Integration of Time into a 2-Dimensional Geography to Visualize Spatio-Temporal Clusters of Dog Rabies in Thailan

Abstract

Introduction: Space-time scanning analysis to detect cylindrical spatio-temporal clusters of diseases is available. Yet, thereis no satisfactory way to visualize the data. Our aim is to visualize spatio-temporal cylindrical clusters of dog rabies inThailand from 2005 to 2021.

Methods: We obtained dog rabies data from 2005 to 2021 from the World Animal Health Information System under theWorld Organisation for Animal Health (WOAH). The rsatscan package in R was applied to identify spatio-temporal clustersof dog rabies using the discrete Poisson model and Monte Carlo simulation. Using a user-defined function developed by ourresearch team, cylindrical shapes were created based on the provincial administration maps to demonstrate significantclusters over space and time.

Results: The average incidence of dog rabies was 0.5 events per 100,000 human-years, and seven clusters were foundduring the study period in all five national regions, based on 15% of the population being at risk.

Conclusion: Our method to generate multi-dimensional graphics can comprehensibly visualize cylinder-shaped outcomesfrom spatio-temporal data. With the relatively large number of dog rabies clusters detected, more intensive controlmeasures are required to alleviate dog rabies