A climate‐based prediction model in the high‐risk clusters of the Mekong Delta region, Vietnam: towards improving dengue prevention and control. Issue 10 (27th July 2016) - Phung, Dung; Talukder, Mohammad Radwanur Rahman; Rutherford, Shannon; Chu, Cordia
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.