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Spatiotemporal characteristics and primary influencing factors of typical dengue fever epidemics in China

BACKGROUND: Dengue fever (DF) is a common mosquito-borne viral infectious disease in the world, and increasingly severe DF epidemics in China have seriously affected people’s health in recent years. Thus, investigating spatiotemporal patterns and potential influencing factors of DF epidemics in typi...

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Autores principales: Zheng, Lan, Ren, Hong-Yan, Shi, Run-He, Lu, Liang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6440137/
https://www.ncbi.nlm.nih.gov/pubmed/30922405
http://dx.doi.org/10.1186/s40249-019-0533-9
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author Zheng, Lan
Ren, Hong-Yan
Shi, Run-He
Lu, Liang
author_facet Zheng, Lan
Ren, Hong-Yan
Shi, Run-He
Lu, Liang
author_sort Zheng, Lan
collection PubMed
description BACKGROUND: Dengue fever (DF) is a common mosquito-borne viral infectious disease in the world, and increasingly severe DF epidemics in China have seriously affected people’s health in recent years. Thus, investigating spatiotemporal patterns and potential influencing factors of DF epidemics in typical regions is critical to consolidate effective prevention and control measures for these regional epidemics. METHODS: A generalized additive model (GAM) was used to identify potential contributing factors that influence spatiotemporal epidemic patterns in typical DF epidemic regions of China (e.g., the Pearl River Delta [PRD] and the Border of Yunnan and Myanmar [BYM]). In terms of influencing factors, environmental factors including the normalized difference vegetation index (NDVI), temperature, precipitation, and humidity, in conjunction with socioeconomic factors, such as population density (Pop), road density, land-use, and gross domestic product, were employed. RESULTS: DF epidemics in the PRD and BYM exhibit prominent spatial variations at 4 km and 3 km grid scales, characterized by significant spatial clustering over the Guangzhou-Foshan, Dehong, and Xishuangbanna areas. The GAM that integrated the Pop-urban land ratio (ULR)-NDVI-humidity-temperature factors for the PRD and the ULR-Road density-NDVI-temperature-water land ratio-precipitation factors for the BYM performed well in terms of overall accuracy, with Akaike Information Criterion values of 61 859.89 and 826.65, explaining a total variance of 83.4 and 97.3%, respectively. As indicated, socioeconomic factors have a stronger influence on DF epidemics than environmental factors in the study area. Among these factors, Pop (PRD) and ULR (BYM) were the socioeconomic factors explaining the largest variance in regional epidemics, whereas NDVI was the environmental factor explaining the largest variance in both regions. In addition, the common factors (ULR, NDVI, and temperature) in these two regions exhibited different effects on regional epidemics. CONCLUSIONS: The spatiotemporal patterns of DF in the PRD and BYM are influenced by environmental and socioeconomic factors, the socioeconomic factors may play a significant role in DF epidemics in cases where environmental factors are suitable and differ only slightly throughout an area. Thus, prevention and control resources should be fully allocated by referring to the spatial patterns of primary influencing factors to better consolidate the prevention and control measures for DF epidemics. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40249-019-0533-9) contains supplementary material, which is available to authorized users.
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spelling pubmed-64401372019-04-11 Spatiotemporal characteristics and primary influencing factors of typical dengue fever epidemics in China Zheng, Lan Ren, Hong-Yan Shi, Run-He Lu, Liang Infect Dis Poverty Research Article BACKGROUND: Dengue fever (DF) is a common mosquito-borne viral infectious disease in the world, and increasingly severe DF epidemics in China have seriously affected people’s health in recent years. Thus, investigating spatiotemporal patterns and potential influencing factors of DF epidemics in typical regions is critical to consolidate effective prevention and control measures for these regional epidemics. METHODS: A generalized additive model (GAM) was used to identify potential contributing factors that influence spatiotemporal epidemic patterns in typical DF epidemic regions of China (e.g., the Pearl River Delta [PRD] and the Border of Yunnan and Myanmar [BYM]). In terms of influencing factors, environmental factors including the normalized difference vegetation index (NDVI), temperature, precipitation, and humidity, in conjunction with socioeconomic factors, such as population density (Pop), road density, land-use, and gross domestic product, were employed. RESULTS: DF epidemics in the PRD and BYM exhibit prominent spatial variations at 4 km and 3 km grid scales, characterized by significant spatial clustering over the Guangzhou-Foshan, Dehong, and Xishuangbanna areas. The GAM that integrated the Pop-urban land ratio (ULR)-NDVI-humidity-temperature factors for the PRD and the ULR-Road density-NDVI-temperature-water land ratio-precipitation factors for the BYM performed well in terms of overall accuracy, with Akaike Information Criterion values of 61 859.89 and 826.65, explaining a total variance of 83.4 and 97.3%, respectively. As indicated, socioeconomic factors have a stronger influence on DF epidemics than environmental factors in the study area. Among these factors, Pop (PRD) and ULR (BYM) were the socioeconomic factors explaining the largest variance in regional epidemics, whereas NDVI was the environmental factor explaining the largest variance in both regions. In addition, the common factors (ULR, NDVI, and temperature) in these two regions exhibited different effects on regional epidemics. CONCLUSIONS: The spatiotemporal patterns of DF in the PRD and BYM are influenced by environmental and socioeconomic factors, the socioeconomic factors may play a significant role in DF epidemics in cases where environmental factors are suitable and differ only slightly throughout an area. Thus, prevention and control resources should be fully allocated by referring to the spatial patterns of primary influencing factors to better consolidate the prevention and control measures for DF epidemics. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s40249-019-0533-9) contains supplementary material, which is available to authorized users. BioMed Central 2019-03-28 /pmc/articles/PMC6440137/ /pubmed/30922405 http://dx.doi.org/10.1186/s40249-019-0533-9 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Zheng, Lan
Ren, Hong-Yan
Shi, Run-He
Lu, Liang
Spatiotemporal characteristics and primary influencing factors of typical dengue fever epidemics in China
title Spatiotemporal characteristics and primary influencing factors of typical dengue fever epidemics in China
title_full Spatiotemporal characteristics and primary influencing factors of typical dengue fever epidemics in China
title_fullStr Spatiotemporal characteristics and primary influencing factors of typical dengue fever epidemics in China
title_full_unstemmed Spatiotemporal characteristics and primary influencing factors of typical dengue fever epidemics in China
title_short Spatiotemporal characteristics and primary influencing factors of typical dengue fever epidemics in China
title_sort spatiotemporal characteristics and primary influencing factors of typical dengue fever epidemics in china
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6440137/
https://www.ncbi.nlm.nih.gov/pubmed/30922405
http://dx.doi.org/10.1186/s40249-019-0533-9
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