Péni Tamás

Röst Gergely

Szederkényi Gábor

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Special theme:& Simulationercim-news.ercim.euERCIMNumber 124 January 2021NEWS2s> ;y;i‘P A  N D EM  ICAlso in this issueResearch and Society:Meeting the Challenges of COVID-19■nBfffainreiCONTENTSEditorial Information JOINT ERCIM ACTIONSERCIM News is the magazine o f ERCIM. Published quarterly, it reports on joint actions o f the ERCIM partners, and aims to reflect the contribu­tion made by ERCIM to the European Community in Information Technology and Applied Mathematics. Through short articles and news items, it provides a forum for the exchange o f information between the institutes and also with the wider scientific community. This issue has a circulation o f about 6,000 printed copies and is also available online.ERCIM News is published by ERCIM EEIG BP 93, F-06902 Sophia Antipolis Cedex, France +33 4 9238 5010, contact@ercim.eu Director: Philipp Hoschka, ISSN 0926-4981ContributionsContributions should be submitted to the local editor o f your country Copyright noticeAll authors, as identified in each article, retain copyright o f their work. ERCIM News is licensed under a Creative Commons Attribution 4.0 International License (CC-BY).AdvertisingFor current advertising rates and conditions, see https://ercim-news.ercim.eu/or contactpeter.kunz@ercim.euERCIM News online edition: ercim-news.ercim.eu/Next issue:April 2021: Brain-Inspired Computing SubscriptionSubscribe to ERCIM News by sending an email to en-subscriptions@ercim. euEditorial Board:Central editor:Peter Kunz, ERCIM office (peter. kunz@ercim. eu)Local Editors:• Christine Azevedo Coste, Inria, France (christine.azevedo@inria.fr)• Andras Benczur, SZTAKI, Hungary (benczur@info.ilab.sztaki.hu)• José Borbinha, Univ. o f  Technology Lisboa, Portugal (jlb@ist.utl.pt)• Are Magnus Bruaset, SIMULA, Norway (arem@simula.no)• Monica Divitini, NTNU, Norway (divitini@ntnu.no)• Marie-Claire F  orgue, ERCIM/W3 C (mcf@w3. org)• Lida Harami, FORTH-ICT, Greece (lida@ics.forth.gr)• Athanasios Kalogeras, ISI, Greece (kalogeras@isi.gr)• Georgia Kapitsaki, Univ. o f Cyprus, Cyprus (gkapi@cs.ucy.ac.cy)• Annette Kik, CWI, The Netherlands (Annette.Kik@cwi.nl)• Hung Son Nguyen, Unviv. o f Warsaw, Poland (son@mimuw.edu.pl)• Alexander Nouak, Fraunhofer-Gesellschaft, Germany (alexander.nouak@iuk.fraunhofer.de)• Maria Rudenschôld, RISE, Sweden (maria.rudenschold@ri.se)• Harry Rudin, Switzerland (hrudin@smile.ch)• Erwin Schoitsch, AIT, Austria (erwin.schoitsch@ait.ac.at)• Thomas Tamisier,LIST, Luxembourg (thomas.tamisier@list.lu)• Maurice ter Beek, ISTI-CNR, Italy (maurice. terbeek@isti. cnr. it)4 Stefano Cresci Wins the 2020 ERCIM Cor Baayen Young Researcher Award5 ERCIM “Alain Bensoussan” Fellowship ProgrammeRESEARCH AND SOCIETYThis section “Facing the Challenges of COVID-19” was coordinated by Peter Kunz (ERCIM Office) and Annette Kik (CWI).6 Smart Working at CNR-ISTI in the COVID-19 Eraby Roberto Scopigno and Daniela Giorgi (ISTI-CNR)7 Home Office - A Curse or a Blessing?by Manuela Kos (AIT Austrian Institute of Technology)8 Special Video Pages for the W3C Member Meetingsby Bert Bos (W3C/ERCIM)10 From We@CWI to CWI@homeby Angelique Schilder (CWI)11 Panel Discussion on Mobile Contact Tracing Apps at IEEE MDM 2020: A Summaryby Demetrios Zeinalipour-Yazti (University of Cypius) and Christophe Claramunt (Naval Academy Research Institute)SPECIAL THEMEThe special theme "Pandemic Modelling and Simulation" has been coordinated by the guest editors Salvatore Rinzivillo (ISTI-CNR), Joakim Sundnes (SIMULA) and Karin Rainer (AGES)12 Pandemic Modelling and Simulation - Introduction to the Special Themeby Salvatore Rinzivillo (ISTI-CNR), Joakim Sundnes (SIMULA) and Karin Rainer (AGES)14 Exploiting a Symptom Tracking Platform for Social Distancingby Nikos Petrellis, (University of the Peloponnese)16 Population Movement Monitoring Based on Mobile Phone Usage Data to Support Pandemic Decision Makingby Peter Gaal, Miklos Szocska, Tamas Joo and Tamas Palicz (Semmelweis University, Budapest)17 Staying Safe in COVID-19by Haridimos Kondylakis, Dimitrios G. Katehakis and Angelina Kouroubali (FORTH-ICS)ERCIM NEWS 124 January 202119 Impact of COVID-19 Outbreak on Italian Tourism During the First Quarter of 2020by Angelica Lo Duca and Andrea Marchetti (IIT-CNR)20 Fairness Analysis of Pandemic Management using a Sustainability Pattern Libraryby Christophe Ponsard and Bérengère Nihoul (CETIC)21 A Tabular Data Analysis Solution to Help Improve the Quality of Data Relating to the COVID-19 Pandemicby Paulo Carvalho (CGIE -  Centre de gestion informatique de l’éducation - Luxembourg), Patrik Hitzelberger (LIST -  Luxembourg Institute of Science and Technology -  Luxembourg)23 Fusing Wireless Network Data to Analyse Indoor Social Distancingby 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)25 Modelling Time-Varying Epidemiological Parameters for COVID-19by Ercan Engin Kuruoglu (ISTI-CNR) and Yang Li (Tsinghua-Berkeley