Spatiotemporal analysis of historical records (2001–2012) on dengue fever in Vietnam and development of a statistical model for forecasting risk

Autoři: Bernard Bett aff001;  Delia Grace aff001;  Hu Suk Lee aff002;  Johanna Lindahl aff001;  Hung Nguyen-Viet aff002;  Pham-Duc Phuc aff005;  Nguyen Huu Quyen aff006;  Tran Anh Tu aff007;  Tran Dac Phu aff008;  Dang Quang Tan aff008;  Vu Sinh Nam aff007
Působiště autorů: International Livestock Research Institute, Nairobi, Kenya aff001;  International Livestock Research Institute, Regional Office for East and Southeast Asia, Hanoi, Vietnam aff002;  Uppsala University, Uppsala, Sweden aff003;  Swedish University of Agricultural Sciences, Uppsala, Sweden aff004;  Centre for Public Health and Ecosystem Research (CENPHER), Hanoi University of Public Health, Hanoi, Vietnam aff005;  Vietnam Institute of Meteorology, Hydrology and Climate Change (IMHEN), Hanoi, Vietnam aff006;  National Institute of Hygiene and Epidemiology, Hanoi, Vietnam aff007;  General Department of Preventive Medicine, Ministry of Health, Hanoi, Vietnam aff008
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0224353



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.


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.


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.


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.

Klíčová slova:

Dengue fever – Dengue virus – Mosquitoes – Rain – Seasons – Vietnam – Wetlands


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2019 Číslo 11