ERCIM News 124
January 2021
Special theme Pandemic Modelling and Simulation
Guest editors Salvatore Rinzivillo (ISTI-CNR), Joakim Sundnes (SIMULA) and Karin Rainer (AGES)
Pandemic Modelling and Simulation - Introduction to the Special Theme
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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.
Exploiting a Symptom Tracking Platform for Social Distancing
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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.
Population Movement Monitoring Based on Mobile Phone Usage Data to Support Pandemic Decision Making
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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.
Staying Safe in COVID-19
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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.
Impact of COVID-19 Outbreak on Italian Tourism During the First Quarter of 2020
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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.
Fairness Analysis of Pandemic Management using a Sustainability Pattern Library
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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.
A Tabular Data Analysis Solution to Help Improve the Quality of Data Relating to the COVID-19 Pandemic
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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.
Fusing Wireless Network Data to Analyse Indoor Social Distancing
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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.
Modelling Time-Varying Epidemiological Parameters for COVID-19
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by Ercan Engin Kuruoglu (ISTI-CNR) and Yang Li (Tsinghua-Berkeley Shenzhen Institute)
Daily estimates of parameters relating to epidemics can help public health experts to track the changes in the epidemic’s dynamics and the effectiveness of public health policy changes.
A Common Information Space for Pandemic Management
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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.
Observing Moving Vehicles as a Signature of Human Activity During a Pandemic
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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?
NDlib: A Python Library to Model and Analyze Diffusion Processes over Complex Networks
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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.
Predicting the Spread of COVID-19 through Marine Ecological Niche Models
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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.
How an Agent-Based Population Model Became a Key-Element of the Austrian Effort Against COVID-19
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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.
The Case for Uncertainty in COVID19 Predictions
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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.
Social Distancing: The Sensitivity of Numerical Simulations
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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.
Using Markovian Models to Simulate Disease Spread
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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.
Control Theoretic Approach for COVID-19 Management
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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.
A Hybrid Predictive Model for Mitigating Health and Economic Factors during a Pandemic
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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.
Smart Working at CNR-ISTI in the COVID-19 Era
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by Roberto Scopigno and Daniela Giorgi (ISTI-CNR)
The CNR Institute for Information Science and Technologies describes its experience in adapting to smart working, which has dramatically changed the institute’s modus operandi for most of the year 2020.
Home Office - A Curse or a Blessing?
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by Manuela Kos (AIT Austrian Institute of Technology)
Together through distance, but virtually. The unexpected disruption called COVID-19 came upon Europe at a rapid pace. Companies and their employees had to adjust to the situation in the best possible way and to embrace new routines. We explain how this was achieved by the AIT Austrian Institute of Technology.
Special Video Pages for the W3C Member Meetings
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by Bert Bos (W3C/ERCIM)
In 2020 the global pandemic accelerated a global trend towards virtualisation, making the Web even more critical to society. W3C converted its physical meetings and workshops to virtual ones, tapping into the capabilities of the Web it has been leading for over 25 years.
From We@CWI to CWI@home
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by Angelique Schilder (CWI)
During the coronavirus pandemic, CWI was one of the many research institutes that asked their employees to work from home, which presented challenges for everyone. The Dutch Research Council (NWO) conducted a survey to find out about researchers’ experiences of working from home. Based on the results of the survey, CWI developed recommendations to ease the pressure of working at home.
Panel Discussion on Mobile Contact Tracing Apps at IEEE MDM 2020: A Summary
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by Demetrios Zeinalipour-Yazti (University of Cyprus) and Christophe Claramunt (Naval Academy Research Institute)
The outbreak of the COVID-19 global pandemic has called for practical information and communication technologies to contribute to the worldwide effort to track and curb the spread of the virus. To this end, a series of mobile contact tracing applications (MCTA) have been developed to help identify people who may have been in contact with a person infected with COVID-19 and to rapidly deliver information to these individuals. We gathered together prominent scientists to discuss the major and open topics surrounding MCTA [1] in an online panel discussion at the 21st IEEE International Conference on Mobile Data Management [L1], held between 30 June and 3 July 2020, Versailles, France.
A Multidimensional Method for Capturing Spatial Data
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by Christian Kollmitzer (AIT Austrian Institute of Technology GmbH), Melanie Schranz (Lakeside Labs GmbH) and Manuel Warum (AIT Austrian Institute of Technology GmbH)
Current methods for collecting spatial information from, e.g. crime scenes, historical excavation sites or construction sites, sometimes have significant limitations. Our approach is based on a modular, adaptable system that can be equipped with multiple sensors, an intuitive presentation and processing of collected, heterogeneous data. The generated model can be stored in a structured manner and supplemented with additional information such as place and time, whereby a continuous signature procedure is used.
Digital Seafaring: Digitising, Curating and Exploring Archival Sources of Maritime History
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by Martin Doerr (ICS-FORTH), Pavlos Fafalios (ICS-FORTH) and Apostolos Delis (IMS-FORTH)
The European project SeaLiT has developed a set of innovative tools for supporting maritime historians in digitising, curating and exploring archival sources of maritime history. The tools are the result of the interdisciplinary work between maritime historians of the Institute of Mediterranean Studies of FORTH and researchers and data engineers of the Centre for Cultural Informatics of the Institute of Computer Science of FORTH.
Airborne Reconnaissance in the Field of Natural Hazard Management and Public Safety
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by Alexander Preinerstorfer, Philip Taupe (AIT Austrian Institute of Technology GmbH) and Christoph Hochwarter (IFES Institut für empirische Sozialforschung GmbH)
Airborne information gathering can benefit crisis and disaster management by allowing authorities to quickly obtain an overall operational picture and gain in-depth insights into specific aspects of the situation. Developments in the field of unmanned aerial vehicles (UAVs) and the associated sensor technology are opening up a wide range of application areas, helping authorities and organisations tasked with security to reduce the operational risks. These areas of application are being investigated in the AREAS research project.
ERCIM “Alain Bensoussan” Fellowship Programme
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Announcement
The ERCIM PhD Fellowship Programme has been established as one of the premier activities of ERCIM. The programme is open to young researchers from all over the world. It focuses on a broad range of fields in Computer Science and Applied Mathematics.
Stefano Cresci Wins the 2020 ERCIM Cor Baayen Young Researcher Award
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Announcement
Stefano Cresci has been selected from among 13 excellent short-listed nominees as the winner of the 2020 ERCIM Cor Baayen Award. Stefano, from CNR, Italy, has received the award in recognition of the outstanding scientific quality of his research and the impact on science and society that he has already achieved as a young researcher.
CNR-Inria Team Wins Gold Medals at the RERS 2020 Parallel CTL Challenge
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Announcement
Franco Mazzanti (FMT lab, ISTI-CNR, Pisa, Italy), Frédéric Lang and Wendelin Serwe (CONVECS, Inria-Grenoble, France), after their successful participation in last year’s RERS 2019 Challenge, again won three gold medals at the “Parallel CTL” track of RERS 2020 [L1].
Dagstuhl Seminars and Perspectives Workshops
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Call for Proposals
Schloss Dagstuhl – Leibniz-Zentrum für Informatik is accepting proposals for scientific seminars/workshops in all areas of computer science, in particular also in connection with other fields.
One Million Enrollments in W3Cx
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W3C’s training activities have reached a significant milestone: one million enrollments in their MOOCs (Massive Open Online Courses) dedicated to front-end Web development!