Dengue is an arbovirus transmitted by mosquitoes of the genus Aedes and is one of the 15 main public health problems in the world, including Colombia. Where limited financial resources create a problem for management, there is a need for the department to prioritize target areas for public health implementation. This study focuses on a spatio-temporal analysis to determine the targeted area to manage the public health problems related to dengue cases. To this end, three phases at three different scales were carried out. First, for the departmental scale, four risk clusters were identified in Cauca (RR ≥ 1.49) using the Poisson model, and three clusters were identified through Getis-Ord Gi* hotspots analysis; among them, Patía municipality presented significantly high incidence rates in the time window (2014–2018). Second, on the municipality scale, altitude and minimum temperature were observed to be more relevant than precipitation; considering posterior means, no spatial autocorrelation for the Markov Chain Monte Carlo was found (Moran test ˂ 1.0), and convergence was reached for b₁–b₁₀₅ with 20,000 iterations. Finally, on the local scale, a clustered pattern was observed for dengue cases distribution (nearest neighbour index, NNI = 0.202819) and the accumulated number of pupae (G = 0.70007). Two neighbourhoods showed higher concentrations of both epidemiological and entomological hotspots. In conclusion, the municipality of Patía is in an operational scenario of a high transmission of dengue.

The Culicidae family has two of the most important disease vector genus: Aedes spp. and Culex spp. Both of these are involved in the transmission of arboviruses. Here, we provide novel data for the geographical distribution of 2,383 specimens in the Culicidae family. We also report the percentage of houses infested with these vectors, and Shannon and Simpson diversity indices in three municipalities located in Cauca, Colombia. This dataset is relevant for research on vector-borne diseases because Aedes spp. can transmit arboviruses such as dengue, Zika and chikungunya, and Culex spp. is a well-known vector of West Nile virus and Venezuelan equine encephalitis.

Aedes aegypti mosquitoes are the main vector of human arbovirosis in tropical and subtropical areas. Their adaptation to urban and rural environments generates infestations inside households. Therefore, entomological surveillance associated with spatio-temporal analysis is an innovative approach for vector control and dengue management. Here, our main aim was to inspect immature pupal stages in households belonging to municipalities at high risk of dengue in Cauca, Colombia, by implementing entomological indices and relating how they influence adult mosquitos’ density. We provide novel data for the geographical distribution of 3,806 immature pupal stages of Ae. aegypti. We also report entomological indices and spatial characterization. Our results suggest that, for Ae. aegypti species, pupal productivity generates high densities of adult mosquitos in neighbouring households, evidencing seasonal behaviour. Our dataset is essential as it provides an innovative strategy for mitigating vector-borne diseases using vector spatial patterns. It also delineates the association between these vector spatial patterns, entomological indicators, and breeding sites in high-risk neighbourhoods.

The potential of dengue infection is of prime public health concern in tropical and subtropical countries. In Colombia, the management of this disease is based mainly on epidemiological monitoring and vector control. This study, covering the period 2015-2022, adds to this approach by investigating a tool that identifies dengue risk zones considering its environmental and sociodemographic determinants. For this purpose, an analytical, comparative, ecological study was carried out in three stages: i) selection of indicators associated with the occurrence of dengue through hierarchical analysis; ii) execution of a spatial-based Ordinary Least Squares (OLS) regression technique; and iii) multi-criteria analysis of the risk data obtained. Consequently, two optimal models, one for the rainy season (R2=0.5761; AIC=366.3929) and the other for the dry season (R2=0.8560; AIC=440.7557) were obtained for the Dengue Incidence Rate (DIR) during the study period mainly based on socio-demographic and environmental variables. A dengue risk map was generated, showing the impact on three neighbourhoods in the municipality of Piamonte in the Cauca Department covering both seasons. In conclusion, the dengue risk map made it possible to identify highrisk areas and also to identify the determinants of disease occurrence, which can contribute to improving disease management in tropical and subtropical regions.