@article{ObermeierHeimBiereetal., author = {Obermeier, Patrick E and Heim, Albert and Biere, Barbara and Hage, Elias and Alchikh, Maren and Conrad, Tim and Schweiger, Brunhilde and Rath, Barbara A}, title = {Linking digital surveillance and in-depth virology to study clinical patterns of viral respiratory infections in vulnerable patient populations}, series = {iScience}, volume = {25}, journal = {iScience}, number = {5}, publisher = {Cell Press}, doi = {10.1016/j.isci.2022.104276}, abstract = {To improve the identification and management of viral respiratory infections, we established a clinical and virologic surveillance program for pediatric patients fulfilling pre-defined case criteria of influenza-like illness and viral respiratory infections. The program resulted in a cohort comprising 6,073 patients (56\% male, median age 1.6 years, range 0-18.8 years), where every patient was assessed with a validated disease severity score at the point-of-care using the ViVI ScoreApp. We used machine learning and agnostic feature selection to identify characteristic clinical patterns. We tested all patients for human adenoviruses, 571 (9\%) were positive. Adenovirus infections were particularly common and mild in children ≥1 month of age but rare and potentially severe in neonates: with lower airway involvement, disseminated disease, and a 50\% mortality rate (n = 2/4). In one fatal case, we discovered a novel virus …}, language = {en} }