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Public Health . Epidemiological characteristics and driving factors of co-circulation and co-detection of multiple respiratory pathogens: A multi-c

tetano

Editor, Senior Moderator
Public Health


. 2026 Mar 9:254:106245.
doi: 10.1016/j.puhe.2026.106245. Online ahead of print.
Epidemiological characteristics and driving factors of co-circulation and co-detection of multiple respiratory pathogens: A multi-center surveillance study in China

Huihui Zhang[SUP] 1 [/SUP], Rongrong Qu[SUP] 1 [/SUP], Qianqian Feng[SUP] 1 [/SUP], Zenghao Xu[SUP] 2 [/SUP], Xiaofei Zhou[SUP] 1 [/SUP], Anqi Dai[SUP] 1 [/SUP], Can Chen[SUP] 1 [/SUP], Mengsha Chen[SUP] 1 [/SUP], Wenkai Zhou[SUP] 1 [/SUP], Jiani Miao[SUP] 1 [/SUP], Jiaxing Qi[SUP] 1 [/SUP], Jiaxin Chen[SUP] 1 [/SUP], Yirong Fang[SUP] 3 [/SUP], Yan Ma[SUP] 4 [/SUP], Shigui Yang[SUP] 5 [/SUP], Jimin Sun[SUP] 6 [/SUP]


Affiliations
Abstract

Objectives: Acute respiratory infections (ARIs) are driven by multiple co-circulating pathogens. We investigated pathogen co-detection patterns and interactions at multiple levels to identify factors shaping their dynamics.
Study design: Population-based surveillance study.
Methods: From November 2023 to December 2024, a multi-center surveillance of ARIs was conducted in 26 sentinel hospitals across Zhejiang Province, China. 14 respiratory pathogens were tested using PCR-based capillary electrophoresis. Population-level pathogen associations were assessed by Spearman's rank correlation and Granger causality tests. Pathogen interactions at the host level were analyzed using proportional distribution model and multivariable logistic regression.
Results: Of 39,115 enrolled ARIs cases, 38,606 were tested for 14 respiratory pathogens. The overall pathogen positivity was 54.84% (21,173/38,606), with co-detections observed in 12.47% (4815/38,606) of ARIs cases. Streptococcus pneumoniae was the most prevalent pathogen and the primary driver of co-detections. A directed predictive network was identified, with RSV, HRV, and HPIV as key hubs. Viral-bacterial synergy was common in children, while adults and the elderly showed significant viral interference, most notably between IFV-A and IFV-B (OR = 0.06). Strong influenza interference was observed in the cold season (IFV-A-IFV-B, OR = 0.10), whereas the warm season was dominated by broader pathogen synergy, alongside notable negative associations such as IFV-A-SARS-CoV-2 (OR = 0.17).
Conclusions: This study reveals that age- and season-dependent pathogen interactions may shape the dynamic patterns of multiple respiratory pathogens, thereby highlighting the importance of considering such interactions in public health strategies.

Keywords: Acute respiratory infection; Multi-pathogen surveillance; Pathogen co-circulation; Pathogen co-detection; Pathogen–pathogen interaction.

 
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