by Salvatore Rinzivillo (ISTI-CNR), Joakim Sundnes (SIMULA) and Karin Rainer (AGES)

The epidemic emergency created by the rapid spread of SARS-Cov-2 drew attention to the methods and models that allow us to understand, predict and control the diffusion of infectious diseases. A thorough knowledge of the diffusion dynamics of viruses can help us conduct effective tracking of the transmission chain, precisely identify and assess restrictive measures, and promptly counteract local outbreaks. Mathematical models and simulation systems based on these models provide a means of obtaining such an understanding and enable evaluation and comparison of multiple mitigation approaches.

by Nikos Petrellis (University of the Peloponnese)

Coronario is a multi-purpose platform that supports symptom tracking, social distancing and tracing, and experimentation with the reactions of the COVID-19 virus. Coronario users can be patients, medical practitioners and researchers. A major aim of the platform is to facilitate early detection and tracing of infected individuals as well as their behaviour based on localization and sensor indications.

by Peter Gaal, Miklos Szocska, Tamas Joo and Tamas Palicz (Semmelweis University, Budapest)

The analysis of routinely generated Big Data is increasingly seen as an inexpensive method to support evidence-based policymaking and implementation. Analysing routine data generated as a result of the use of mobile phones has long been recognised as a potential method to monitor population movement. This would provide invaluable information on the impact of social distancing measures that were implemented at an unprecedented scale during the COVID-19 pandemic [1]. While population movement monitoring on the basis of mobile phone use seems an obvious choice to support the management of the outbreak, several technical questions need addressing: in particular, the challenge of collating data from different mobile network providers. There are also data protection concerns, such as the need to comply fully with General Data Protection Regulation of the European Union (GDPR), which limits the feasibility of using the data in this way.

by Haridimos Kondylakis, Dimitrios G. Katehakis and Angelina Kouroubali (FORTH-ICS)

We protect the community. We protect ourselves. We decongest the health system.  We stay safe in COVID-19. One of the many responses to the global call against the world pandemic of COVID-19 resulted in “Safe in COVID-19”, an electronic platform developed by the Institute of Computer Science of the Foundation for Research and Technology – Hellas (FORTH-ICS), which is intended for tracing suspect, probable and confirmed incidence cases.

by Angelica Lo Duca and Andrea Marchetti (IIT-CNR)

Within the Institute of Informatics and Telematics in Pisa (Italy), a novel working group was born, called Human-Centered Technologies (HCT). One of the main objectives of HCT involves the definition of decision support systems, which help stakeholders and people in general to understand the Italian society, economy and health. Within this context, we have implemented a strategy to assess the impact of the COVID-19 outbreak on the Italian tourism industry in terms of tourism income and reduction in the number of tourist arrivals.

by Christophe Ponsard and Bérengère Nihoul (CETIC)

The design sustainable systems requires to explore and combine multiple facets relating to the dimensions of society, economics and the environment. To analyse COVID-19 pandemic management strategies from a fairness perspective, we used a sustainability modelling framework together with a pattern library focusing on fairness. This helps with the analysis of strategic decision making and provides guidance for a successful adoption of measures.

by Paulo Carvalho (CGIE – Centre de gestion informatique de l’éducation - Luxembourg), Patrik Hitzelberger (LIST – Luxembourg Institute of Science and Technology – Luxembourg)

Tabular structures (e.g. Excel, CSV) are often used to represent and store information. Unfortunately, user error can result in the valuable and diverse data stored in such structures being lost or overwritten. This can lead to major problems, depending on how and why the data is intended to be re-used. We propose a visual solution to help users analyse and detect problems in tabular data.

by Sébastien Faye (Luxembourg Institute of Science and Technology – LIST), Tai-yu Ma (Luxembourg Institute of Socio-Economic Research – LISER), Pascal Lhoas (LIST) and Djamel Khadraoui (LIST)

The COVID-19 pandemic has given rise to many digital tools to help monitor and interrupt infection chains. Among them, contact tracing apps are a reliable means of preventing the virus from spreading further, but they suffer from a low adoption rate. This article introduces complementary approaches based on data fusion from wireless networks such as Bluetooth or Wi-Fi, which will be tested in Luxembourg in 2021 in the mobility sector and have the potential to facilitate the monitoring of social interactions in indoor environments.

