The University of Luxembourg is an important pillar in the research effort to fight the spread of COVID-19. It is one of the participating partners in the research activities coordinated by Research Luxembourg, working on multiple interdisciplinary projects.
In a first FNR COVID-19 Fast Track Call, 16 projects from the University of Luxembourg have recently been retained for funding, representing a financial commitment of 780,500 euros. This programme to address COVID-19 is based on a fast-track mechanism that allows the support of research projects requiring an immediate start during the current crisis.
The research projects led by the University address biomedical and life sciences; humanities and social sciences; law and economics; as well as material sciences. Below please find an exhaustive overview of the University projects and their abstracts.
Phylodynamic Real-time Monitoring Of Sars-cov-2 Genomes In Luxembourg (Co-PhyloDyn)
Principal investigator: Patrick May
The current COVID-19 pandemic caused by the SARS-CoV-2 virus is an extreme challenge to mankind’s health affecting currently more than 1.7 million people world-wide. Current approaches to manage this challenge rely on reliable testing as well as the development of vaccination which both depend on the stability of specific sequences of the viral genome. Sequencing of viral genomes from patient samples provides deep insights on pathogen dynamics in terms of phylogeny and evolution allowing for spreading analysis and adaptation of molecular diagnostic and prevention strategies. Hence, the real-time monitoring and automated dynamic analysis of phylogenomic data derived from virus genome sequencing together with available geographical and clinical data in an accessible and portable manner is key to support decisions in research, diagnostics and physiology, which strongly impacts individuals and societies within and outside Luxembourg and is also important as a health monitoring resource for possible similar future events. In the frame of the planned COVID+ study, the Co-PhyloDyn project will lay the foundation of the retrospective in-depth genomic characterisation of COVID-19 positive cases over the COVID-19 pandemic in Luxembourg and its neighbouring countries by implementing an efficient fully automated and reproducible infrastructure for the phylodynamic analysis of COVID-10 in Luxembourg. The close collaboration between the Luxembourg Centre of Systems Biomedicine (LCSB) at the University of Luxembourg and the Department of Microbiology at the Laboratoire National de Santé (LNS) will bring together the needed expertise in genomics, bioinformatics, sequencing and virology to tackle the needed in-time phylogenetic analysis of COVID-19 in Luxembourg.
Multi-omics Evaluation Of Microbial Co-infection As Marker Of Covid-19 Severity (CO-INFECTOMICS)
Principal investigator: Paul Wilmes
Since March 11 2020, the world is facing a large-scale pandemic with the COVID-19 outbreak affecting more than 1.7 million individuals globally. The SARS-CoV-2 fatality rate has been maintained at a low level thanks to countermeasures established in many countries, in particular, physical distancing to limit the infection rate and the enormous effort of nursing staff. Yet there is a major risk of overwhelming health care systems, which would lead to a dramatic increase in morbidity. Several risk factors for developing a severe pathology have been identified to date: sex, age, obesity, diabetes, hypertension and cardiovascular disease. Co-infections have been reported in several Chinese studies, but have not been examined systematically. In the frame of the PREDICOVID study, the CO-INFECTOMICS project aims to understand if apart from SARS-CoV-2, co-infections are a predictive marker of disease severity in COVID-19 positive patients in Luxembourg. To achieve this, our project will compare the taxonomic microbial composition as well as the functional potential and transcriptional activity of the respiratory and intestinal tract microbiota of 60 mild and 60 severe COVID-19 patients with a non-targeted approach. Nucleic acids will be extracted from sputum, nasal/oropharyngeal swabs, and stool samples using in-house established protocols, which will be optimised for bacteria, fungi and for virus detection at once. To examine co-infection or potential co-carriage, metagenomic and metatranscriptomic analyses will be performed using our multi-omic computational pipelines, i.e. the Integrated Meta-Omic Pipeline (IMP) and PathoFact, amongst others. Multi-omic microbial profiles, covering viruses, bacteria, and fungi, will be generated and compared between mild and severe patients. Furthermore, the taxonomic composition as well as the functional potential and transcriptional activity of the upper and lower respiratory tract will be compared, using sputum and nasal/oropharyngeal swabs derived from the same patients, at two time points. The respiratory and intestinal microbial profiles will be associated to the immune response and clinical data to detect links between co-infection and disease severity. CO-INFECTOMICS aims to determine microbial markers which could be used in clinics and, thus, to support the stratification of high-risk patients.
