Respiratory Pathogen Dynamics in Community Fever Cases — Jiangsu Province, China (2023-2024)

Background Respiratory infectious disease was the world's highest incidence of infectious diseases, it was caused by a variety of respiratory pathogens, and the current monitoring of respiratory pathogens in the world focused on influenza and coronavirus. This study aimed to establish the pathogen spectrum of local acute respiratory infections and to further study the co-infection of pathogens. Time series models commonly used to predict infectious diseases can effectively predict disease outbreaks and serve as auxiliary tools for disease surveillance and response strategy formulation. Methods From June 2023 to February 2024, we collected influenza-like illness (ILI) cases weekly from the community in Xuanwu District, Nanjing, and obtained a total of 2,046 samples. We established a spectrum of respiratory pathogens in Nanjing and analyzed the age distribution and symptom counts associated with various pathogens. We compared age, gender, symptom counts, and viral loads between individuals with co-infections and those with single infections. An autoregressive comprehensive moving average model (ARIMA) was constructed to predict the incidence of respiratory infectious diseases. Results Among 2046 samples, the total detection rate of respiratory pathogen nucleic acids was 53.57% (1096/2046), with influenza A virus 503 cases (24.00%), influenza B virus 224 cases (10.95%), and HCoV 95 cases (4.64%) being predominant. Some pathogens were statistically significant in age and number of symptoms. The positive rate of mixed infections was 6.11% (125/2046), There was no significant difference in age and number of symptoms between co-infection and simple infection. After multiple iterative analyses, an ARIMA model (0,1,4), (0,0,0) was established as the optimal model, with an R ² value of 0.930, indicating good predictive performance. Conclusions In the past, the spectrum of respiratory pathogens in Nanjing, Jiangsu Province was complex, and the main age groups of different viruses were different, causing different symptoms, and the co-infection of viruses had no correlation with the age and gender of patients. The ARIMA model provided an estimate of future incidence, which plateaued in subsequent months.