Research Luxembourg: Results second FNR COVID-19 Call

According to the World Health Organization (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 center 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 popularization 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 behavior 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.

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.

Emergency services receive a large number of calls during an epidemic. Some calls are from people who are critical cases and require a rapid intervention. However, we have identified that the volume of calls and complexity of remote diagnosis may lead to delays or failure in diagnosis. We will develop a classification system for health status based on the voice and cough patterns of the caller. Respiratory conditions, such as dry cough, sore throat, excessively breathy voice and dyspnea, caused by Covid-19, can make patients’ voices distinctive, creating identifiable voice signatures, that may be discovered using the proposed system. CDCVA project will provide a way for health professionals to improve remote diagnosis while also minimising contact between medical staff and patients.

Knowledge Graphs (KG) or, more generally speaking, Knowledge bases implemented using Semantic Web technologies provide the basics to represent domain knowledge in a machine interpretable manner. This allows computers to consume this knowledge for various purposes (e.g. information retrieval, knowledge discovery, decision support…). Because of their properties, KG are currently used to formalize knowledge about Covid 19 to provide a deep understanding of the state of knowledge about the disease at a given moment in time (e.g. CORD 19, Lens Covid 19 datasets, John Hopkins university datasets). However, only the most recent state of knowledge is retained in these graphs and the history of the disease (i.e. how our knowledge about the disease has evolved) is rarely preserved. In consequence, crucial information about the disease is lost. As a direct consequence, past knowledge described with outdated terminology/concepts can hardly be retrieved which is the case of scientific literature indexed with an outdated ontology. This phenomenon also impact patient data that cannot be retrieved because annotated with an older version of the knowledge graph and, not identified at recruitment time for clinical trials. In this project we will construct an historical knowledge graph about the Covid 19 disease based on existing knowledge graph (cf COVlit project at LCSB) and corpora of scientific articles.

Researchers around the world are in a race against the clock to find treatments, vaccines and diagnosis methods for COVID19. They can succeed only if they share and remain abreast of the latest developments concerning COVID19. A major hurdle lies in the necessity to digest an abundant scientific literature which grows by the day. Exploring large corpora comprising tens of thousands of publications about COVID19 is practically infeasible without appropriate visual text analytics software, combining the power of text mining algorithms with the flexibility of interactive visualizations. The present proposal combines two existing software assets from LIST and LCSB to address this problem. Papyrus is a web-based fully operational corpus visualization software providing an interactive visual map of topics extracted automatically without prior knowledge of the corpus. Unlike search engines or faceted search, Papyrus provides a user-friendly overview of the corpus and full drill-down capabilities into topics of interest. The user is typically able to home in on a few articles, usually 2-10 articles, addressing a specific question, within a couple of clicks. Going beyond the simple use of the MESH ontology currently achieved in Papyrus, we will reuse the BioKB software of LCSB to annotate the textual content of the corpora at hand. Medical researchers using Papyrus will be able to better distinguish the names of genes, proteins, chemicals, living organisms etc. thanks to this new synergy. Another major benefit of BioKB consists in the extraction of biologically meaningful relationships between bioentities occurring in the same sentence. We will enrich these relationships with non-biological entities encountered in the text, like geographic locations, author names, research institutions, the papers in which they are mentioned etc. We will support the exploratory analysis of such multiway relationships by combining tensor coclustering methods with interactive multilayer network visualizations. We would like to evaluate the usefulness of this approach for the discovery of subtle information by COVID19 researchers. The result of this integration will be accessible freely online at https://colibri.list.lu/ and will be advertised on COVID19 resource repositories to support researchers around the world and collect their feedback.

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 prioritize and schedule the re-opening of major Luxembourg’s employers?” or “which global exit strategies guarantee that the hospitalization rate never exceeds 25% of available beds?”

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 Organization 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 visualization or tracking of topics over time. Since data underpins the warehouse, the methodology of DEEPHOUSE is transferable to other diseases.

As of May 10, 2020, worldwide, over 4.15 million people have been diagnosed and over 282,000 deaths with ongoing deadly coronavirus disease-19 (COVID-19) caused by SARS coronavirus 2 (SARS-CoV-2). Currently, there are no approved treatments against COVID-19. Vaccines are under development, but they will be available at least in one year. We need to find quickly new drug targets by fueling basic research projects on SARS- CoV-2 infectivity. A fast track approach would be to reuse already approved drugs that have been broadly tested through multiple clinical trials. Virophagy is a catabolic autophagic pathway used by mammalian cells to destroy viruses. Viruses have now acquired the capability to repurpose autophagy (block or activate) for their lifecycle and pathogenesis. Coronaviruses hijack and fine tune multiple steps of virophagy to escape destruction, increase replication and release from infected cells. Autophagy inhibitors and old anti-malarial drugs, Chloroquine (CQ) and Hydroxychloroquine (HCQ) are creating a lot of excitement in the fight against COVID-19. However, clinical data on CQ and HCQ against COVID-19 is still very limited and inconclusive. At the basic research level, our CIAO-COVID-19 project will investigate the impact of different druggable steps of autophagy on SARS-CoV-2 pseudovirus entry into human airway epithelial cells. At the clinical level, this study aims to compare antiviral efficacy of different potent, selective and specific autophagy inhibitors against COVID-19 infection in vitro. We believe that the knowledge generated during this project will provide important clues for the control and treatment of COVID-19.

