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

PLoS One. 2019 Feb 7;14(2):e0212003. doi: 10.1371/journal.pone.0212003. eCollection 2019.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Cambodia / epidemiology
  • Dengue / diagnosis*
  • Dengue / epidemiology
  • Disease Outbreaks*
  • Early Diagnosis
  • Government Programs
  • Humans
  • Population Surveillance / methods*
  • Sensitivity and Specificity

Grant support

The ECOMORE project was funded by the Agence française de Développement (AFD) https://www.afd.fr/. The funder provided support in the form of salaries for authors JL, KS, AC, MP, YF, SO, VD, PD and AT, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section. One of the authors is employed by a commercial company (GlaxoSmithKline) but was previously working at Pasteur institute where he managed the laboratory-confirmation of dengue cases. These data are used in our paper and all the persons who have collected and handle these data were invited to be a co-author. The commercial company had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.