A Hybrid Clustering Model for Analyzing COVID-19 National Prevention and Control Strategy

In order to analyze the relationship between the national epidemic strategy response to COVID-19 and the severity of the current epidemic, this paper firstly collects the national strategic data responds to the COVID-19 epidemic situation and the resulting data of COVID-19 in these nations, then use StandardScaler normalization method to pre-process the data, and use K-Means Clustering, Agglomerative Clustering and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm to perform cluster analysis on the above two types of data, use Silhouette Coefficient index, Calinski Harabasz Score, Davies bouldin score to evaluate the clustering results, and take the clustering results with the best clustering effect to analyze the impact of national epidemic policies on the current epidemic results, finally use cases to prove the effectiveness and feasibility of the model, and provide data support and strategic support for the prevention and control of the epidemic similar to COVID-19 in the future.