Spatio-temporal clusters and patterns of spread of dengue, chikungunya, and Zika in Colombia

Abstract

Background Colombia has one of the highest burdens of arboviruses in South America. The country was in a state of hyperendemicity between 2014 and 2016, with co-circulation of several Aedes-borne viruses, including a syndemic of dengue, chikungunya, and Zika in 2015. Methodology/Principal findings We analyzed the cases of dengue, chikungunya, and Zika notified in Colombia from January 2014 to December 2018 by municipality and week. The trajectory and velocity of spread was studied using trend surface analysis, and spatio-temporal high-risk clusters for each disease in separate and for the three diseases simultaneously (multivariate) were identified using Kulldorff’s scan statistics. During the study period, there were 366,628, 77,345 and 74,793 cases of dengue, chikungunya, and Zika, respectively, in Colombia. The spread patterns for chikungunya and Zika were similar, although Zika’s spread was accelerated. Both chikungunya and Zika mainly spread from the regions on the Atlantic coast and the south-west to the rest of the country. We identified 21, 16, and 13 spatio-temporal clusters of dengue, chikungunya and Zika, respectively, and, from the multivariate analysis, 20 spatio-temporal clusters, among which 7 were simultaneous for the three diseases. For all disease-specific analyses and the multivariate analysis, the most-likely cluster was identified in the south-western region of Colombia, including the Valle del Cauca department. Conclusions/Significance The results further our understanding of emerging Aedes-borne diseases in Colombia by providing useful evidence on their potential site of entry and spread trajectory within the country, and identifying spatio-temporal disease-specific and multivariate high-risk clusters of dengue, chikungunya, and Zika, information that can be used to target interventions.

A Systematic Review of Joint Spatial and Spatiotemporal Models in Health Research

“…Seven studies (26.6%) used data from a national health survey or the Demographic and Health Survey (DHS) [ 43 , 46 , 51 , 60 , 64 , 72 , 75 ], while seven other studies (26.6%) used data from cancer registries. An estimated 11.63% of the studies used data from multiple surveys.…”

“…To analyse two or more health outcomes simultaneously, several joint spatial and spatiotemporal models were used. To investigate the relative risk of the study variables and their risk factors, joint spatial models were used in 24 studies [ 36 , 38 , 39 , 40 , 42 , 43 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 54 , 56 , 57 , 59 , 60 , 61 , 62 , 64 , 73 , 75 ], joint spatiotemporal models in 12 studies [ 32 , 33 , 35 , 37 , 41 , 44 , 55 , 58 , 63 , 65 , 80 ], and joint spatial and spatiotemporal autocorrelation methods such as Moran Index statistics, Local Indicator Spatial Analysis (LISA), Getis Ord Gi statistics, or Kulldroff spatial and Spatio-temporal scan statistical tests used in 7 studies [ 34 , 72 , 74 , 76 , 77 , 78 , 79 ].…”

“…Of the 43 studies, 26 studies used R software [ 34 , 35 , 36 , 42 , 43 , 44 , 45 , 48 , 51 , 52 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 75 , 77 , 79 , 80 ], 21 studies used either WinBUGS or OpenBUGS or GeoBUGS [ 32 , 33 , 36 , 37 , 38 , 39 , 40 , 41 , 43 , 44 , 46 , 49 , 50 , 51 , 53 , 54 , 56 , 63 , 73 , 78 , 79 ], 7 studies used ArcGIS or QGIS [ 38 , 41 , 50 , 53 , 60 , 75 , 77 ], 4 studies used GeoDa [ …”

“…Seven studies were joint spatial/spatiotemporal autocorrelation studies [ 34 , 48 , 72 , 74 , 76 , 77 , 80 ], the joint Bayesian shared Spatiotemporal model was used in four studies [ 42 , 53 , 60 , 65 ], and the multivariate Bayesian Spatiotemporal shared component model with Poisson distribution was used in two studies [ 33 , 37 ]. Other models included the bivariate Bayesian logit spatial model [ 46 , 51 , 63 , 64 ], geo-additive mixed models [ 57 , 75 ], and the multivariate negative binomial models with CAR random effects [ 43 , 80 ] ( Table 3 ).…”

“…Almost all joint spatial and spatiotemporal studies used maps to present the risk estimates. In the joint spatial and joint spatiotemporal models, socio-economic variables were the predominant variables among the covariates considered in the model to predict the outcomes across space or space-time [ 34 , 37 , 38 , 43 , 46 , 47 , 48 , 52 , 54 , 55 , 57 , 59 , 60 , 64 , 73 , 75 ]. Regarding standardisation, only five of the joint spatial and spatiotemporal models applied standardisation for common demographic variables [ 42 , 58 , 59 , 63 , 80 ].…”