ArticleInternational journal of health geographics2016
Online platform for applying space-time scan statistics for prospectively detecting emerging hot spots of dengue fever.
Article in International journal of health geographics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Systematic Review on Modeling Methods and Influential Factors for Mapping Dengue-Related Risk in Urban Settings.International journal of environmental research and public health · 2022Pooled it
- A systematic review of spatial decision support systems in public health informatics supporting the identification of high risk areas for zoonotic disease outbreaks.International journal of health geographics · 2018Pooled it
- Space scan statistics to identify clusters of neonatal mortality associated with bacterial sepsis.Epidemiology and infection · 2024Article
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- A spatio-temporal autoregressive model for monitoring and predicting COVID infection rates.Journal of geographical systems · 2022Article
- Detecting space-time clusters of COVID-19 in Brazil: mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities.Journal of geographical systems · 2021Article
- Visual Analytic Tools and Techniques in Population Health and Health Services Research: Scoping Review.Journal of medical Internet research · 2020Article
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- Analyzing spatial and space-time clustering of facility-based deliveries in Bangladesh.Tropical medicine and health · 2019Article
- Multistate analysis of prospective Legionnaires' disease cluster detection using SaTScan, 2011-2015.PloS one · 2019Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundCases of dengue fever have increased in areas of Southeast Asia in recent years. Taiwan hit a record-high 42,856 cases in 2015, with the majority in southern Tainan and Kaohsiung Cities. Leveraging spatial statistics and geo-visualization techniques, we aim to design an online analytical tool for local public health workers to prospectively identify ongoing hot spots of dengue fever weekly at the village level.
methodsA total of 57,516 confirmed cases of dengue fever in 2014 and 2015 were obtained from the Taiwan Centers for Disease Control (TCDC). Incorporating demographic information as covariates with cumulative cases (365 days) in a discrete Poisson model, we iteratively applied space-time scan statistics by SaTScan software to detect the currently active cluster of dengue fever (reported as relative risk) in each village of Tainan and Kaohsiung every week. A village with a relative risk >1 and p value <0.05 was identified as a dengue-epidemic area. Assuming an ongoing transmission might continuously spread for two consecutive weeks, we estimated the sensitivity and specificity for detecting outbreaks by comparing the scan-based classification (dengue-epidemic vs. dengue-free village) with the true cumulative case numbers from the TCDC's surveillance statistics.
resultsAmong the 1648 villages in Tainan and Kaohsiung, the overall sensitivity for detecting outbreaks increases as case numbers grow in a total of 92 weekly simulations. The specificity for detecting outbreaks behaves inversely, compared to the sensitivity. On average, the mean sensitivity and specificity of 2-week hot spot detection were 0.615 and 0.891 respectively (p value <0.001) for the covariate adjustment model, as the maximum spatial and temporal windows were specified as 50% of the total population at risk and 28 days. Dengue-epidemic villages were visualized and explored in an interactive map.
conclusionsWe designed an online analytical tool for front-line public health workers to prospectively detect ongoing dengue fever transmission on a weekly basis at the village level by using the routine surveillance data.
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