Bipartite Network modelling and analysis to understand the dynamics of dengue incidence in Ecuador during the 2014-2021 period

Abstract

Dengue is a significant public health concern in tropical countries, particularly Latin America, where its incidence is high. The disease is endemic on the Coast, in Amazonia, and in the Andean foothills of Ecuador. Dengue prevalence is closely related to various factors, including climatic patterns, socioeconomic conditions, vector control measures, and human mobility. Although statistical methods have been employed to analyze incidence patterns in Ecuador, they do not consider the connectivity between cities. To fill this gap, we developed a bipartite network to understand the co-occurrence of dengue instances among Ecuadorian cities and pinpoint illness hotspots. The projectionweighted Newman technique generated a network where nodes were cities with reported dengue cases. The centrality metrics of networks were applied to identify focal nodes in the spread of dengue. These key nodes were Guayaquil, Machala, Santo Domingo, Quevedo, Manta, Orellana, and Lago Agrio, which play a crucial role as connectors and propagators of the disease. The results of this study can be used to focus on medical interventions such as cleanliness and immunization and how the dengue virus can be avoided or controlled in contiguous areas. The network analysis for this study was performed using R and Gephi, but it could be fully automated in R. This study highlights the importance of using a network approach to understand the spread of dengue, particularly in regions with high mobility and incidence. The findings of this study could aid public health officials and policymakers in developing effective strategies for controlling dengue fever in Ecuador and other similar regions.