dc.contributor.author | Phung, Dung | |
dc.contributor.author | Talukder, Mohammad Radwanur Rahman | |
dc.contributor.author | Rutherford, Shannon | |
dc.contributor.author | Chu, Cordia | |
dc.date.accessioned | 2018-01-04T04:37:45Z | |
dc.date.available | 2018-01-04T04:37:45Z | |
dc.date.issued | 2016 | |
dc.identifier.issn | 1360-2276 | |
dc.identifier.doi | 10.1111/tmi.12754 | |
dc.identifier.uri | http://hdl.handle.net/10072/100473 | |
dc.description.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. | |
dc.description.peerreviewed | Yes | |
dc.language | English | |
dc.publisher | Wiley-Blackwell | |
dc.relation.ispartofpagefrom | 1324 | |
dc.relation.ispartofpageto | 1333 | |
dc.relation.ispartofissue | 10 | |
dc.relation.ispartofjournal | Tropical Medicine and International Health | |
dc.relation.ispartofvolume | 21 | |
dc.subject.fieldofresearch | Health services and systems | |
dc.subject.fieldofresearch | Public health | |
dc.subject.fieldofresearch | Clinical sciences | |
dc.subject.fieldofresearch | Epidemiology | |
dc.subject.fieldofresearchcode | 4203 | |
dc.subject.fieldofresearchcode | 4206 | |
dc.subject.fieldofresearchcode | 3202 | |
dc.subject.fieldofresearchcode | 4202 | |
dc.title | A climate-based prediction model in the high-risk clusters of the Mekong Delta region, Vietnam: towards improving dengue prevention and control | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dc.type.code | C - Journal Articles | |
gro.faculty | Griffith Sciences, Griffith School of Environment | |
gro.hasfulltext | No Full Text | |
gro.griffith.author | Chu, Cordia M. | |
gro.griffith.author | Rutherford, Shannon | |
gro.griffith.author | Phung, Dung T. | |