What is the issue?
Infections with the Respiratory Syncytial Virus (RSV) occur worldwide and are seasonally more frequent during the winter months. By the end of the second year of life, almost all children had contact with the pathogen at least once. In most cases, the infection of small children leads to a mild cold that heals untreated. However, in about 1% of children, the progression of the disease leads to complications that result in inflammation of the upper and lower respiratory tract. These children are hospitalized and may need to be ventilated, in rare cases the infection can be fatal.
Currently there are no active vaccines available to protect against infection with the virus. Passive immunization with a virus-neutralizing antibody is offered to children who are at high risk for a severe infection. These risk groups include, for example, premature babies who will be protected from RSV infection in their first year of life by the antibody. However, even children without known risk factors repeatedly develop severe RSV infections. The factors that cause such severe RSV infections are not fully understood. Seriously ill children are treated symptomatically, since a causal, directly antiviral therapy is not available. Research is therefore concentrating on the development of new therapies and an active vaccine. Thus, the identification of risk factors contributing to the development of an infection should help to provide optimal protection for susceptible children and to find starting points for a therapy.
How do we reach our goals?
To achieve this goal, we have developed a multidisciplinary research approach that brings together physicians, researchers and computer experts. The basis of the research project is the development of a complete integration of clinical, molecular, genetic and metabolic data in order to gain a deeper understanding of the infection process. By using state-of-the-art computer-based methods (artificial intelligence and machine learning), this complex multidimensional information will be processed to unlock the individual determinants that contribute to the onset of infection.
The project builds on clinical data obtained from anonymous seasonal patients affected by RSV. The initial data originate from pre-existing cohorts such as the IRIS cohort and data from the cluster of excellence RESIST. For the season 2019 - 2020 and 2020 – 2021, new cohorts will be generated for the INDIRA project and the generated data will produce the main body of the project analysis.
For the respective cohorts, the analysis will start from the clinical data acquired for each patient and their integration with genomic and metabolic analyses. In parallel, in vitro studies will be carried out that will generate additional genetic and metabolic information on the RSV infection. Finally, the data shall be analyzed and integrated through calculation procedures based on artificial intelligence and machine learning.
TRAIN (Translational Alliance in Lower Saxony), CiiM (Centre for individualized infection Medicine), MHH (Medical School Hannover), HZI (Helmholtz Centre for Infection Research),TU-BS (Technical University Braunschweig), Leibniz Institute DSMZ (German Culture Collection of Microorganisms and Cell Cultures), TWINCORE – Centre for Experimental and Clinical Infection Research, LUH (Leibniz University Hannover)
- The INDIRA consortium met for a kick off meeting in 2019 and an annual meeting in 2020. Between the two events, there were meetings among the working groups (Machine Learning, Visual analytics, Proof of Concept and Replica cohort) to organize and coordinate the ongoing analysis.