Covid19 Literature Bio-curation, Text-mining And Semantic Web Technologies (COVlit)
Principal investigator: Reinhard Schneider
The world wide scientific response to the COVID-19 pandemic is reflected in the ever-growing scientific literature. Enriching our current knowledge base (https://biokb.lcsb.uni.lu) with these publications requires joint efforts at each stage of the chain of process involved in the text-mining pipeline. This pipeline comprises several challenging tasks such as part-of-speech tagging, entity recognition and normalisation, or event extraction, which are essential to discover relevant knowledge in the form of entities, relations and events. Such knowledge is then made available to the public via semantic web technologies and curated through collaborative curation interfaces. This literature growth calls for updated ontologies to cope with the new terms and biological entities, more robust event extraction and named entity recognition, as well as further development of our collaborative curation interface and search tools. In this project we are building a knowledge base with relevant cross-domain events and relationships from available COVID19 publications can help in-silico and in-vitro researchers navigate the growing COVID-19 literature corpus.
Combined In Silico Molecular Docking And In Vitro Experimental Assessment Of Drug Repurposing Candidates For Covid-19 (CovScreen)
Principal investigator: Enrico Glaab
Currently no vaccine or sufficiently validated pharmacological treatment is available for COVID-19. Drug-based strategies to reduce the viral load in patients with severe forms of COVID-19 include the repurposing of existing small molecule compounds that inhibit the activity of key viral proteins, or human proteins involved in mediating viral entry or release from the host cell. However, so far, the identified small molecule inhibitors for SARS-CoV-2 target proteins studied in -vitro have limitations in terms of either their binding affinity for the target protein, their bioavailability in the lung, known adverse effects, and high manufacturing costs. We therefore propose a combined computational and experimental approach to rank alternative candidate known drugs, antivirals and natural compounds, which are commercially available, inexpensive, and known to be safe in humans. We will focus on assessing , in terms of their ability to selectively bind to and inhibit the SARS-CoV-2 3CL protease (target 1), which is essential for viral replication, or the human protein TMPRSS2 (target 2), which is essential for viral entry into the host cell. For this purpose, we will screen and filter in silico x M~10k compounds using molecular docking and machine learning based lung bioavailability estimations, and conduct molecular dynamics simulations for refined binding affinity estimation of the 100 top-ranked compounds. The top 20 compounds per drug target in terms of predicted binding affinity will be validated experimentally in -vitro and cellular assays. As part of our prior work, we have already identified natural compounds which are safe in humans, reported to inhibit the replication of SARS-CoV (the predecessor82% identical to SARS-CoV-2, from the 2002/2003 outbreak), and for which our molecular docking and binding affinity estimation analyses predict similar inhibitory effects for the ortholog protein from SARS-CoV-2. This will enable us to start quickly with the first validation experiments for assessing ligand-binding, while selecting further candidate compounds in parallel through the computational screening., while conducting computational predictions for further candidate compounds in parallel. In summary, this project will provide a fast experimental validation of drug repurposing candidates for COVID-19 from a computational pre-selection of antivirals, drugs and natural compounds that are inexpensive, have known safety properties and high predicted bioavailability in the lung. These studies would pave the way for a quick progression to follow-up efficacy testing against virus infectivity in collaboration with a BSL-3 certified laboratory.
AI Based Diagnosis Of Covid-19 From Ct/X-ray Imaging (AICovIX+)
Principal investigator: Andreas Husch
The gold standard for Covid-19 diagnosis is RT-PCR for SARS-CoV-2 from the upper airways. This method suffers from decreased accuracy in more severe disease stages affecting the lower airways. False negative PCR tests in severe cases impose a massive risk to the health system, promoting intra-hospital disease spread in the actual pandemic situation. Reports from China indicate false negative PCR from upper airway samples in severe Covid-19 pneumonia in more than 50% of the cases. Therefore, complementary tests for the detection of these dangerous cases are urgently needed. The value of chest CT and/or X-ray has been demonstrated as complementary diagnostics. Luxembourg has already reacted to these needs, and is urgently acquiring four additional CT scanners only for lung CTs. Based on our experience in deep learning for medical imaging our project aims to rapidly provide AI tools to speed-up diagnosis of high-risk cases from medical imaging. Our short-term objective is a an AI tool for fast and accurate discrimination between Covid-19, other pneumonia, or healthy. This information can be key for early treatment as well as for the safety of health workers, hospitals and other patients. A basic version of an AI tool is already in training and aims to be applied as soon as possible during the current pandemic. Additionally, long term research and preparation to future scenarios is planned and will be integrated with diagnostic approaches using other biomarkers for disease staging by aligning with other groups in Luxembourg.
