - behavioural, sociological and clinical research on “frailty” and MCI in the elderly population; - state of art ICT technology (i) for “sensing” personal data and exposing them as linked open data, (ii) for designing the algorithms and the API’s to extract relevant behaviour changes and correlated risks, and (iii) for designing interventions to counter the risks, - stakeholder engagement in order to be driven by relevant user needs to ensure end-user acceptance.

Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) are chronic diseases characterized by progressive or alternate impairment of neurological functions (motor, sensory, visual, cognitive). Patients have to manage alternated periods in hospital with care at home, experiencing a constant uncertainty regarding the timing of the disease acute phases and facing a considerable psychological and economic burden that also involves their caregivers. Clinicians, on the other hand, need tools able to support them in all the phases of the patient treatment, suggest personalized therapeutic decisions, indicate urgently needed interventions. Artificial Intelligence is the key to successfully satisfy these needs to: i) better describe disease mechanisms; ii) stratify patients according to their phenotype assessed all over the disease evolution; iii) predict disease progression in a probabilistic, time dependent fashion; iv) investigate the role of the environment; v) suggest interventions that can delay the progression of the disease. BRAINTEASER will integrate large clinical datasets with novel personal and environmental data collected using low-cost sensors and apps. Software and mobile apps will be designed embracing an agile and user-centred design approach, accounting for the technical, medical, psychological and societal needs of the specific users. BRAINTEASER will implement a system able to guarantee cybersecurity and data ownership to the patients; will provide quantitative evidence of benefits and effectiveness of using AI in health-care pathways implementing a proof-of-concept of its use in real clinical setting. Procedural requirements that support Software as Medical Device certification will be used involving clinicians and patients stakeholders and producing a set of recommendations for public health authorities. Results will be disseminated accordingly to an open science paradigm under the European Open Science Cloud initiative.

The impact of the COVID-19 pandemic has been deep and wide. In spite of unprecedented efforts to understand the COVID-19 disease and its causative virus SARS-CoV-2, months after the emergence of the first local case in Europe (San Matteo hospital, Pavia, 21st February 2020) significant knowledge gaps persist. While social and natural scientists managed to develop new research and shed light on the dynamics of the outbreak and the most effective possible containment measures, governments have been increasingly faced with the need to adopt urgent decisions. Against this background, PERISCOPE plans to contribute to a dramatically deeper understanding of the dynamics of the outbreak, by means of an intense multi-disciplinary research, both theoretical and experimental, and the consideration of different viewpoints: clinic and epidemiologic; humanistic and psychologic; socio-economic and political; statistical and technological. The overarching objectives of PERISCOPE are to map and analyse the unintended impacts of the COVID-19 outbreak; develop solutions and guidance for policymakers and health authorities on how to mitigate the impact of the outbreak; enhance Europe’s preparedness for future similar events; and reflect on the future multi-level governance in the health as well as other domains affected by the outbreak. In pursuing this objective, PERISCOPE sheds new light on the unintended and indirect consequences of the outbreak and the related government responses, with the intention to preserve evidence-based policymaking by collecting an unprecedented amount of data and information on the social, economic and behavioural consequences of the current pandemic. At the same time, PERISCOPE will produce new information on the conditions that led to the impact of the pandemic, the differences in “policy mix” adopted at the national level in EU and associated countries, and the behavioural impacts of both the outbreak and the policies adopted.

PULSE (Participatory Urban Living for Sustainable Environments) will leverage diverse data sources and big data analytics to transform public health from a reactive to a predictive system, and from a system focused on surveillance to an inclusive and collaborative system supporting health equity. Working within five global cities, PULSE will harvest open city data, and data from health systems, urban and remote sensors, personal devices and social media to enable evidence-driven and timely management of public health events and processes. The clinical focus of the project will be respiratory diseases (asthma) and metabolic diseases (Type 2 Diabetes) in adult populations. The project will develop risk stratification models based on modifiable and non-modifiable risk factors in each urban location, taking account of biological, behavioural, social and environmental risk factors. Following the recommendations of WHO Europe (2015), the project will also focus on the development of metrics, and data-driven approaches, to community resilience and well-being in cities. Deploying a Health in All Policies (HiAP) perspective, and a ‘whole-of-city’ model, the project will integrate and analyze data from the health, environment, planning and transport sectors in each city. PULSE will pioneer the development and testing of dynamic spatio-temporal health impact assessments using geolocated population-based data. PULSE will also develop simulation models of potential policy scenarios to allow decision-makers, citizens and businesses to ascertain the impact of proposed policies. The project will culminate in the establishment of Public Health Observatories in each urban location. These observatories will serve as linked hubs that utilize knowledge-driven processes and big data to shape intersectoral public policy and service provision, support citizen health, and encourage entrepreneurship in the fields of data science and mobile health.

BEYOND brings forward a reference Big Data Management Platform, on top of which an advanced AI analytics toolkit will be offered allowing for the delivery of derivative data and intelligence out of a blend of real-life building data and relevant data coming from external sources (batch and real-time). The Analytics Toolkit will enable the execution of a wealth of descriptive-predictive-prescriptive analytics on the basis of pre-trained algorithms focusing on Personal Analytics (consumer behaviour, comfort and flexibility profiling), Industrial Analytics (Energy Performance, Predictive Maintenance, Forecasting and Flexibility analytics), along with Edge Analytics towards intelligent real-time automated control of building assets. The BEYOND Big data platform and AI Analytics Toolkit will be associated with novel data (intelligence) sharing mechanisms that enable the integration of value chain stakeholders (building and market actors), thus allowing the latter ones to gain the opportunity to acquire building data & advanced analytics and build their own applications and solutions, towards (i) providing innovative energy services to the building sector, while (ii) at the same time improving their business operations and increasing their business processes and operations (de-risked EPC, informed policy planning & infrastructure sizing). To achieve this degree of intelligence and optimization, BEYOND will leverage data, primary or secondarily related to the building domain, coming from diverse sources (data APIs, historical, sensor / IoT, weather and market data, the EU Building Stock Observatory, open building data hubs) integrated to the BEYOND Platform to enable data exchange in an interoperable and standards-based manner. BEYOND will be validated in 4 large-scale demonstrators (EL, ES, FI, RS) involving (i) data collection from diverse building typologies, data sources, building systems and devices, and (ii) data (intelligence) sharing with various market actors.