Incidência de infecções virais das vias aeríferas superiores em crianças e seu estudo por meio de um modelo matemático

Abstract

Acute respiratory infections, especially upper respiratory tract infections (URTI), are the most frequent causes of infantile morbidity in the world. Day-care facilities are closed, with great circulation of people and infectious agents as well, being therefore prone to the spreading of viral respiratory infections. Mathematical epidemic models are quantitative analysis methods that might be used for understanding and predicting the transmission dynamics of infectious diseases. Objective: Verify the monthly incidence of URTI, of 8 respiratory viruses, and to simulate a mathematical model, evaluating its qualitative and quantitative behavior regarding true data from URTI in school of infantile education in integral period children. Casuistic and Methods: From July 2003 to July 2004, all children (173) in the school of infantile education in integral period were followed from 1.6 to 12 months. Them presenting signs of respiratory infections were examined and their nasopharyngeal aspirate specimen was collected, in a total of 255 analyses. Soon after, specific multiplex trial of reverse transcription, followed by the polymerase chain reaction (multiplex RT-PCR), was accomplished for identification of the 8 viruses related to respiratory infections. Results and Conclusions: The average incidence of URTI was 2.33 episodes per child-year. URTI was observed throughout the year of study, especially in the fall and winter, lowering during spring and presenting few cases in summer. Rhinovirus presented the greatest incidence, being observed throughout the period of study. Influenza B, respiratory syncytial virus (RSV), and metapneumovirus presented lower incidence, especially during fall and winter. URTI caused by other analyzed viruses - influenza A, parainfluenza 1, 2, and 3 were rare. The evaluation of the mathematical model through simulations has provided promising results, as it was possible to get true data reproduction. The model is promising. Having its suppositions adequate, it might be useful for understanding the dynamics and spreading of diseases, planning and evaluating prevention and immunization strategies in epidemics.