A climate-based prediction model in the high-risk clusters of the Mekong Delta Region, Vietnam: towards improving dengue prevention and control.
Trop Med Int Health. 2016 Jul 12;
Authors: Phung D, Talukder MR, Rutherford S, Chu C
OBJECTIVE: To develop a prediction score scheme useful for prevention practitioners and authorities to implement dengue preparedness and controls in the Mekong Delta Region (MDR).
METHODS: We applied a spatial scan statistic to identify high-risk dengue clusters in the MDR and used generalized linear distributed lag models to examine climate-dengue associations using dengue case records and meteorological data from 2003 to 2013. The significant predictors were collapsed into categorical scales, and the β-coefficients of predictors were converted to prediction scores. The score scheme was validated for predicting dengue outbreaks using ROC analysis.
RESULTS: The north-eastern MDR was identified as the high-risk cluster. A 1°C increase in temperature at lag 1-4 and 5-8 weeks increased the dengue risk 11% (95%CI, 9-13) and 7% (95%CI, 6-8) respectively. A 1% rise in humidity increased dengue risk 0.9% (95%CI, 0.2-1.4) at lag 1-4 and 0.8% (95%CI, 0.2-1.4) at lag 5-8 weeks. Similarly a 1 mm increase in rainfall increased dengue risk 0.1% (95%CI, 0.05-0.16) at lag 1-4 and 0.11% (95%CI, 0.07-0.16) at lag 5-8 weeks. The predicted scores performed with high accuracy in diagnosing the dengue outbreaks (96.3%).
CONCLUSION: This study demonstrates the potential usefulness of a dengue prediction score scheme derived from complex statistical models for high-risk dengue clusters. We recommend a further study to examine the possibility of incorporating such a score scheme into the dengue early warning system in similar climate settings. This article is protected by copyright. All rights reserved.
PMID: 27404323 [PubMed - as supplied by publisher]