Visualizing the impact of space-time uncertainties on dengue fever patterns

Abstract

In this article, we evaluate the impact of positional and temporal inaccuracies on the mapping and detection of potential outbreaks of dengue fever in Cali, an urban environment of Colombia. Positional uncertainties in input data are determined by comparison between coordinates following an automated geocoding process and those extracted from on-field GPS measurements. Temporal uncertainties are modeled around the incubation period for dengue fever. To test the robustness of disease intensities in space and time when accounting for the potential space-time error, each dengue case is perturbed using Monte Carlo simulations. A space-time kernel density estimation (STKDE) is conducted on both perturbed and observed sets of dengue cases. To reduce the computational effort, we use a parallel spatial computing solution. The results are visualized in a 3D framework, which facilitates the discovery of new, significant space-time patterns and shapes of dengue outbreaks while enhancing our understanding of complex and uncertain dynamics of vector-borne diseases.