Paper Information

Weekly dengue case counts from 2009 to 2023 were analyzed using generalized linear mixed models with a negative binomial distribution. Epidemiological data were integrated with remotely sensed environmental variables, including vegetation indices (Normalized Difference Vegetation Index [NDVI] and Normalized Difference Water Index [NDWI]), daytime and nighttime Land Surface Temperature (LST), precipitation, and human population density. Lagged environmental effects (1-4 weeks) and biologically plausible interactions were evaluated.

Dengue fever is a major mosquito-borne disease whose transmission is influenced by climatic, environmental, and demographic factors. Colombia is a hyperendemic country, yet long-term local-scale studies remain limited. This study assessed the relationships between dengue incidence and environmental, climatic, and demographic predictors in Santa Marta, Colombia.

Dengue transmission in Santa Marta is shaped by complex and delayed interactions among environmental, climatic, and demographic factors. Vegetation cover increased dengue risk, particularly in densely populated areas, whereas excessive rainfall and high temperatures were associated with reduced incidence. These findings support the development of One Health-based surveillance and early-warning systems for dengue prevention.

A total of 9237 dengue cases were recorded during the study period. The best-fitting model identified significant positive associations between dengue incidence and NDVI (P < 0.001). Significant negative associations were observed for maximum daytime LST at a one-week lag (P < 0.001), minimum nighttime LST at a four-week lag (P < 0.001), NDWI at a one-week lag (P = 0.018), and precipitation at a four-week lag (P < 0.001). Population density significantly strengthened the positive effect of NDVI on dengue cases.