The body’s production of specific antibodies is the defense mechanism by which we derive immunity to a pathogen, like SARS-CoV-2 (COVID-19). Tracking of the specific antibodies which patients develop against SARS-CoV-2 will allow a deeper understanding of this pandemic and a formation of an exit strategy. Antibodies can identify different parts or proteins of a pathogen known as antigens, with the recognition of different antigen sites leading to varying immune responses. Most current tests do not distinguish which antigens antibodies are recognizing and rely on recognition of antigens in viral lysate or a set of proteins containing the predominant neutralizing antigens. Using the additional information coded in the different recognition sites of the antibodies will provide a clearer understanding of the host immune system’s response to the infection. This information will allow the characterization of patients further based on their antibody response profile. By including the immune response to other common coronavirus strains the test will identify potential false-positive classifications and potential cross-reactivity benefits. Within our project, we will develop a new antigen array allowing a parallelized approach covering the SARS-CoV-2 virus as well as other coronavirus variants such as those ascribed to the common cold. Through the use of antigen arrays, we can report antibody response profiles to SARS-CoV-2 providing: 1. A detailed map of the virus proteins, which are recognized by the antibodies. 2. The immunoglobulin isotype prevalence against each antigen and 3. The antibody titer for each immunoglobulin isotype. Such stratification will provide several insights into the reason for the various disease progressions and outcomes that have been observed. A more nuanced evaluation of a cohort may also be pivotal in understanding the homogeneity and potential robustness of long-term immunity to this and future coronavirus strains which may develop.

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 characterize 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.

Infection with SARS-CoV-2, the etiologic agent of the current COVID-19 pandemic, is accompanied by excretion of the virus in the feces. Therefore, we assume that the quantification of SARS CoV-2 in sewage allows the prevalence of infections in the population to be monitored by wastewater-based epidemiology, particularly when clinical trial capacity is limited. The overall objective of the CORONASTEP+ project is to determine to which extent SARS-CoV-2 is present in domestic wastewater and to determine how far wastewater monitoring can be used to track and understand the circulation of SARS-CoV-2 nationally. For this purpose, we need to understand how the faecal shedding parameters (frequency, duration, magnitude) in (a)symptomatic individuals influence the uncertainty and variability of the data collected in wastewater treatment plants, in order to assess the significance of the presence of the SARS-CoV-2 in wastewater. Moreover, knowing the infectious hazard of SARS-CoV-2 containing sewage is of critical importance. To achieve these goals, some specific methodological objectives must be met, such as the establishment and the validation of a concentration procedure adapted to enveloped viruses, the set-up of quantitative RT-PCR assays, the implementation of an infectivity assay using cell culture as well as a high throughput sequencing approach to solve the viral strains diversity and the emergence of new variants. The data collected during this study from a set of more than 100 samples could help policy-makers surround the advancement or setback of social distancing and quarantine efforts on the basis of prevalence estimates at the level of treatment plants.

The current pandemic caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) is a threat not only to global health but also to the economy since it is dramatically affecting the socio-economic layers of societies around the planet. Thus, it will be critically important to advance in the production of an effective vaccine, which will be crucial for building up sufficient population-based immunity to efficiently curb the pandemic. While SARS-CoV-2 can infect everyone at all ages, it is mainly the elderly population that is most at risk of a fatal outcome. Elderly persons are characterized by a declining immune function, known as immuno-aging. To address this specific challenge, vaccines against SARS-CoV-2 that specifically protect not only younger people but also the elderly are urgently needed. We propose a project for vaccine development against SARS-CoV-2 based on expression of recombinant SARS-CoV-2 antigenic variants in a novel trimeric form and combine the antigen delivery with the adjuvant activity of CpG oligodinucleotides. CpG adjuvant is known as an already FDA-approved vaccine adjuvant that confer strong immune responses in young and elderly persons. After 6 months, we will deliver an optimized lead antigen vaccine candidate that will already be pre-clinically evaluated for robust induction of virus-neutralizing immunity in mice.

