A climate-based prediction model in the high-risk clusters of the Mekong Delta region, Vietnam: Towards improving dengue prevention and control

Dung Phung, Mohammad Radwanur Rahman Talukder, Shannon Rutherford, Cordia Chu

Research output: Contribution to journalArticlepeer-review

Abstract

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 generalised 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.

Original languageEnglish
Pages (from-to)1324-1333
Number of pages10
JournalTropical Medicine and International Health
Volume21
Issue number10
DOIs
Publication statusPublished - 1 Oct 2016
Externally publishedYes

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