Analysis of influenza surveillance results in Sichuan Province in China, 2023-2025

An analysis of influenza surveillance data from sentinel hospitals throughout five areas of Sichuan Province from 2023 to 2025 will clarify influenza epidemic trends, therefore providing significant support for preventative and control methods.

The Influenza monitoring Information System of China gathered Influenza-Like Illness (ILI) data from sentinel hospitals for respiratory monitoring in Sichuan Province. Descriptive epidemiological techniques and cross-correlation analysis were used to evaluate lag displacement across areas in the Chengdu Plain. Dynamic Time Warping (DTW) methods evaluated the morphological variance of epidemic curves, whereas spatio-temporal coupling coefficients quantified the strength of collaborative diffusion across locations.

During a three-year period, the province's influenza positivity rate averaged 16.70%, reaching a maximum of 19.31% in 2023, with slight variations in the following 2 years (p < 0.001). The predominant circulating strains demonstrated a temporal shift from A(H3N2) in 2023 to A(H1N1)pdm09 in 2024, before returning to A(H3N2) in 2025, with the B(Victoria) lineage co-circulating throughout the study period. The population mostly consisted of children aged 0-14, with the 0-4 age group being the biggest proportion, fluctuating between 40 and 60% overall. A spatial analysis revealed that 56.3% of provincial cases originated from the Chengdu Plain, with influenza spreading from central to peripheral areas.

The spread of influenza in Sichuan Province is shaped by natural terrain and human mobility, resulting in a pattern where outbreaks propagate from the Chengdu Plain to surrounding areas. Future preventative and control measures should effectively use the 1 week epidemiological lag time across regions to establish a geographically diversified, sub-regional collaborative early warning system. It is essential to prioritize the enhancement of pathogen genomes and the monitoring of extensive data in areas with high cross-regional population mobility, hence enabling precise interventions and resource allocation within the province.