ArticleBMC medical research methodology2024
Evaluation and comparison of spatial cluster detection methods for improved decision making of disease surveillance: a case study of national dengue surveillance in Thailand.
Article in BMC medical research methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 18 citations in OpenAlex.
- Epidemiological shift in age distribution of dengue in Thailand, 2005-2024.PLoS neglected tropical diseases · 2026Article
- A descriptive assessment of mitochondrial COI genetic diversity and dengue and chikungunya RNA detection in Aedes aegypti across eight provinces in Thailand.Parasites & vectors · 2026Article
- Spatiotemporal epidemiology, geographic hotspots, and risk factor associations of drug-resistant tuberculosis incidence in Indonesia: a Bayesian hierarchical modelling approach.Infectious diseases of poverty · 2026Article
- Spatial and spatio-temporal county-level trends in COVID-19 mortality and emergency department visits in U.S. with R-INLA.Spatial and spatio-temporal epidemiology · 2026Article
- Spatiotemporal epidemiology and associated risk factors of tuberculosis incidence and mortality in Indonesia 2017-2022: a nationwide space-time hierarchical analysis.Population health metrics · 2026Article
- CalCORVID: a dynamic RShiny dashboard approach to visualize spatiotemporal clusters for public health surveillance.BMC public health · 2026Article
- Article
- Evaluation of the Entomological Adaptive Surveillance Framework for malaria vector monitoring: a comparative field trial with routine surveillance in Ghana and Mozambique.BMJ public health · 2026Article
- Spatial heterogenicity of tuberculosis and dengue in Nepal.Scientific reports · 2025Article
- Spatiotemporal epidemiology and hierarchical analysis of suicide mortality and associated risk factors in Thailand using national surveillance data during 1997-2021.Scientific reports · 2025Article
- A spatial clustering-based approach to design monitoring networks of infectious diseases: a case study of hand, foot, and mouth disease.Infectious diseases of poverty · 2025Article
- Spatial occurrence-intensity modeling of dengue incidence in southernmost provinces of Thailand.PLoS neglected tropical diseases · 2025Article
- Global spatiotemporal analysis of suicide epidemiology and risk factor associations from 2000 to 2019 using Bayesian space time hierarchical modeling.Scientific reports · 2025Article
- Comparative evaluation of spatiotemporal methods for effective dengue cluster detection with a case study of national surveillance data in Thailand.Scientific reports · 2024Article
- Spatiotemporal patterns and association with climate for malaria elimination in Lao PDR: a hierarchical modelling analysis with two-step Bayesian model selection.Malaria journal · 2024Article
- Perspectives and challenges in developing and implementing integrated dengue surveillance tools and technology in Thailand: a qualitative study.PLoS neglected tropical diseases · 2024Article
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Authors and funding
5 authors at 4 institutions in 3 countries.
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Abstract
backgroundDengue 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.
methodsTo evaluate the effectiveness of different cluster detection methods for dengue surveillance, we selected and assessed commonly used methods: Getis Ord [Formula: see text], 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.
resultsIn the simulation study, Getis Ord [Formula: see text] 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 [Formula: see text] 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.
conclusionsBayesian 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.
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