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Big Data against the Norovirus
Sudden nausea, violent vomiting, diarrhoea: Noroviruses are responsible for the majority of gastrointestinal infections (gastroenteritis). They are highly infectious and can cause acute gastroenteritis outbreaks in community facilities, hospitals, retirement homes or on cruise ships. Noroviruses are particularly dangerous for immunocompromised patients, for example after organ transplantation, as they can establish chronic infections and trigger complications.
To date, there is no vaccine or specific therapy to prevent or treat norovirus infections. It is also not known what constitutes the individual differences in susceptibility and in the course of infection. In the research project PRESENt, scientists from TWINCORE, the Hannover Medical School, the Helmholtz Centre for Infection Research and the L3S will jointly search for the causes of these variations at the Centre for Individualised Infection Medicine, which is currently being established in Hanover. In clinical studies, PRESENT researchers will evaluate over 5000 patient records and use big data analysis methods to uncover critical factors for the development of complications from norovirus infection. The aim is to accurately and quickly predict the susceptibility of each individual patient to complicated and severe norovirus diseases. The scientists compare clinical data and individual parameters such as gender, age and pre-existing conditions and calculate patterns for the course of infection. In the next step, the consortium will test the efficacy of disinfectants against patient-isolated norovirus samples. Finally, the researchers are evaluating microbial data to determine whether probiotic therapy can prevent severe norovirus infections and cure chronic infections. Thus PRESENt should not only reveal individual differences in the course of norovirus infection, but above all enable a personalized prognosis, prevention and treatment of severe norovirus progressions.
Dr. Megha Khosla
Megha Kosla is a research assistant at L3S. She researches machine-learning methods for the analysis of complex data such as social networks, web graphs and biomedical data.