A Unified Web-based Platform For Viral Phylogeny, Proteomics And Genomics Research In Real-time SARS-CoV-2 (UCoVis)
Principal investigator: Aymeric Fouquier D’herouel
We propose the development of an integrated web-based data exploration tool for genomics, proteomics, phylogeny, evolutionary and geographical data on SARS-CoV-2 samples from Luxembourg supporting researchers and physicians to gauge the potential impact of viral evolution on virulence and transmission dynamics but also the efficacy of molecular detection methods. We believe that integrating different data sources and visualisation modes through a graphical web-interface will offer a unique perspective on the pathogen and serve as crucial tools to assess potential imminent risks. Our tool will enable researchers, clinicians and the general public to visualise and study different aspects of the evolutionary dynamics of the virus from most recent datasets using a single interface, reducing the need of cross-referencing and comparing different resources.
Information Diffusion In Twitter During The Covid-19 Pandemic: The Case Of The Greater Region (PandemicGR)
Principal investigator: Jun Pang
The PandemicGR project will address of the challenge of understanding and analysing the information diffusion mechanism in online social media during the COVID-19 pandemic, based on a newly collected and properly anonymised Twitter dataset concentrating on Luxembourg and the greater region. In this project, we aim to (1) achieve in-depth analysis of user engagement and communication patterns during this public health crisis, (2) build a machine learning model to simulate and predict COVID-19 information cascades, and (3) develop an effective classifier to detect misinformation in order to improve information trustworthiness in online social media. The results from the PandemicGR project will have both immediate and medium-term impact for crisis management for bother Luxembourg and the greater region.
Machine Learning To The Rescue: From Health Recovery To Economic Revival (REBORN)
Principal investigator: Jacques Klein
REBORN is a data science project that focuses on the challenge of ensuring sustainable economic recovery in the face of COVID-19. The project team will apply advanced Machine Learning, business ecosystem modelling (i.e., expert knowledge) and simulation techniques to yield recommendations of economic actions given different scenarios in which the lockdown is relaxed, partially or totally lifted. By interacting with other teams of the Luxembourg Task Force, this project targets high impact for the various sectors of the Luxembourg economy, by providing appropriate data-driven recommendations for political decision-makers. We expect that REBORN could answer important questions such as: What are the industrial sectors to help in priority? What are the sectors that should be restarted first? What are the possible changes in consumer habits and the impact of neighboring country decisions on commuters? etc. Ultimately, REBORN contributes towards reflections on initiatives to limit the spread of the future economic crisis due to COVID-19 as well as avoid worsening future waves of coronaviruses.
Privacy Preserving Monitoring Of Social Distancing In Public Environments Machine Learning, Computer Vision, Social Distancing, GDPR By Design (PEOPLE)
Principal investigator: Raphael Frank
The aim of this project is to provide a platform to run a comprehensive analysis on the Social Distancing measures decided by the government in the context of the COVID-19 pandemic. To do so we propose to analyse anonymised video data in the city of Luxembourg. The first step will be to anonymise the video feed by using well known Artificial Intelligence (AI) models (face blurring). In a next step will use other AI models to identify pedestrians and groups of individuals, calculate their relative distances and overall density. Those metrics can then be evaluated over time for different locations and provide valuable insights on the greater or lesser risks of infection spreading based on behaviour. The rules can be used either to inform where the police need to focus their efforts in enforcing rules, or to inform and influence the public’s actions (or both).
Facilitating Optimal Containment And Exit Strategies With Minimal Disclosure Access Control And Tracking (SmartExit)
Principal investigator: Peter Y. A. Ryan
This project aims at facilitating exit strategies that incorporate access control to the public space, border crossings, and critical areas. The strategies are based on the individual COVID-19 immunity and/or infection status. Also, the project will investigate the implementation of contact-tracing apps in Luxembourg, which clearly is an essential component of a successful exit strategy in order to backtrack and contain the infection. The smart access control system can be based on passports, ID cards or smart cards. We will propose a mechanism, produce a prototype implementation, and present a preliminary formal analysis of access control solutions for exit strategies. While it might be necessary to waive users’ privacy in order to efficiently contain the epidemic, we will look for mechanisms that waive it to the least possible extent. In this sense, the focus of the project will be on preserving privacy, unlinkability and GDPR compliance for the access control system. Further, contact-tracing apps with minimal privacy disclosure will be investigated, especially the DP-3T proposal from PEPP-PT.