Shenzhen Institute)26 A Common Information Space for Pandemic Managementby Mario Drobics, Alexander Preinerstorfer and Andrés Carrasco (AIT Austrian Institute of Technology)27 Observing Moving Vehicles as a Signature of Human Activity7 During a Pandemicby Refiz Duro, Alexandra-Ioana Bojor and Georg Neubauer (AIT Austrian Institute of Technology GmbH)29 NDlib: A Python Library to Model and Analyze Diffusion Processes over Complex Networksby Giulio Rossetti (ISTI-CNR), Letizia Milli (University of Pisa) and Salvatore Rinzivillo (ISTI- CNR)30 Predicting the Spread of COVID-19 through Marine Ecological Niche Modelsby Gianpaolo Coro (ISTI-CNR)32 How an Agent-Based Population Model Became a Key-Element of the Austrian Effort Against COVID-19by Stefan Emrich and Niki Popper (dwh GmbH,TU Wien, DEXHELPP)33 The Case for Uncertainty' in COVID19 Predictionsby Wouter Edeling (CWI) and Daan Crommelin (CWI and University of Amsterdam)35 Social Distancing: The Sensitivity of Numerical Simulationsby 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)36 Using Markovian Models to Simulate Disease Spreadby Stelios Zimeras (University of the Aegean)38 Control Theoretic Approach for COVID-19 Managementby Gábor Szederkényi (Pázmány Péter Catholic University), Tamás Péni (SZTAKI) and Gergely Rost (University of Szeged)40 A Hybrid Predictive Model for Mitigating Health and Economic Factors during a Pandemicby Lisa Veiber, Salah Ghamizi (University of Luxembourg) and Jean-Sébastien Sottet (LIST)RESEARCH AND INNOVATION42 A Multidimensional Method for Capturing Spatial Databy Christian Kollmitzer (AIT Austrian Institute of Technology GmbH), Melanie Schranz (Lakeside Labs GmbH) and Manuel Warum (AIT Austrian Institute of Technology GmbH)44 Digital Seafaring: Digitising, Curating and Exploring Archival Sources of Maritime Historyby Martin Doerr (ICS-FORTH), Pavlos Fafalios (ICS- FORTH) and Apostólos Delis (IMS-FORTH)45 Airborne Reconnaissance in the Field of Natural Hazard Management and Public Safetyby Alexander Preinerstorfer, Philip Taupe (AIT Austrian Institute of Technology GmbH) and Christoph Hochwarter (IFES Institut fur empirische Sozialforschung GmbH)ANNOUNCEMENTS41 SAFECOMP 202147 Dagstuhl Seminars and Perspectives Workshops47 CNR-Inria Team Wins Gold Medals at the RERS 2020 Parallel CTL Challenge47 One Million Enrollments in W3Cx48 The GATEKEEPER 1st Open Call is now openERCIM NEWS 124 January 2021 3Special ThemeControl Theoretic Approach for COVID-19 Managementby Gábor Szederkényi (Pázmány Péter Catholic University), Tamás Péni (SZTAKI) and Gergely Rost (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.The COVID-19 pandemic is one of the biggest challenges the world is currently facing. Until a vaccine and effective treatm ent are availab le , carefully  planned measures are needed in every country to control the spread of the dis­ease. Choosing the right management policy is a sensitive task that requires several potentially contradicting objec­tives to be considered. The most impor­tant limiting constraint is the capacity of the healthcare system, which can easily be overwhelmed if the spread of the dis­ease is not controlled. It is clear that the transmission of the virus can be effi­ciently slowed down by appropriate restrictions (social distancing, lock- down), but these measures have nega­tive social and economic impacts that we can ’t afford to overlook. At the moment, governments are continuously evaluating their control measures, trying to find a balance between public health concerns and the costs of social dis­tancing measures. This paper shows that control theory provides an appropriate framework for the support of decision­making through the systematic design of optimal management strategies.A mathematical model of the epidemic spreadThe computation of the control policy requires a m athem atical model describing the relationship between important time-dependent quantities and capable of predicting the future behav­iour of the epidemic. The most common approach is to use com partm ental models [1] for this purpose. In this mod­elling framework the total population is divided into groups (compartments) such that each compartment collects individ­uals of the same infection status. One possible grouping is obtained by intro­ducing the following compartments [2]: Susceptible (S) collects individuals who can be infected ; Latent (L) contains those who have already contracted the disease, but do not show symptoms and are not infectious yet. Individuals who have just recently been infected and needa few more days to develop symptoms are collected in class Pre-symptomatic (P). Depending on whether or not an infected individual develops symptoms he/she belongs to the com partm ent Sym ptom atic infected  (I) or Asymptomatic infected (A). Three addi­tional groups are defined for the Hospitalised (H), Recovered (R) and Deceased (D) individuals. The transition diagram representing the interconnec­tions betw een the com partm ents