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

Project Information

CIMPLEX

Grant agreement ID: 641191

DOI

10.3030/641191

Project closed

EC signature date 24 November 2014

Start date 1 January 2015

End date 31 December 2017

Funded under

• EXCELLENT SCIENCE - Future and Emerging Technologies (FET)

Total cost

€ 4 206 875,00

EU contribution

€ 3 450 625,00

Coordinated by

DEUTSCHES FORSCHUNGSZENTRUM FUR KUNSTLICHE INTELLIGENZ GMBH
Germany

Periodic Reporting for period 3 - CIMPLEX (Bringing CItizens, Models and Data together in Participatory, Interactive SociaL EXploratories)

Reporting period: 2017-01-01 to 2017-12-31

Summary of the context and overall objectives of the project

The project aims 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. In particular we will go beyond the one-dimensional focus of current approaches tackling one aspect of the problem at a time. We will interconnect contagion progression (e.g. epidemics) with social adaptation, the economic impact and other systemic aspects that will finally allow a complete analysis of the inherent systemic risk. We will develop models dealing with multiple time and length scales simultaneously, leading to the definition of new, layered computational approaches. Towards policy impact and social response we will work to close the loop between models, data, behavior and perception and develop new concepts for the explanation, visualization and interaction with data and models both on individual and on collective level. We will cast the fundamental advances into an integrated system building on widely accepted open ICT technologies that will be used and useful beyond the project. As a tangible ICT outcome directed at facilitating the uptake and impact of the project, we will implement “Interactive Social Exploratories” defined as interactive environments which act as a front-end to a set of parameterizable and adjustable models, data analysis techniques, visualization methods and data collection frameworks. In summary, we aim to (1) produce fundamental theoretical, methodological and technological advances (2) mold them into a broadly usable ICT platform that will be a catalyst for producing, delivering, and embedding scientific evidence into the policy and societal processes and (3) evaluate the system empirically with policy makers and citizens focusing on the concrete problem of epidemic spreading.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

From the basic theoretical understanding of various aspects of complex contagious phenomena achieved in PY1, in PY2, we have focused on (1) the interaction and integration between different modeling approaches, in particular multi scale and multi-aspects models combining work from different partners, (2) inclusion of realistic, interactively collected behavioral data in the models, (3) the implementation of a first fully integrated Explanatory prototype which allows flexible combination of different data sources, models, and visualization techniques and (4) the development and real life deployment of a participatory, privacy aware data collection App for the GrippeNet system.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

To provide data to calibrate and validate the models a novel concept of social interactions based, distributed data collection and aggregation has been developed. The concept is based of the notion propagating request for data along people’s social networks with everyone deciding, which request to propagate based on his/hers individual assessment of the importance of the request. In a similar manner collected data is incrementally aggregated, interpreted and propagated back to the source of the request in a way that respects individual policies and privacy concerns. The concepts is currently being implemented in mobile App that is connected to the Influenza Net (which is a production data collection system for monitoring influenza spread)

In parallel the development of new social mining and methods connecting the data to the models and capable of adapting to changes in model representation and configurations has started This includes on one hand methods based on multi-dimensional network representation applied to the specific problem evolution of social communities and interplay between individual profiles and collective patterns. On the other hand, data mining process that addresses a particular formulation of the link prediction problem for dynamic networks, called Interaction Prediction has been proposed. New data based models of human mobility taking into account so called returners and explored have also been studied.

To facilitate new concepts for mass participation in data collection, analysis and decision making processes workshops with the partners from ETHZ and DFKI highlighted key aspects for motivating individuals to contribute to data collections are personal benefits and the protection of individual-related data (T4.2). Currently we are discussing mass health data collection and focus on the possible realizations in the form of web services and mobile apps. We also developed and refined prototypes to monitor social responses related to epidemics and other information diffusion processes from public social media platforms such as Twitter. Finally we have developed a cognitively grounded model of opinion spreading in which the dynamics of opinions about a risk situation are modeled and tested. Furthermore, we investigate the dynamics and control of interacting spreading processes by, investigating information-driven disease control through voluntary vaccination, when two diseases spread simultaneously and infection by one pathogen makes infection by another more likely. To educate the general public about the power of modern computation techniques, models and data analysis we are invited to design and implement an exhibition together with the Museum Pfalzgalerie Kaiserslautern (http://www.mpk.de/) Germany. With different exhibits we plan to evaluate different interaction techniques and visualizations of a diverse set of data sets. Furthermore, we will show possibilities of modern data collection.

Based on the work carried out in previous projects (FP7 Epiwork), the ISI team is building a multiscale modeling platform for epidemic spreading simulations. In particular, the platform has at its core a computational modeling and simulation engine. We use different forecast methodologies based on statistical regression approaches and the GLEAM (GLobal Epidemic And Mobility model). At the beginning of the first year of the project, in May 2015, the ISI team has organized a public workshop with policy makers, which has been held in Florence, Italy. The workshop was part of the Digital Disease Detection conference organized by ISI Foundation, Healthmap and Skoll Global Threats Fund (http://www.healthmap.org/ddd/).

For the implementation of the planned exploratories an overall architecture has been defined and divided into three parts - Data Collection, Interaction and Modelling. The three modules are connected via service-based interfaces making use of open protocols, libraries and API standards. Different possible technical tools and systems for implementations available for defined sub architectural modules have been evaluated. DFKI added better CSS support to XML3D, which will allow an even smoother transition of 2D and 3D data visualizations in the Observatories. In collaboration with ISI, DFKI developed a web-based GLEAMviz client based on XML3D. CNR exploits the synergy with the EU Research Infrastructure SoBigData (http://sobigdata.eu). SoBigData proposes to create the Social Mining & Big Data Ecosystem: a research infrastructure (RI) providing an integrated ecosystem for ethic-sensitive scientific discoveries and advanced applications of social data mining on the various dimensions of social life, as recorded by “big data”.

Data Collection Architecture