Frontiers in cellular and infection microbiology · 2025
Epidemiological trends and climatic drivers of pediatric respiratory infections in Wuhan, China: a multi-pathogen analysis.
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Li Changzhen, Xi Lei, Rao Jingjing, Tang Feng, Xiang Yun, Wang Xiaomei
Аннотация
OBJECTIVES: To characterize the epidemiology of pediatric respiratory infections and evaluate the lagged, nonlinear associations between meteorological factors and pathogen activity in post-COVID-19 Wuhan, China.
METHODS: A total of 28,903 respiratory specimens were collected from pediatric patients at a tertiary hospital between November 2023 and February 2025. Seven pathogens-Mycoplasma pneumoniae, adenovirus, respiratory syncytial virus (RSV), influenza A/B, and parainfluenza virus types I/III-were detected using multiplex RT-PCR. Epidemiological patterns were analyzed by age, sex, seasonality, and clinical setting. Daily meteorological data (temperature, relative humidity, wind speed) were aggregated citywide and temporally matched to case data. Spearman correlation and generalized additive models integrated with distributed lag nonlinear models (GAM-DLNMs) were used to assess pathogen-specific climatic sensitivity.
RESULTS: M. pneumoniae (18.9%), adenovirus (14.5%), and RSV (9.1%) were the most prevalent pathogens. Distinct age- and sex-specific distributions were observed, with M. pneumoniae peaking in school-aged boys and RSV in infants. Seasonal peaks were evident: RSV and influenza A predominated in winter, while adenovirus peaked in spring. Meteorological analysis revealed pathogen-specific associations: low humidity preceded RSV surges by 7-14 days; influenza B was strongly associated with wind exposure; and extreme climatic conditions showed heterogeneous effects on transmission risk across pathogens.
CONCLUSIONS: This study demonstrates the utility of GAM-DLNMs in capturing climate-sensitive, time-lagged transmission dynamics for multiple pediatric respiratory pathogens. The findings support the development of localized, climate-informed early warning systems to enhance respiratory disease surveillance and preparedness.
METHODS: A total of 28,903 respiratory specimens were collected from pediatric patients at a tertiary hospital between November 2023 and February 2025. Seven pathogens-Mycoplasma pneumoniae, adenovirus, respiratory syncytial virus (RSV), influenza A/B, and parainfluenza virus types I/III-were detected using multiplex RT-PCR. Epidemiological patterns were analyzed by age, sex, seasonality, and clinical setting. Daily meteorological data (temperature, relative humidity, wind speed) were aggregated citywide and temporally matched to case data. Spearman correlation and generalized additive models integrated with distributed lag nonlinear models (GAM-DLNMs) were used to assess pathogen-specific climatic sensitivity.
RESULTS: M. pneumoniae (18.9%), adenovirus (14.5%), and RSV (9.1%) were the most prevalent pathogens. Distinct age- and sex-specific distributions were observed, with M. pneumoniae peaking in school-aged boys and RSV in infants. Seasonal peaks were evident: RSV and influenza A predominated in winter, while adenovirus peaked in spring. Meteorological analysis revealed pathogen-specific associations: low humidity preceded RSV surges by 7-14 days; influenza B was strongly associated with wind exposure; and extreme climatic conditions showed heterogeneous effects on transmission risk across pathogens.
CONCLUSIONS: This study demonstrates the utility of GAM-DLNMs in capturing climate-sensitive, time-lagged transmission dynamics for multiple pediatric respiratory pathogens. The findings support the development of localized, climate-informed early warning systems to enhance respiratory disease surveillance and preparedness.