Space-time clusters and co-occurrence of chikungunya and dengue fever in Colombia from 2015 to 2016

Acta Trop. 2018 Sep:185:77-85. doi: 10.1016/j.actatropica.2018.04.023. Epub 2018 Apr 27.

Abstract

Vector-borne diseases (VBDs) infect over one billion people and are responsible for over one million deaths each year, globally. Chikungunya (CHIK) and Dengue Fever (DENF) are emerging VBDs due to overpopulation, increases in urbanization, climate change, and other factors. Colombia has recently experienced severe outbreaks of CHIK AND DENF. Both viruses are transmitted by the Aedes mosquitoes and are preventable with a variety of surveillance and vector control measures (e.g. insecticides, reduction of open containers, etc.). Spatiotemporal statistics can facilitate the surveillance of VBD outbreaks by informing public health officials where to allocate resources to mitigate future outbreaks. We utilize the univariate Kulldorff space-time scan statistic (STSS) to identify and compare statistically significant space-time clusters of CHIK and DENF in Colombia during the outbreaks of 2015 and 2016. We also utilize the multivariate STSS to examine co-occurrences (simultaneous excess incidences) of DENF and CHIK, which is critical to identify regions that may have experienced the greatest burden of VBDs. The relative risk of CHIK and DENF for each Colombian municipality belonging to a univariate and multivariate cluster is reported to facilitate targeted interventions. Finally, we visualize the results in a three-dimensional environment to examine the size and duration of the clusters. Our approach is the first of its kind to examine multiple VBDs in Colombia simultaneously, while the 3D visualizations are a novel way of illustrating the dynamics of space-time clusters of disease.

Keywords: Clusters; Colombia; GIS; Space-time statistics; Vector-borne diseases.

MeSH terms

  • Chikungunya Fever / epidemiology*
  • Cities / epidemiology
  • Colombia / epidemiology
  • Dengue / epidemiology*
  • Disease Outbreaks*
  • Epidemiological Monitoring
  • Humans
  • Incidence
  • Space-Time Clustering
  • Spatio-Temporal Analysis