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University projects receive additional funding to research COVID-19

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Publié le mercredi 03 juin 2020

The FNR has published the results of its second COVID-19 Fast Track Call. Eight projects from the University of Luxembourg have been retained for funding, representing a financial commitment of 347,300 euros.

This programme to address COVID-19 is based on a fast-track mechanism that allows the support of research projects requiring a quick start during the pandemic. The first FNR COVID-19 Fast Track Call in April 2020 had retained 16 University-led projects for funding.

The University of Luxembourg is heavily involved in the research effort to fight the spread of COVID-19 and is one of the partners in the research activities coordinated by Research Luxembourg, working on multiple interdisciplinary projects.

The research projects led by the University address ICT; biomedical and life sciences; humanities and social sciences; law and economics; as well as mathematics. Below is an overview of the University projects.

Pocket Rehab: Mhealth-based Rehabilitation Program for Patients with Cardiovascular Disease as Prevention and Treatment Strategy for Covid-19 Victims: An International Collaborative Multicentre Research Trial

Principle investigator: Jorge Augusto Meira

According to the World Health Organisation (WHO), Cardiovascular Diseases (CVDs) are responsible for approximately 17 million deaths per year, being the cause number 1 of deaths worldwide and considered the major public health problem. In Europe, CDVs account for 45 % of all deaths. In the context of the COVID-19 pandemic, the CDVs figure in the list of groups of elevated risks. Among the reasons, it is known that the pathophysiological mechanism of the coronavirus SARS-CoV-2 uses the angiotensin converting enzyme 2 (ACE2) as a functional receptor, a surface molecule that is localised on the endothelial cells of arteries and veins, increasing the risk of contamination and the mortality rate CVD population. In countries such as Brazil, according to the epidemiological bulletin of COE-COVID19 of the Ministry of Health, among the 1124 deaths recorded until 11 April 2020, in those under the age of 60, 82.5% had illness associated CVDs. In order to mitigate the risks for these patients, multi professional cardiopulmonary and metabolic rehabilitation program has been presented as an essential therapeutic and preventive strategy, with a positive impact on cardiorespiratory capacity and quality of life, adherence to clinical treatment, impacting on the reduction of cardiovascular disease risk factors and hospital admissions. Traditional cardiac rehabilitation (CR) is usually performed in a single outpatient centre and involves a structured exercise program (usually 3 sessions per week for 36 total sessions) supervised by trained physicians, nurses, and exercise therapists. As an alternative method, home-based CR involves prescribed exercises that can be carried out in a variety of settings and can be delivered “mostly or entirely outside of the traditional CR setting”. Recently, the popularisation of technologies such as internet and mobile phones have enabled Mobile health (mHealth) tools. MHealth can be defined as the use of mobile and wireless technologies to support the achievement of health objectives, such as surveillance, diagnosis, and management of chronic diseases [4]. The use of mHealth interventions has the potential to support successful management of chronic conditions and health behaviour by: (1) improving patient self-monitoring and management, (2) building social networks for patients, (3) informing health care professionals of patients’ health status, (4) providing indirect feedback interactions, (5) tailoring care and education to patient needs, and (6) improving communication among health care professionals. Considering the current and undefined social distance scenario period, and to prevent and treat of CVD population during Covid-19 pandemic, we propose a cardiopulmonary and metabolic rehabilitation program based on mobile technology (mHealth). This program can counteract the new dysfunctions occasioned by this virus and avoid the demand for services face-to-face, dropping contamination and mortality.

Short, Mid-term and Exit Strategies Predictions of the Covid-19 Epidemic in Luxembourg

Principle investigator: Jorge Gonçalves

This project aims to develop mathematical models and predictions of the Covid-19 epidemic in Luxembourg to aid the Luxembourg COVID-19 Task Force in advising the Government. The predictions include the most important parameters to make informed decisions: newly infected, hospital ICU occupancy, use of ventilators and deaths. It will focus on three main categories. First, it considers short-term predictions on a daily basis. Second, it develops models for mid-term exit strategies projections considering the specific Luxembourgish circumstances including demographic, geographic and economic data. Third, build both short and mid-term models for other European countries and translate this information to Luxembourgish models. This will include neighbouring regions to Luxembourg to predict the effects of returning cross border commuters. Key findings will be regularly reported to the Luxembourgish Government.

Pandemic Simulation and Forecasting for an Empowered Policy-making: Convergence of Machine Learning and Epidemiological Models

Principle investigator: Yves Le Traon

Luxembourg is entering the post-lockdown stage of the Covid-19 pandemic. It goes without saying that a key concern is the risk of triggering a new outbreak, due to over-permissive post-lockdown policies. However, understanding the propagation of the pandemic remains challenging, mainly because no existing model can accurately evaluate the individual contributions of the mitigation strategy (border control, school closure, open-air activities, retail activities…) on the reproduction rate of the disease. Moreover, current predictions lean on selected experts’ opinions and on epidemiological models whose parameters are set arbitrarily. This impedes any reliable analysis and scheduling of proper post-lockdown measures. Therefore, the objective of PILOT is to develop a data-driven pandemic simulation and forecasting tools to support policymakers in designing safe and efficient exit strategies. Thereby, they will enable appropriate planning of those measures, allowing policymakers to answer practical questions such as: “How to prioritise and schedule the re-opening of major Luxembourg’s employers?” or “which global exit strategies guarantee that the hospitalisation rate never exceeds 25% of available beds?”