The outbreak of SARS-CoV-2 infections has underlined the critical value of laboratory diagnosis in order to quickly identify infected-patients, to provide optimized care, and to limit coronavirus transmission. Several real-time RT-PCR diagnostic assays were rapidly developped, in particular by pharmaceutical companies which were Conformité Européenne (CE) marked. However, recent data suggest a poor reliability of the diagnostic results that may result from improper or insufficient sample collection, sub-optimal real-time PCR conditions, or personnel operation. The World Health Organization has already warned on PCR assay compatibility that could change over time in the long run. This is particularly the case for commercially manufactured kits, which may be less likely to have published primer/probe sequences. Since all countries are currently facing reagent shortages that limit the testing capacity, several real-time PCR assays were established to counteract the COVID-19 outbreak by using different techniques and suppliers in Luxembourg. Few of them have been fully evaluated or compared to each other by independent laboratories, especially in various respiratory sample matrice or in alternative sample types such as stool, urine or serum. In addition, minimal handling of the samples will be required to ensure the diagnostic of large cohorts of patients. Optimized Real-Time PCR methods without RNA extraction need therefore to be set-up and validated. As a response to the currently ongoing pandemic spread, the present COMPARE proposal, which is embedded in WP04 “Diagnostic capacity and large-scale testing strategies for Luxembourg” of the COVID-19 Task Force of Research Luxembourg , aims to 1) evaluate and compare three real-time PCR techniques established in Luxembourg in terms of sensitivity, specificity, accuracy, and precision 2) evaluate RNA extraction and RT-PCRs for alternative samples considered in diagnostic and research settings 3) develop and validate an assay without RNA extraction to test large cohorts of patients such as for the the Convince and Predi-Covid studies performed in Luxembourg. The results of this study will provide evidence to the national public health authorities whether the techniques used in the country provide similar technical performances and comparable results as well as to to provide technical evidence for alternative large scale testing strategies.

Preferences, attitudes, and well-being affect people’s economic decisions and their effective adherence to policies. Arguably, the COVID-19 pandemics changed these factors, but we do not know the direction of the changes nor how permanent they are. Today more than ever, decision makers need timely information on these factors to effectively introduce policies to exit from the crisis, promote economic recovery, and support social cohesion. This project addresses this urgent need by informing about how people’s preferences, attitudes, and well-being changed during the COVID-19 crisis, and whether such changes are permanent or transitory. We use sentiment analysis on data sourced from Twitter to provide real-time tracking of the social change triggered by the pandemics. In this way we aim to provide timely information and to avoid delays typical of conventional large scale surveys. We will track the changes of life satisfaction, mental stress, trust in others and in institutions, loneliness, uncertainty about the future, populism, and attitudes towards globalization. The project will produce a time-series database for each of these aspects in Luxembourg and 5 European countries severely affected by the crisis (Italy, France, Germany, Spain, and United Kingdom). The data will go from December 2019 to December 2020. We believe that timely data releases produced by our project will be a useful tool to inform economic and social policies, and an important contribution to public and scientific debate at European level.

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.

Lockdowns and the economic crisis induced by Covid-19 are imposing unprecedented constraints on families in terms of freedom of choice, consumption opportunities, time use, and social interactions. The “Farewell-to-C19” project focuses on the role of the family as a place that can both buffer and amplify the shockwave. For example, a spouse that already had a higher earnings before the crisis will (in most cases) continue to work more, and absorb less of the increase in the unpaid workload due to childcare, likely amplifying gender-specific sharing rules and inequalities within the household – a factor that has been shown to affect the well-being of its members (Peluso and Trannoy 2007, Couprie et al. 2010). However, compared to singles, the family plays a natural inequality-reducing role due to the insurance possibilities offered by multiple income sources or consumption and time sharing. Looking across households, people most likely to be working from home were already better off, and children of already better-off households suffer less in terms of loss of human capital during the lockdown and are less exposed to material deprivation. To investigate how these interlaced effects will impact Luxemburgish households, the Farewell-to-C19 project will be developed by a team of researchers belonging to the Living Conditions, Labor Market and Urban Development and Mobility Departments of LISER, in collaboration with the University of Glasgow and the AMSE Marseille. This project is organized in three work packages (WP): The first WP compares different types of households to identify how individual preferences can be affected by family ties in the circumstances induced by the Covid-19 crisis. The second WP analyses several effects of the Covid-19 crisis on children conditions. The third WP focuses on preferences towards redistribution, their development within the family, and their transmission to children.

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.