by Mario Drobics, Alexander Preinerstorfer and Andrés Carrasco (AIT Austrian Institute of Technology)

Managing a global pandemic requires constant analysis of the current situation and corresponding responses. An open message bus can help organisations achieve a common operational picture across system boundaries, thus ensuring efficiency and effectiveness of their efforts.

by Refiz Duro, Alexandra-Ioana Bojor and Georg Neubauer (AIT Austrian Institute of Technology GmbH)

The measures to tackle the COVID-19 pandemic have introduced a new way of living: human activities and behaviour have had to change. Lockdowns, closed businesses and social distancing have placed governments and their decision-making processes under scrutiny. Significant amounts of timely and precise data are critical in decision-making processes. Our contribution comes from a high vantage point – collecting and analysing Earth observation satellite imagery to detect moving vehicles as a direct sign of human activity. Can it be done? 

by Giulio Rossetti (ISTI-CNR), Letizia Milli (University of Pisa) and Salvatore Rinzivillo (ISTI-CNR)  

Analysing the dynamics of and on networks is currently a hot topic in social network analysis. To support students, teachers, developers and researchers in this work, we have developed a novel framework, namely NDlib, an environment designed to describe diffusion simulations. NDlib is designed to be a multi-level ecosystem that can be fruitfully used by different user segments.

by Gianpaolo Coro (ISTI-CNR)

Researchers from ISTI-CNR (Italy) used marine models, designed to monitor species habitats and invasions, to identify the countries with the highest risk of COVID-19 spread due to climatic and human factors. The model correctly identified most locations where large outbreaks were recorded, independent of population density and dynamics, and is a valuable source of information for smaller-scale population models.

by Štefan Emrich and Niki Popper (dwh GmbH, TU Wien, DEXHELPP)

COVID-19 brought unprecedented publicity for modelling and simulation. But a broad audience was left with very little information about what modern simulation models have to take into account and how valuable they have become as decision-support-tool. And how versatile: by far not limited to health-care.

by Wouter Edeling (CWI) and Daan Crommelin (CWI and University of Amsterdam)

We argue that COVID19 epidemiological model simulations are subject to uncertainty, which should be made explicit when these models are used to inform government policy.

by Christophe Henry, Kerlyns Martinez-Rodriguez, Mireille Bossy (Université Côte d’Azur, Inria,  CNRS, Cemef), Hervé Guillard (Université Côte D’Azur, Inria, CNRS, LJAD), Nicolas Rutard and Angelo Murrone (DMPE, ONERA)

Researchers from Inria and the French Aerospace Lab ONERA are collaborating on a joint project. The goal is to assess the variability in the advice for social distancing precautions that can be drawn from numerical simulations of airborne dispersion. This variability depends on a number of factors, including: physical variables (e.g. droplet size, ejection velocity), modelling methods used (e.g. turbulence model) and numerical aspects (mesh). We use sensitivity analysis tools to quantify and order the role these factors play in influencing the numerical results.

by Stelios Zimeras (University of the Aegean)

In disease spread processes where hidden information dramatically affects the quality of the data, modelling of the spatial patterns is a challenging task. In this situation, models based on spatial structure are important for the investigation of neighbourhood structure between regions where spatial connectivity is defined. We have developed spatial techniques to investigate homogeneity.

by Gábor Szederkényi (Pázmány Péter Catholic University), Tamás Péni (SZTAKI) and Gergely Röst (University of Szeged)

A control theoretic approach can efficiently support the systematic design of strategies to suppress or mitigate the effects of the COVID-19 pandemic.

by Lisa Veiber, Salah Ghamizi (University of Luxembourg) and Jean-Sébastien Sottet (LIST)

Many statistical and machine learning (ML) models have been developed to provide forecasts for the COVID-19 crisis. Acquiring qualitative data with a rather short timeframe is a challenge for anyone who wants to build a ML algorithm to support forecasts about the pandemic. We propose a hybrid approach that takes into consideration factors from human knowledge in order to reinforce or correct data-driven ML predictions.

Next issue: April 2021
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