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An algorithm applied to national surveillance data for the early detection of major dengue outbreaks in Cambodia

Ledien Julien, Souv Kimsan, Leang Rithea, Huy Rekol, Cousien Anthony, Peas Muslim, Froehlich Yves, Duboz Raphaël, Ong Sivuth, Duong Veasna, Buchy Philippe, Dussart Philippe, Tarantola Arnaud. 2019. An algorithm applied to national surveillance data for the early detection of major dengue outbreaks in Cambodia. PloS One, 14 (2):e0212003, 11 p.

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Url - jeu de données - Entrepôt autre : https://figshare.com/articles/An_algorithm_applied_to_national_surveillance_data_for_the_early_detection_of_major_dengue_outbreaks_in_Cambodia/7691483

Quartile : Q2, Sujet : MULTIDISCIPLINARY SCIENCES

Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Psychologie-éthologie-ergonomie; Staps

Résumé : Dengue 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.

Mots-clés Agrovoc : maladie des animaux, surveillance épidémiologique, méthode statistique

Mots-clés géographiques Agrovoc : Cambodge

Classification Agris : L73 - Maladies des animaux
U10 - Informatique, mathématiques et statistiques

Champ stratégique Cirad : CTS 4 (2019-) - Santé des plantes, des animaux et des écosystèmes

Auteurs et affiliations

  • Ledien Julien, Institut Pasteur du Cambodge (KHM) - auteur correspondant
  • Souv Kimsan, Institut Pasteur du Cambodge (KHM)
  • Leang Rithea, Centre National de Malariologie (KHM)
  • Huy Rekol, Centre National de Malariologie (KHM)
  • Cousien Anthony, Institut Pasteur du Cambodge (KHM)
  • Peas Muslim, Institut Pasteur du Cambodge (KHM)
  • Froehlich Yves, Institut Pasteur du Cambodge (KHM)
  • Duboz Raphaël, CIRAD-BIOS-UMR ASTRE (FRA) ORCID: 0000-0002-2853-6195
  • Ong Sivuth, Institut Pasteur du Cambodge (KHM)
  • Duong Veasna, Institut Pasteur du Cambodge (KHM)
  • Buchy Philippe, GlaxoSmithKline (SGP)
  • Dussart Philippe, Institut Pasteur du Cambodge (KHM)
  • Tarantola Arnaud, Institut Pasteur du Cambodge (KHM)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/591184/)

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