Correlates Of Resilience In The Context Of Social Isolation In Seniors (CRISIS)
Principal investigator: Isabelle Astrid Albert
In the current COVID-19 crisis, older adults are at particular risk for severe health outcomes and increased mortality. Whereas it is of prime importance to raise public awareness regarding the special risk of older people, and reduced in-person contact is essential to protect vulnerable groups, we have to take into account what effects these measures have on subjective well-being, mental health and further development of older persons. The present research project will tackle the question of how current measures and their communication to the public are experienced by the target group (60+) and will focus on the psychological and behavioural correlates and outcomes. In particular, the following questions will be addressed: 1) How are claims of being a risk group and COVID 19 related ageing stereotypes incorporated in views of self and others and how are they related to psychological and behavioural consequences, e.g. regarding the experience of self-efficacy and agency? 2) How is subjective risk experienced and how do older people commit to protective measures and guidelines, also depending on their self-views? 3) How can the risk for social isolation and loneliness be reduced? This includes the availability of appropriate information and communication channels? 4) What are resilience factors that protect older adults from negative mental health outcomes and help to maintain subjective well-being? Results will inform policies related to controlling the virus and information strategies to ensure compliance with the measures, especially (but not only) in the at-risk population of older adults. Furthermore, the project will contribute knowledge to reduce negative side effects of the preventive measures for older adults’ long-term autonomy, health, and well-being.
Young People And Covid-19 – Social, Economic, And Health Consequences Of Infection Prevention And Control Measures For Young People In Luxembourg (YAC)
Principal investigator: Robin Samuel
Whereas young people have a low risk for severe illness due to COVID-19, they are an important link in the transmission chain and they might find it particularly hard to accept and comply with governmental guidelines and measures to prevent and control the disease. Some of the measures may further interfere disproportionally with their development and result in short- and long-term consequences for education, professional careers, economic situation, psychosocial development, and mental health. Our project will generate the knowledge required to address the current and post-pandemic challenges of the COVID-19 crisis, by (1) focusing on how young people residing in Luxembourg comply with and accept the governmental measures to prevent and control infection with COVID-19 and (2) by identifying the economic, psycho-social, and health consequences of these measures during and after the COVID-19 pandemic in relation to a pre-pandemic baseline we were able to collect in 2019. Inequalities between and within socio-demographic and socio-economic groups in relation to (1) and (2) are of particular interest. To evaluate the situation during and after the pandemic, two intermediate waves (2020 and 2021) are added to the 2019 and 2023/24 waves of the Youth Survey Luxembourg. The Youth Survey Luxembourg is a representative large-scale survey among 16- to 29-year-old residents in Luxembourg, covering all relevant areas outlined above (e.g., education and employment, social situation, health, etc.). COVID-19-specific modules, for example on compliance with measures, will be included to provide vital information for the control of the COVID-19 pandemic. Statistical comparisons of recent data with pre-pandemic baselines will enable a broad and thorough assessment of the short-term, mid-term, and long-term impacts of the COVID-19 crisis on the lives of young people. Results will be made available to the public and the research community on a rolling basis. Furthermore, the evidence generated will inform policy-making in Luxembourg and contribute to address various key research priorities identified by the WHO.
History In The Making: #Covidmemory (COMEM)
Principal investigator: Stefan Krebs
The COVID-19 pandemic is a global crisis, but its local impact on Luxembourg will be determined in part by how people memorialise its effects in the moment, and how they will remember it later. Luxembourg declared a state of emergency on 18 March which dramatically reshaped public and private life. Schools, businesses and shops are closed. Students stay home and learn online. New rules govern home workers and cross-border commuters. With no end yet in sight, it is already clear that we are experiencing an extraordinary moment in the collective memory of Luxembourg. With the online platform #covidmemory, the Luxembourg Centre for Contemporary and Digital History (C2DH) at the University of Luxembourg will offer people living or working in Luxembourg the opportunity to share their personal experiences with one another and to archive them for future generations. Contributors can post their photos, videos or stories on an open web-based platform from their personal computers or mobile devices. A team of reviewers and curators will oversee the website in the coming weeks and months. The #covidmemory project is inspired by the “Rapid Response Collecting” approach that has been used in public history and museum circles as a way to collect the stories, material culture, digital creations and ephemera of historical events as Hurricane Katrina, 9/11 and the 2015 terrorist attacks in France.