“Strategies adopted by authorities to counteract the diffusion of the Covid-19 are largely based on social distancing measures, and on tracing the contacts of infected individuals. The ability to quickly identify those exposed to the virus has been widely recognized as a key element in Singapore’s and Taiwan’s successful epidemic containment. Among tracing strategies, the use of tracking applications (“apps”) – software installed on mobile devices – has become prominent. Several countries are resorting to contact-tracing apps as crucial elements of their strategies to gradually ease lockdowns. This issue is also debated and studied in Luxembourg. Several smartphone tracking apps have been proposed to detect coronavirus exposure (O’Neill et al,.2020). By tracking users’ contacts within a certain period of time, the apps enable quick identification and notification of Covid-19 exposure to all those users who have come in contact with a carrier. The use of tracking apps does not come without drawbacks. Concerns have been raised on data gathering, sharing, and use. Even if it were mandatory, a strategy based on tracking apps requires a substantial level of acceptance and adherence in the population to be effective. Recent simulation studies suggest that the epidemic could be stopped if approximately 60% of the adult population adopted a contact tracing app. For the time being, little is known about the propensity of Luxembourgish residents to accept and adopt a tracing app, or regarding their concerns. The project aims to investigate the current propensity of Luxembourgish residents to adopt COVID tracing app, as well as people’s main concerns and how they change over time. Understanding the key barriers and facilitators associated with use of contact tracing applications may inform the public debate and help the design of better apps. The project will contribute to the ongoing cross-country study in Germany, France, UK, USA and Italy (Altmann et al. 2020, available here https://osf.io/n7w48/ ). Additionally, this research will address some of the limitations of the current studies, namely representativeness and the possibility to follow respondents over time using a newly constructed an online probability-based access panel based on STATEC data.

MODVid is a set of four complementary and interdependent work packages (WP) involving 6 PI’s and about 20 partners from Unilu, STATEC and LISER. Some WP’s aim to inform public decisions during the crisis. They provide estimates of the macroeconomic, distributional, and epidemiological effects of the crisis and of restarting scenarios in Luxembourg. These outputs will be delivered within one to two months. Other WP’s aim to inform public decisions in the aftermath of the crisis, in order to predict medium-term effects on the industry and occupational structure of the labor force, on income inequality and poverty, and on the adverse wealth effects for young-mid-aged adults. These medium-term outputs will be delivered within 6 months.

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.

The recent COVID-19 pandemic has shown that rapid information integration is of high importance to control the spatial and temporal spread of unknown deadly diseases across different scales, from local sources of origin to the entire globe. Risk and probability maps can provide a good picture of the spatial and temporal distribution of COVID-19 across local, regional, national and continental areas and can help to control spreads, not overwhelming health emergency infrastructures and allowing for controlled return to our daily lives. First results of the CON-VINCE study shows that only 2 % of the population has been in contact with the virus. Due to low, daily infection rates, the Luxembourgish Government is reopening schools, parts of the economy and societal life. But a second infection wave is likely to occur. Therefore, further testing and surveys are currently implemented through the COVID-19 TaskForce, building on interdisciplinary probabilistic scenarios and the Weizmann COVID-19 pilot study for self reporting of COVID-19 cases to get a better idea about the spatio-temporal distribution across the country. This is of high importance to allow controlled reopenings and a good functioning of logistics chains. The TIGER project will strongly contribute to the COVID-19 TaskForce’s WPs 06, 07 and 13 with a replicable, reproducible and scalable probabilistic approach, implemented into an interoperable geospatial web platform which can be reused by, linked to or integrated into current and future initiatives on infectuous disease spread and control. We propose an interoperable geospatial disease response system, built upon the existing iGuess® software technology platform developed at LIST to: 1) integrate existing high resolution geospatial information on infrastructure, population vulnerability, self reporting, publicly released and TaskForce COVID-19 data; 2) provide tools to analyse and forecast spatio-temporal disease patterns and to optimise spatial sampling to get a better picture on spatial coverage of infected cases and disease spread; 3) provide easy access to tools and generated information for experts and decision makers through secure interoperable web services. We suggest to use geostatistical spatio-temporal methods, such as space-time point-to-area (aggregation) and area-to-point (disaggregation) simulation and Log-Gaussian Cox based simulations to analyse and estimate current and near future status of the pandemic situations. We will use established OGC web service standards which enable the integration of distributed geospatial data from geoportals, OpenStreetMap, social media etc. with COVID-19 observation data, and provide an automated spatio-temporal mapping and prediction system to retrieve optimal information for crisis response support for public health across the entire country. The TIGER project has strong impacts and innovation potential due to its integrated approach combining distributed data, methods and tools to provide spatio-temporal evidence for multiple experts, researchers and stakeholders, to take improved decision for lock-down measures and a controlled reopening of societal life. The platform will provide easy secured access to different experts and researchers within the TaskForce and thus can provide integrated views across different WPs and expert teams. Results can be easily shared through interoperable web services to the different expert teams and the government in forms of maps, tables, graphs and summary statistics at different aggregation levels. Such a replicable, reproducible and scalable response system will help to react more rapid in future outbreaks of infectious diseases and can provide better coordination with neighbouring countries as if well established data standards for sharing are used and further adapted to pandemic outbreaks.