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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

Bett, Bernard and Grace, Delia and Lee, Hu Suk and Lindahl, Johanna and Nguyen-Viet, H. and Phuc, Pham-Duc and Quyen, Nguyen Huu and Tu, Tran Anh and Phu, Tran Dac and Tan, Dang Quang and Nam, Vu Sinh (2019). Spatiotemporal analysis of historical records (2001-2012) on dengue fever in Vietnam and development of a statistical model for forecasting risk. PLoS ONE. 14 , e0224353 , 1-22
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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.

Authors/Creators:Bett, Bernard and Grace, Delia and Lee, Hu Suk and Lindahl, Johanna and Nguyen-Viet, H. and Phuc, Pham-Duc and Quyen, Nguyen Huu and Tu, Tran Anh and Phu, Tran Dac and Tan, Dang Quang and Nam, Vu Sinh
Title:Spatiotemporal analysis of historical records (2001-2012) on dengue fever in Vietnam and development of a statistical model for forecasting risk
Year of publishing :2019
Volume:14
Article number:e0224353
Number of Pages:22
Publisher:PLoS
ISSN:1932-6203
Language:English
Publication Type:Journal article
Article category:Scientific peer reviewed
Version:Published version
Copyright:Creative Commons: Attribution 4.0
Full Text Status:Public
Subjects:(A) Swedish standard research categories 2011 > 3 Medical and Health Sciences > 303 Health Sciences > Public Health, Global Health, Social Medicine and Epidemiology
URN:NBN:urn:nbn:se:slu:epsilon-p-104628
Permanent URL:
http://urn.kb.se/resolve?urn=urn:nbn:se:slu:epsilon-p-104628
Additional ID:
Type of IDID
DOI10.1371/journal.pone.0224353
Web of Science (WoS)000533896400010
ID Code:17228
Faculty:VH - Faculty of Veterinary Medicine and Animal Science
Department:(VH) > Dept. of Clinical Sciences
Deposited By: SLUpub Connector
Deposited On:10 Jul 2020 09:47
Metadata Last Modified:10 Jul 2020 09:47

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