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- Bett, Bernard, et al.
(författare)
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Spatiotemporal analysis of historical records (2001-2012) on dengue fever in Vietnam and development of a statistical model for forecasting risk
- 2019
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 14:11
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Dengue fever is the most widespread infectious disease of humans transmitted by Aedes mosquitoes. It is the leading cause of hospitalization and death in children in the Southeast Asia and western Pacific regions. We analyzed surveillance records from health centers in Vietnam collected between 2001-2012 to determine seasonal trends, develop risk maps and an incidence forecasting model.METHODS: The data were analyzed using a hierarchical spatial Bayesian model that approximates its posterior parameter distributions using the integrated Laplace approximation algorithm (INLA). Meteorological, altitude and land cover (LC) data were used as predictors. The data were grouped by province (n = 63) and month (n = 144) and divided into training (2001-2009) and validation (2010-2012) sets. Thirteen meteorological variables, 7 land cover data and altitude were considered as predictors. Only significant predictors were kept in the final multivariable model. Eleven dummy variables representing month were also fitted to account for seasonal effects. Spatial and temporal effects were accounted for using Besag-York-Mollie (BYM) and autoregressive (1) models. Their levels of significance were analyzed using deviance information criterion (DIC). The model was validated based on the Theil's coefficient which compared predicted and observed incidence estimated using the validation data. Dengue incidence predictions for 2010-2012 were also used to generate risk maps.RESULTS: The mean monthly dengue incidence during the period was 6.94 cases (SD 14.49) per 100,000 people. Analyses on the temporal trends of the disease showed regular seasonal epidemics that were interrupted every 3 years (specifically in July 2004, July 2007 and September 2010) by major fluctuations in incidence. Monthly mean minimum temperature, rainfall, area under urban settlement/build-up areas and altitude were significant in the final model. Minimum temperature and rainfall had non-linear effects and lagging them by two months provided a better fitting model compared to using unlagged variables. Forecasts for the validation period closely mirrored the observed data and accurately captured the troughs and peaks of dengue incidence trajectories. A favorable Theil's coefficient of inequality of 0.22 was generated.CONCLUSIONS: The study identified temperature, rainfall, altitude and area under urban settlement as being significant predictors of dengue incidence. The statistical model fitted the data well based on Theil's coefficient of inequality, and risk maps generated from its predictions identified most of the high-risk provinces throughout the country.
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| 2. |
- Thi Tuyet-Hanh, Tran, et al.
(författare)
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Climate Variability and Dengue Hemorrhagic Fever in Hanoi, Viet Nam, During 2008 to 2015
- 2018
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Ingår i: Asia Pacific journal of public health. - : Sage Publications. - 1941-2479 .- 1010-5395. ; 30:6, s. 532-541
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Tidskriftsartikel (refereegranskat)abstract
- Dengue fever/dengue hemorrhagic fever (DF/DHF) has been an important public health challenge in Viet Nam and worldwide. This study was implemented in 2016-2017 using retrospective secondary data to explore associations between monthly DF/DHF cases and climate variables during 2008 to 2015. There were 48 175 DF/DHF cases reported, and the highest number of cases occurred in November. There were significant correlations between monthly DF/DHF cases with monthly mean of evaporation (r = 0.236, P < .05), monthly relative humidity (r = −0.358, P < .05), and monthly total hours of sunshine (r = 0.389, P < .05). The results showed significant correlation in lag models but did not find direct correlations between monthly DF/DHF cases and monthly average rainfall and temperature. The study recommended that health staff in Hanoi should monitor DF/DHF cases at the beginning of epidemic period, starting from May, and apply timely prevention and intervention measures to avoid the spreading of the disease in the following months. A larger scale study for a longer period of time and adjusting for other potential influencing factors could better describe the correlations, modelling/projection, and developing an early warning system for the disease, which is important under the impacts of climate change and climate variability.
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| 3. |
- Kien, Tran Mai, et al.
(författare)
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Climate Services For Infectious Disease Control: A Nexus Between Public Health Preparedness and Sustainable Development, Lessons Learned From Long-Term Multi Site Time Series Analysis of Dengue Fever in Vietnam
- 2016
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Ingår i: International conference on public health: Accelerating the achievement of sustainable development goals for the improvement and equitable distribution of population health. ; , s. 83-84
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Background: Climate Services provide valuable information for making actionable, data-driven decisions to protect public health in a myriad of manners. There is mounting global evidence of the looming threat climate change poses to human health, including the variability and intensity of infectious disease outbreaks in Vietnam and other low-resource and developing areas. In light of the Sustainable Development Goals, lessons learned from time-series analysis may inform public health preparedness strategies for sustainable urban development in terms of dengue epidemiology, surveillance, control, and early warnings.Subjects and Methods: Nearly 40 years of spatial and temporal (times-series) dataset of meteorological records, including rainfall, temperature, and humidity (among others) which can be predictors of dengue were assembled for all provinces of Vietnam and associated with case data reported to General Department of Preventive Medicine, Ministry of Health of Vietnam during the same period. Time series of climate and disease variables was analyzed for trends and changing patterns of those variables over time. The time-series statistical analysis methods sought to identify spatial (when possible) and temporal trends, seasonality, cyclical patterns of disease, and to discover anomalous outbreak events, which departed from expected epidemiological patterns and corresponding meteorological phenomena, such as El Nino Southern Oscillation (ENSO).Results: Analysis yielded largely conserved finding with other locations in South East Asia for larger Outbreak years and events such as ENSO. Seasonality, trend, and cycle in many provinces were persistent throughout the dataset, indicating strong potential for Climate Services to be used in dengue early warnings.Conclusion: Even public health practitioners, having adequate tools for dengue control available must plan and budget vector control and patient treatment efforts well in advance of large scale dengue epidemics to curb such events overall morbidity and mortality. Similarly, urban and sustainable development in Vietnam might benefit from evidence linking climate change, and ill-health events spatially and temporally in future planning. Long term analysis of dengue case data and meteorological records, provided a cases study evidence for emerging opportunities that on how refined climate services could contribute to protection of public health.
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