Deep Mining with the Covid-19 Data Warehouse

Principle investigator: Christoph Schommer

In a time where COVID-19 is attracting worldwide attention, the data quantity and variety is increasing dramatically. The result are data lakes, where (raw) data appears in different formats and quality. In the case of COVID-19, the Johns Hopkins University Center for Systems Science and Engineering (JSU-CCSE) has compiled a number of various data sources including data from the World Health Organisation and others, where the published data itself is largely time-series data that covers worldwide mortality rates, infected and recovered cases of the Covid-19 disease for more than 200 countries. The Open Research Dataset Challenge (CORD-19) is a resource of almost 60000 scholarly articles, where more than 75% of these are full text articles. These are only two examples of publicly available data that aims to provide a comprehensible analysis of the entire disease development. The decisive problem here, however, is that the heterogeneity, diversity, and (partially) unstructuredness of data makes a deep analysis more difficult rather than easier. In this view, DEEPHOUSE has two central goals: first, we consolidate the available text data and time series data in a Covid-19 data warehouse, e.g., along multidimensional axes (time, place, and topic) by applying appropriate data integration techniques. Second, we build a web-based platform being extendable, which demonstrates the successful discovery of time-related sequences or time series, for example by visualisation or tracking of topics over time. Since data underpins the warehouse, the methodology of DEEPHOUSE is transferable to other diseases.

Leveraging Systems Biology to Target Hyperinflammation in Critically-ill Covid-19 Patients

Principle investigator: Antonio Del Sol Mesa

The emergence of COVID-19 pandemic implies new challenges for the Health Systems worldwide. A small percentage of the patients require hospitalisation and specialised attention in Intensive Care Units (ICUs). Furthermore, accumulating evidence suggests that a subgroup of patients with severe COVID-19 might have a cytokine storm syndrome. Therefore, the main goal of the proposal is to elucidate the potential role of cytokine storm in COVID-19 disease severity, and to propose novel strategies for counteracting this hyperinflammatory response. In this context, we propose to develop a single-cell based systems biology approach that infers and compares functional cell-cell communication networks of immune cells between patients with mild and severe symptoms to characterise the cytokine storm and, in particular, to identify functionally relevant intercellular positive feedback loops maintaining this hyperinflammatory condition. Indeed, feedback loops have been shown to support the inflammatory response in other infectious diseases. Therefore, we propose that these loops are responsible for maintaining and amplifying the cytokine storm during the COVID-19 infection. As a plausible therapeutic strategy to modulate hyperinflammation, we propose to target these feedback loops by simulating the effect of perturbing receptor-ligand interactions as well as intracellular signaling molecules participating in them. In this regard, an automatic search in databases of clinically approved drugs would identify candidates for specifically disrupting or modulating the functioning of these loops. To carry out this study we will perform a single cell RNA sequencing of blood cells from 16 COVID-19 patients, half of which only showing mild symptoms whereas the others present with severe symptoms needing ICU treatment.

Socio-economic Impacts of Covid-19: Collecting the Data Short- and medium-term (SEI)

Principle investigator: Claus Vögele

Humans are a social species, and their health, life and genetic legacy are threatened by social isolation. Like other animals, humans fare poorly when isolated. The preventive measures to contain the current COVID-19 outbreak limit all forms of physical social contacts to a minimum, more and earlier in some countries than in others. Differences exist also within the same country due to household composition and dwelling location. Social isolation is associated with ill health. In this project we will investigate which factors predict levels of psychological distress and well-being associated with the current social-distancing measures, and which mechanisms mediate this relationship. In doing so, we will look at individual, context, and societal factors. The expected results will inform policies to prevent pandemics of this kind in the future, and provide new results that will help to identify people most at risk to suffer from the negative effects of social confinement measures.

Protection Against Infection Through Regulatory Law

Principle investigator: Stefan Braum

Across Europe, and particularly in Luxembourg, there was no specific legal framework to adequately address the problems of a pandemic. This applies both to the aspect of repression (containment of the virus through administrative and criminal measures) and to the aspect of prevention (tracking systems and health protection). On the one hand, the project examines the question of how the measures associated with the containment of the SARS-Cov-2 pandemic can be applied according to legal criteria and how constitutional normality can be restored. On the other hand, the post-crisis strategy raises the question of what risks to constitutional principles are (still) discernible and how these can be overcome in the long term by a normatively justified legal framework of infection control. The project is therefore designed for the long term (3 years), because it aims to cover an evolutionary arc from taking stock of the existing measures to contain the virus in various European countries, through the evaluation of possible consequences for fundamental rights, to the development of a normative legal framework for infection protection.

An Agent-based Model of Covid-19 in Luxembourg

Principle investigator: James Thompson

The ongoing coronavirus pandemic is the most disruptive global event in modern history. It is of vital importance that we continue to build a rigorous understanding of how the SARS-COV-2 virus spreads within the human population, and predict the impact of interventions. This is especially true given the widely expected possibility of a second wave. This project aims to do so using a sophisticated agent-based model of COVID-19 in Luxembourg, based on real data and accompanied by computer simulations, that incorporates unique features not typically seen in other models. In particular, our model will feature a dynamic sequence of contact networks varying stochastically over time, modelling both permanent contacts within homes, workplaces and schools but also the random mixing of strangers in, for example, restaurants, bars, shops and public transport. We hope that this model will yield accurate predictions that could help shape government policy and save lives. The SARS-COV-2 virus presents the greatest challenge of our time and this project aims to strengthen Luxembourg in its fight against the virus.