Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance: a case study of national dengue surveillance in Thailand
Issued Date
2024-12-01
Resource Type
eISSN
14712288
Scopus ID
2-s2.0-85182684271
Pubmed ID
38243198
Journal Title
BMC Medical Research Methodology
Volume
24
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
BMC Medical Research Methodology Vol.24 No.1 (2024)
Suggested Citation
Rotejanaprasert C., Chinpong K., Lawson A.B., Chienwichai P., Maude R.J. Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance: a case study of national dengue surveillance in Thailand. BMC Medical Research Methodology Vol.24 No.1 (2024). doi:10.1186/s12874-023-02135-9 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/95555
Title
Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance: a case study of national dengue surveillance in Thailand
Corresponding Author(s)
Other Contributor(s)
Abstract
Background: Dengue is a mosquito-borne disease that causes over 300 million infections worldwide each year with no specific treatment available. Effective surveillance systems are needed for outbreak detection and resource allocation. Spatial cluster detection methods are commonly used, but no general guidance exists on the most appropriate method for dengue surveillance. Therefore, a comprehensive study is needed to assess different methods and provide guidance for dengue surveillance programs. Methods: To evaluate the effectiveness of different cluster detection methods for dengue surveillance, we selected and assessed commonly used methods: Getis Ord Gi∗ , Local Moran, SaTScan, and Bayesian modeling. We conducted a simulation study to compare their performance in detecting clusters, and applied all methods to a case study of dengue surveillance in Thailand in 2019 to further evaluate their practical utility. Results: In the simulation study, Getis Ord Gi∗ and Local Moran had similar performance, with most misdetections occurring at cluster boundaries and isolated hotspots. SaTScan showed better precision but was less effective at detecting inner outliers, although it performed well on large outbreaks. Bayesian convolution modeling had the highest overall precision in the simulation study. In the dengue case study in Thailand, Getis Ord Gi∗ and Local Moran missed most disease clusters, while SaTScan was mostly able to detect a large cluster. Bayesian disease mapping seemed to be the most effective, with adaptive detection of irregularly shaped disease anomalies. Conclusions: Bayesian modeling showed to be the most effective method, demonstrating the best accuracy in adaptively identifying irregularly shaped disease anomalies. In contrast, SaTScan excelled in detecting large outbreaks and regular forms. This study provides empirical evidence for the selection of appropriate tools for dengue surveillance in Thailand, with potential applicability to other disease control programs in similar settings.