Multi-pathogen respiratory surveillance in western China: Synchronized circulation patterns and predictive modeling.

Area of Science:

• Infectious Diseases
• Epidemiology
• Public Health

Background:

• Respiratory pathogen dynamics in western China post-COVID-19 restrictions were poorly understood.
• Limited data existed on the resurgence patterns of various respiratory pathogens after prolonged control measures.

Purpose of the Study:

• To characterize the dynamics of 14 respiratory pathogens in western China from January 2020 to December 2024.
• To assess the impact of COVID-19 restrictions on pathogen prevalence and inter-pathogen relationships.
• To evaluate the effectiveness of forecasting models for predicting future pathogen trends.

Main Methods:

• Analysis of 50,247 respiratory specimens using multiplex PCR.
• Statistical analysis of pathogen positivity rates, including percentage increases for specific pathogens like H1N1 and Mycoplasma pneumoniae.
• Correlation analysis to identify temporal associations between pathogen pairs.
• Application of Seasonal Autoregressive Integrated Moving Average (SARIMA) models for 12-month forecasting.

Main Results:

• Overall pathogen positivity increased by 314% post-pandemic, with significant surges in H1N1 (1,826%) and Mycoplasma pneumoniae (519%).
• Human rhinovirus was the most frequently detected pathogen (9.05%).
• Contrary to typical patterns, 89% of pathogen pairs exhibited positive temporal associations.
• School-aged children bore a disproportionate burden, particularly for M. pneumoniae (30.0% positivity).
• SARIMA models demonstrated predictive capability with 15.2%-24.8% mean absolute percentage errors.
• Winter months showed concentrated peaks for six pathogens.

Conclusions:

• Prolonged control measures led to a synchronized resurgence of multiple respiratory pathogens.
• Findings underscore the need for integrated surveillance systems that monitor pathogen interactions.
• Enhanced epidemic preparedness requires understanding both individual pathogen dynamics and their complex interrelationships.