Dung Phung1, Mohammad Radwanur Rahman Talukder2, Shannon Rutherford3, Cordia Chu3. 1. Centre for Environment and Population Health, Griffith University, Nathan, Brisbane, Qld, Australia. d.phung@griffith.edu.au. 2. Centre for Environment and Population Health, Griffith University, Nathan, Brisbane, Qld, Australia. radwan.talukder@griffithuni.edu.au. 3. Centre for Environment and Population Health, Griffith University, Nathan, Brisbane, Qld, Australia.
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.
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.
Keywords:
Delta del Mekong; Delta du Mékong; Mekong Delta; Predicción; Vietnam; dengue incidence; incidence de la dengue; incidencia del dengue; prediction; prédiction
Authors: Van-Hau Nguyen; Tran Thi Tuyet-Hanh; James Mulhall; Hoang Van Minh; Trung Q Duong; Nguyen Van Chien; Nguyen Thi Trang Nhung; Vu Hoang Lan; Hoang Ba Minh; Do Cuong; Nguyen Ngoc Bich; Nguyen Huu Quyen; Tran Nu Quy Linh; Nguyen Thi Tho; Ngu Duy Nghia; Le Van Quoc Anh; Diep T M Phan; Nguyen Quoc Viet Hung; Mai Thai Son Journal: PLoS Negl Trop Dis Date: 2022-06-13