Author(s)

Shuyuan Ye

Guangdong Experimental High School, Guangzhou, China.

As a highly contagious respiratory disease, influenza exhibits significant spatiotemporal fluctuations in incidence, posing a persistent threat to public health and placing considerable strain on healthcare resource allocation and emergency response systems. Accurately grasping its epidemic characteristics is crucial for improving prevention and control efficiency. This study selects 29 cities as the research subjects and employs K-means clustering to classify them based on three core indicators: administrative level, GDP, and influenza incidence rate. The optimal number of clusters is determined using the elbow method, and MATLAB is used for data processing and model computation. The findings reveal that cities with higher GDP and administrative level tend to have lower incidence rates, likely due to more abundant medical resources and robust prevention systems. In contrast, cities with lower GDP and administrative levels generally exhibit higher incidence rates due to limited resource allocation. The results provide a scientific basis for developing differentiated influenza prevention strategies and optimizing the allocation of public health resources.

K-Means Algorithm, Influenza Transmission, Cluster Analysis, Urban Characteristics

Ye, S. (2025) Application of the K-Means Algorithm in the Study of Influenza Transmission Patterns. Journal of Computer and Communications, 13, 1-12. doi: 10.4236/jcc.2025.139001.