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Understanding etiological role and epidemiological profile is needed to improve clinical management and prevention of acute respiratory infections (ARIs). A 5-year prospective study about active surveillance for outpatients and inpatients with ARIs was conducted in Gansu province, China, from January 2011 to November 2015. Respiratory specimens were collected from patients and tested for eight respiratory viruses using polymerase chain reaction (PCR) or reverse transcription polymerase chain reaction (RT-PCR). In this study, 2768 eligible patients with median age of 43 years were enrolled including pneumonia (1368, 49.2%), bronchitis (435, 15.7%), upper respiratory tract infection or URTI (250, 9.0%), and unclassified ARI (715, 25.8%). Overall, 29.2% (808/2768) were positive for any one of eight viruses, of whom 130 cases were identified with two or more viruses. Human rhinovirus (HRV) showed the highest detection rate (8.6%), followed by influenza virus (Flu, 7.3%), respiratory syncytial virus (RSV, 6.1%), human coronavirus (hCoV, 4.3%), human parainfluenza (PIV, 4.0%), adenovirus (ADV, 2.1%), human metapneumovirus (hMPV, 1.6%), and human bocavirus (hBoV, 0.7%). Compared with URTI, RSV was more likely identified in pneumonia (χ = 12.720, P < 0.001) and hCoV was more commonly associated with bronchitis than pneumonia (χ = 15.019, P < 0.001). In patients aged less than 5 years, RSV showed the highest detection rate and hCoV was the most frequent virus detected in adults and elderly. The clear epidemical seasons were observed in HRV, Flu, and hCoV infections. These findings could serve as a reference for local health authorities in drawing up further plans to prevent and control ARIs associated with viral etiologies.

The antigenic variability of influenza viruses has always made influenza vaccine development challenging. The punctuated nature of antigenic drift of influenza virus suggests that a relatively small number of genetic changes or combinations of genetic changes may drive changes in antigenic phenotype. The present study aimed to identify antigenicity-associated sites in the hemagglutinin protein of A/H1N1 seasonal influenza virus using computational approaches. Random Forest Regression (RFR) and Support Vector Regression based on Recursive Feature Elimination (SVR-RFE) were applied to H1N1 seasonal influenza viruses and used to analyze the associations between amino acid changes in the HA1 polypeptide and antigenic variation based on hemagglutination-inhibition (HI) assay data. Twenty-three and twenty antigenicity-associated sites were identified by RFR and SVR-RFE, respectively, by considering the joint effects of amino acid residues on antigenic drift. Our proposed approaches were further validated with the H3N2 dataset. The prediction models developed in this study can quantitatively predict antigenic differences with high prediction accuracy based only on HA1 sequences. Application of the study results can increase understanding of H1N1 seasonal influenza virus antigenic evolution and accelerate the selection of vaccine strains.