Legally Fighting Covid-19 (LEGAFIGHT)
Principal investigator: Elise Poillot
The LEGAFIGHT project proposes a strategic assessment of the existing legal framework to be respected both at the EU and domestic level (compliance with the GDPR) to fight the spreading of the virus through tracking applications. It will also consider the already existing legal measures taken by foreign legislatures. It aims, as a first step, to provide the Luxembourg with a specifically socially and ethically tailored legal regime for tracking applications, leading to the drafting of a legislation, which could be proposed, as a second step, as a European and global model system in the frame of a longer-term project of epidemiological data management.
Nonperforming Mortgage Loans In Luxembourg And The EU After Covid-19 (COVID-19-MORTGAGE)
Princi palinvestigator: Christos Koulovatianos
Luxembourg is one of the countries with the highest household leveraging on private debt and mortgage loans. For ensuring financial stability in Luxembourg after the income disruption due to the COVID-19 lockdowns, it is crucial to examine if the COVID-19 shock caused a critical increase in the number of households that have not been able to service their mortgages. An additional crucial aspect of the problem is to study the extent to which banks in Luxembourg and the EU face a risk of increased nonperforming loans. The ability to detect threats of nonperforming loans to banks in a timely manner allows for developing policies that can tackle the financial instability problem. The project will have two parts. The first part will focus on collecting and analysing data before and after the COVID-19 crisis. The second part will develop simulated household-finance models that can investigate a number of out-of-sample questions and policy questions. The central evaluation question is to predict how income losses due to the COVID-19 lockdown can influence nonperforming loans and how nonperforming loans can cause pressure on the banking system. Because Luxembourg is a very open economy having its banking system exposed banking-sector risks in Europe, studying this question problem both in Luxembourg and in other EU countries, is necessary and insightful. The policy questions involve studying whether, (i) extraordinary aid to households for meeting their mortgage responsibilities and (ii) aid to banks for tackling their nonperforming-loans problems, can avoid bank/household insolvency and a possible collapse in the housing market. In order to ensure that up-to-date post-COVID-19 data can be collected, a team from the Dept. of Finance, U Luxembourg, will collaborate with researchers from STATEC and the Austrian Institute of Economic Research (Wifo), Vienna. Beyond data collection and analysis, computer simulations of household-finance models will guide predictions and out-of-sample policy analysis.
Virus-surface Interactions In Dynamic Environments (V-SIDE)
Principal investigator: Anupam Sengupta
Since the recent outbreak of the COVID-19 pandemic, a growing body of scientific literature has reported on the pre-infection viability and stability of the SARS-CoV-2 virus, qualitatively indicating the role of physical surfaces in transmission of the virus. The nature of the surfaces (e.g., paper, plastics, glass, or metals) and their properties (smooth v/s rough), are believed to play a critical role in determining the viability during the pre-infection phase. Virus-surface interactions are inherently physical in nature wherein dynamics of the environmental parameters (variations in temperature or humidity over time) could underpin the viral viability. Here, using non-pathogenic mutant of SARS-CoV-2 virus as our model organism, we will uncover key physical parameters that underpin viability of SARS-CoV-2 species on a range of commonly used surfaces. Enzyme-linked immunosorbent assay (ELISA) for S proteins, the material forming ‘spikes’ of the coronavirus will be used to test the surface-specific viability, and complemented by flow virometry and high resolution visualisation of the chosen material surfaces (using AFM, SEM and fluorescence imaging), providing quantitative estimation of viral viability. Crucially, time series analysis of viability and concomitant changes in the chemical signature of the material surfaces (FTIR method), will give us unprecedented insights into the mechanistic underpinnings of surface-specific viability of coronavirus, under steady state and dynamic changes in temperature and humidity parameters. The data obtained from this three-pronged approach will be used to biophysically model the system, with a long-term goal of incorporating machine learning methods to identify surface-specific viabilities of virus species. In summary, by revealing the fundamental biophysics of virus-surface interactions, V-SIDE will be in a robust position to develop an integrative mechanistic framework for SARS-Cov-2 viability on various material surfaces. V-SIDE benefits from an active follow up strategy, wherein the overarching goal will be to develop generic recipes for tailoring anti-viral surfaces in a scalable and facile manner, equipping us ultimately to better tackle the recurring incidence of novel viral pandemics.