is depicted in Figure 1. The transmission rates are given in the labels o f the arrows. The model depends on several parameters (a,/3,p, etc.) which can be determined and continuously updated by following the current literature and analysing the data registered worldwide on the active COVID-19 cases (e.g., LI).Formulating COVID management as a control design task In a control theory framework, dynam­ical systems are considered as operators mapping from an input signal (function) space to an output space. We distinguish between manipulable inputs which can be set (often between certain limits) by the user and disturbance inputs from the environment that cannot be directly influenced. The outputs are either directly measured quantities or they are com puted from m easurem ents. The inner variables representing the actual status of the model are the states. The contro l goals can be prescribed bydefining constraints and optimality cri­teria for the predicted future behaviour of the system. Possible examples for the former are (physical) bounds on the inputs and/or on the state variables and minimal control costs or operation time for the latter. Therefore, a complex con­trol problem can be expressed in the form of constrained optimisation [2]. In the compartmental model introduced above the control input is the scaling factor o f coefficient /? determ ining in fec tion  probability . By applying restrictions of varying stringency index (from mandatory mask wearing through closing of different institutions and lim­iting public gatherings to total lock- down) this factor can be varied between well-defined limits. Assuming that the number of hospitalised and deceased individuals can be reliably documented,these two quantities are chosen for out­puts. The main goals of epidemic man­agement, such as protecting the health­care system and applying less stringent interventions to avoid social and eco­nomic crisis, can be form alised by defining a strict upper lim it for the number o f hospitalised individuals (e.g., H<Hmax) and adding the control cost to the optimality criteria.State estimationIn order to use the model to predict the future behaviour of the epidemic, infor­mation is needed about the non-meas- ured compartments. The state variablesFigure 1: Transition diagram o f the compartmental model describing the transmission dynamics o f COVID-19.3 8 ERCIM NEWS 124 January 2021. io4 Number of individuals in the compartmentsZooming on hospitalized patientsFigure 2: Simulation results obtained by a predictive controller computed by constrained optimisation. The goal is to mitigate the effect o f  the epidemic and protect the functionality o f  the healthcare system by taking less stringent measures. The limitation o f the healthcare system is modelled by specifying two upper bounds IlJ/Jx and H/JJX with lfJ/Jx < flJt2Jx fo r the number o f hospitalised patients (H). The primary goal is to keep H  under l l j / f -  I f  this is not possible, this limit can be exceeded, but only up to /  i j f f  and only fo r a given time period. The control input can vary between “no-interventio”‘ and “total lockdown”*. It can be seen that the required control goal can be achieved by applying strict measures at the very beginning o f  the epidemic and systematically easing the restrictions thereafter. Together with the control input, the bottom figure depicts the time dependent replication number (Rc) as well.corresponding to these have to be esti­mated from the past measurements and the applied control actions using the nonlinear compartmental model.Links:[LI] COVID-NET: A weekly summary of US COVID-19 Hospitalization Data: https://kwz.me/h2XPlease contact:Tamás Páni SZTAKI, Hungary peni@sztaki.huIllustrative resultsFigure 2 presents a simulation result obtained by perform ing the control design concept above for the Hungarian situation. In this specific scenario, we assumed that the capacity of the health­care system (Hmax) can be temporarily exceeded if needed, but only for a short time and by only a specific amount. This scenario models the actual, real sit­uation, when there is an extra, but lim­ited and possibly costly reserve in the healthcare system that can be activated if necessary.This research was partially supported by the Artificial Intelligence Hungarian National Laboratory, https://milab.hu/References:[1] S. V. Rakovic, W. S. Levine (editors): "Handbook of Model Predictive Control”, Birkhauser 2019.[2] F. Brauer, C. Castillo-Chavez, Z. Feng (editors): “Mathematical Models in Epidemiology”, Texts in Applied Mathematics, 69, 1st ed. 2019.[3] T. Péni, B. Csutak, G. Szederkényi, G. Rost: "Nonlinear model predictive control with logic constraints for COVID-19 management”. Nonlinear Dynamics, 2020.ERCIM NEWS 124 January 2021 3 9

Control Theoretic Approach for COVID-19 Management

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Control Theoretic Approach for COVID-19 Management

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