Comparative evaluation of spatiotemporal methods for effective dengue cluster detection with a case study of national surveillance data in Thailand

Abstract

Dengue fever poses a significant public health burden in tropical regions, including Thailand, where periodic epidemics strain healthcare resources. Effective disease surveillance is essential for timely intervention and resource allocation. Various methods exist for spatiotemporal cluster detection, but their comparative performance remains unclear. This study compared spatiotemporal cluster detection methods using simulated and real dengue surveillance data from Thailand. A simulation study explored diverse disease scenarios, characterized by varying magnitudes and spatial-temporal patterns, while real data analysis utilized monthly national dengue surveillance data from 2018 to 2020. Evaluation metrics included accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. Bayesian models and FlexScan emerged as top performers, demonstrating superior accuracy and sensitivity. Traditional methods such as Getis Ord and Moran's I showed poorer performance, while other scanning-based approaches like spatial SaTScan exhibited limitations in positive predictive value and tended to identify large clusters due to the inflexibility of its scanning window shape. Bayesian modeling with a space-time interaction term outperformed testing-based cluster detection methods, emphasizing the importance of incorporating spatiotemporal components. Our study highlights the superior performance of Bayesian models and FlexScan in spatiotemporal cluster detection for dengue surveillance. These findings offer valuable guidance for policymakers and public health authorities in refining disease surveillance strategies and resource allocation. Moreover, the insights gained from this research could be valuable for other diseases sharing similar characteristics and settings, broadening the applicability of our findings beyond dengue surveillance.

Keywords: Cluster detection; Dengue; Spatiotemporal; Surveillance; Thailand.

Fig. 1

Maps of simulated relative risks in the simulation study, generated using RStudio version 2022.07.0 + 548 (available at https://posit.co/products/open-source/rstudio/).

Fig. 2

Plot of monthly dengue case reported to the Thai national dengue surveillance program during 2011–2020, created using RStudio version 2022.07.0 + 548 (available at https://posit.co/products/open-source/rstudio/).

Fig. 3

Accuracy maps of the prospective Bayesian model with BESAG and random walk order 2 effect terms, generated using RStudio version 2022.07.0 + 548 (available at https://posit.co/products/open-source/rstudio/).

Fig. 4

Accuracy maps of the FlexScan with flexible scanning window, generated using RStudio version 2022.07.0 + 548 (available at https://posit.co/products/open-source/rstudio/).

Fig. 5

Accuracy maps of the retrospective space–time SaTScan model with elliptical scanning window, generated using RStudio version 2022.07.0 + 548 (available at https://posit.co/products/open-source/rstudio/).

Fig. 6

Accuracy maps of spatial SaTScan model with elliptical scanning window, generated using RStudio version 2022.07.0 + 548 (available at https://posit.co/products/open-source/rstudio/).

Fig. 7

Monthly provincial maps of standardized morbidity ratios for dengue from the national surveillance data from 2018 to 2020. Created using RStudio version 2022.07.0 + 548 (available at https://posit.co/products/open-source/rstudio/).

Fig. 8

Provincial hotspot maps of spatiotemporal Bayesian model using monthly national reported dengue data in Thailand during 2018–2020, generated using RStudio version 2022.07.0 + 548 (available at https://posit.co/products/open-source/rstudio/).

Fig. 9

Provincial hot spot maps of FlexScan with flexible shape using monthly national reported dengue data in Thailand during 2018–2020, generated using RStudio version 2022.07.0 + 548 (available at https://posit.co/products/open-source/rstudio/).

Fig. 10

Provincial hot spot maps of spatial SaTScan with elliptical shape using monthly national reported dengue data in Thailand during 2018–2020, generated using RStudio version 2022.07.0 + 548 (available at https://posit.co/products/open-source/rstudio/).

Fig. 11

Provincial hot spot maps of prospective space–time SaTScan with elliptical shape using monthly national reported dengue data in Thailand during 2018–2020, generated using RStudio version 2022.07.0 + 548 (available at https://posit.co/products/open-source/rstudio/).