02208nas a2200217   4500000000100000008004100001260004400042653001800086653002200104653002800126653002500154100002100179700001100200700001200211700001200223245007200235856007300307300000700380520157800387022002501965       2025                            d  bSpringer Science and Business Media LLC10aGeostatistics10aJoint circulation10aMosquito-borne diseases10aSpatial distribution1 aPayares-Garcia D1 aOsei F1 aMateu J1 aStein A00aA poisson cokriging modeling of mosquito-borne diseases in Colombia  uhttps://link.springer.com/content/pdf/10.1007/s10651-025-00646-w.pdf  a253 a<p>Mosquito-borne diseases pose a significant public health concern in Colombia, necessitating robust quantification of their geographic patterns to guide and optimize interventions. This study explores the spatial dynamics and interactions among Zika, Dengue, and Chikungunya within the context of joint disease modeling in the Andean region of Colombia. Leveraging the Poisson cokriging method, we modeled and mapped an improved version of risks associated with the three diseases by incorporating a related mosquito-borne disease as secondary information while accounting for heterogeneous population distributions. Our findings reveal similar disease spatial risk patterns, suggesting possible shared localized transmission dynamics among the three diseases, with hotspots primarily occurring in municipalities characterized by high co-morbidity rates. The semivariogram and cross-semivariogram ranges suggested the potential influence of common local risk factors that might contribute to the spatial variation across the region. The smoothed disease risk maps highlight areas with elevated incidence rates, informing targeted intervention strategies. This study provides insights into the spatial distribution of the risk of Zika, Dengue, and Chikungunya, and hypothesize possible shared factors that drive their emergence in Colombia. It further highlights the utility of Poisson cokriging for improving disease risk mapping when auxiliary disease data are available, advancing the understanding of the intricate spatial relationships between related diseases.</p>
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