Etiological and epidemiological features of acute respiratory infections in China.
Zhong-Jie LiHai-Yang ZhangLi-Li RenQing-Bin LuXiang RenCui-Hong ZhangYi-Fei WangSheng-Hong LinXiao-Ai ZhangJun LiShi-Wen ZhaoZhi-Gang YiXiao ChenZuo-Sen YangLei MengXin-Hua WangYing-Le LiuXin WangAi-Li CuiSheng-Jie LaiTao JiangYang YuanLu-Sha ShiMeng-Yang LiuYu-Liang ZhuAn-Ran ZhangZhi-Jie ZhangYang YangMichael P WardLu-Zhao FengHuai-Qi JingLiu-Yu HuangWen-Bo XuYu ChenJian-Guo WuZheng-Hong YuanMeng-Feng LiYu WangLi-Ping WangLi-Qun FangWei LiuSimon I HayGeorge Fu GaoWei-Zhong Yangnull nullPublished in: Nature communications (2021)
Nationwide prospective surveillance of all-age patients with acute respiratory infections was conducted in China between 2009‒2019. Here we report the etiological and epidemiological features of the 231,107 eligible patients enrolled in this analysis. Children <5 years old and school-age children have the highest viral positivity rate (46.9%) and bacterial positivity rate (30.9%). Influenza virus, respiratory syncytial virus and human rhinovirus are the three leading viral pathogens with proportions of 28.5%, 16.8% and 16.7%, and Streptococcus pneumoniae, Mycoplasma pneumoniae and Klebsiella pneumoniae are the three leading bacterial pathogens (29.9%, 18.6% and 15.8%). Negative interactions between viruses and positive interactions between viral and bacterial pathogens are common. A Join-Point analysis reveals the age-specific positivity rate and how this varied for individual pathogens. These data indicate that differential priorities for diagnosis, prevention and control should be highlighted in terms of acute respiratory tract infection patients' demography, geographic locations and season of illness in China.
Keyphrases
- respiratory tract
- end stage renal disease
- klebsiella pneumoniae
- ejection fraction
- chronic kidney disease
- newly diagnosed
- gram negative
- sars cov
- liver failure
- multidrug resistant
- young adults
- escherichia coli
- prognostic factors
- antimicrobial resistance
- public health
- intensive care unit
- hepatitis b virus
- machine learning
- deep learning
- aortic dissection
- induced pluripotent stem cells
- genetic diversity