journal.pone.0212003.pdf (1.04 MB)
An algorithm applied to national surveillance data for the early detection of major dengue outbreaks in Cambodia
journal contribution
posted on 2023-06-09, 17:30 authored by Julia Ledien, Kimsan Souv, Rithea Leang, Rekol Huy, Anthony Cousien, Muslim Peas, Yves Froehlich, Raphaël Duboz, Sivuth Ong, Veasna Duong, Philippe Buchy, Philippe Dussart, Arnaud TarantolaDengue is a national priority disease in Cambodia. The Cambodian National Dengue Surveillance System is based on passive surveillance of dengue-like inpatients reported by public hospitals and on a sentinel, pediatric hospital-based active surveillance system. This system works well to assess trends but the sensitivity of the early warning and time-lag to usefully inform hospitals can be improved. During The ECOnomic development, ECOsystem MOdifications, and emerging infectious diseases Risk Evaluation (ECOMORE) project’s knowledge translation platforms, Cambodian hospital staff requested an early warning tool to prepare for major outbreaks. Our objective was therefore to find adapted tools to improve the early warning system and preparedness. Dengue data was provided by the National Dengue Control Program (NDCP) and are routinely obtained through passive surveillance. The data were analyzed at the provincial level for eight Cambodian provinces during 2008–2015. The R surveillance package was used for the analysis. We evaluated the effectiveness of Bayesian algorithms to detect outbreaks using count data series, comparing the current count to an expected distribution obtained from observations of past years. The analyses bore on 78,759 patients with dengue-like syndromes. The algorithm maximizing sensitivity and specificity for the detection of major dengue outbreaks was selected in each province. The overall sensitivity and specificity were 73% and 97%, respectively, for the detection of significant outbreaks during 2008–2015. Depending on the province, sensitivity and specificity ranged from 50% to 100% and 75% to 100%, respectively. The final algorithm meets clinicians’ and decisionmakers’ needs, is cost-free and is easy to implement at the provincial level.
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Publication status
- Published
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- Published version
Journal
PLoS ONEISSN
1932-6203Publisher
Public Library of ScienceExternal DOI
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2Volume
14Page range
1-11Article number
e0212003Department affiliated with
- Evolution, Behaviour and Environment Publications
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- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2019-04-05First Open Access (FOA) Date
2019-04-05First Compliant Deposit (FCD) Date
2019-04-04Usage metrics
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