Analysis of Regional Dengue Data Dynamics in Peru Using Principal Components, 2000–2023

Rojas-Paucar W.; Carranza López A.O.; Chávez Garcés E.M.; Supo Gavancho K.Y.; Gutierrez Perez J.R. · Revista Cubana de Investigaciones Biomedicas (2024)

Resumen

Objective: To analyze the spatial and temporal behavior of reported dengue cases in Peru from 2000 to 2023, using Principal Component Analysis (PCA) to identify behavioral patterns and factors contributing to the spread of the disease. Methods: This exploratory descriptive study analyzed 775,142 dengue cases recorded across 22 regions of Peru, based on data from the National Epidemiological Network (RENACE). PCA was applied, selecting two principal components, PC1 and PC2, which together explained 73% of the data variability. Results: The regions of Piura, Lambayeque, Lima, La Libertad, Tumbes, and Ancash were the most affected, with a sustained increase in dengue cases over time, particularly during 2023, which recorded the highest infection rate in recent decades. PCA identified PC1 as accounting for 59.2% of the variability, showing a strong correlation with sustained case increases in the affected regions. PC2 explained 14.7% of the variability, describing erratic case trends in the involved regions. Conclusions: PCA proved valuable in understanding the dynamics of dengue in complex and changing scenarios. Priority should be given to regions with higher infection rates, implementing strategies to prevent and control elevated contagion levels.

Instituciones: Universidad Nacional Mayor de San Marcos; Universidad Nacional de Moquegua; Universidad Nacional Jorge Basadre Grohmann