Research Domain
Computational Epidemiology & Public Health

Research Leaders
Ciro Cattuto, Corrado Gioannini, Daniela Paolotti, Vittoria Colizza

The activity of Computational Epidemiology & Public Health domain research focuses on the following main areas:

I. development of the mathematical and computational methods needed to achieve prediction and predictability of disease spreading in complex techno-social systems;
II. development of large scale, data driven computational models endowed with a high level of realism and aimed at epidemic scenario forecast and policy making;
III. design and implementation of original data-collection schemes motivated by identified modelling needs, such as the collection of real-time disease incidence, through innovative web and ICT applications;
IV. set up of a computational platform for epidemic research and data sharing that will generate important synergies between research communities and countries;
V. evidence-based scenario building in order to enable and to support decision and policy making. This research domain proposes a truly interdisciplinary effort combining complex systems science, computational sciences, mathematical epidemiology, and ICT technologies.

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Steering Socio-technical Systems (S3)

S3 is a multipronged project thrusting a major effort to explore societal issues by complex systems, data and computational thinking science, in an integrated framework for mathematical, modelling and computational foundation of socio/econo-technical systems science, with a portfolio of case studies.

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Influenzanet & Influweb

Influenzanet is a system designed to monitor the activity of influenza-like-illness (ILI) with the aid of volunteers via the Internet.

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Citizen Science to promote creativity, scientific literacy, and innovation throughout Europe

We are witnessing a remarkable growth of citizen science (CS), that is, the participation of people from all walks of life in scientific research. The main aim of this Action is to bundle capacities across Europe to investigate and extend the impact of the scientific, educational, policy, and civic outcomes of citizen science with the stakeholders from all sectors concerned (e.g., policy makers, social innovators, citizens, cultural organizations, researchers, charities and NGOs), to gauge the potential of citizen science as enabler of social innovation and socio-ecological transition.

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The global epidemic and mobility model, Gleam, combines real-world data on populations and human mobility with elaborate stochastic models of disease transmission to deliver analytic and forecasting power to address the challenges faced in developing intervention strategies that minimize the impact of potentially devastating epidemics.

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SocioPatterns is an interdisciplinary research collaboration that adopts a data-driven methodology with the aim of uncovering fundamental patterns in social dynamics and coordinated human activity.The SocioPatterns team also works on developing tools and techniques to represent, analyze and visualize the collected data.

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CIMPLEX - Bringing CItizens, Models and Data together in Participatory, Interactive SociaL EXploratories

We propose visionary research to develop modeling, computational, and ICT tools needed to predict and influence disease spread and other contagion phenomena in complex social systems. To achieve non-incremental advances we will combine large scale, realistic, data-driven models with participatory data-collection and advanced methods for Big Data analysis.

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Predemics aims at providing preparedness, prediction and prevention of emerging zoonotic viruses with pandemic potential using multidisciplinary approaches. The project will study the following zoonotic viruses with epidemic potential in Europe: influenza virus, hepatitis E virus, viruses of the Japanese encephalitis serocomplex and lyssaviruses.

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