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Scrub typhus in Jiangsu Province, China: epidemiologic features and spatial risk analysis

BACKGROUND: With the increasing incidence of scrub typhus in recent years, it is of great value to analyse the spatial and temporal distribution of scrub typhus by applying micro-geographical studies at a reasonably fine scale, and to guide the control and management. METHODS: We explored the use of...

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Autores principales: Yu, Huiyan, Sun, Changkui, Liu, Wendong, Li, Zhifeng, Tan, Zhongming, Wang, Xiaochen, Hu, Jianli, Shi, Shanqiu, Bao, Changjun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6080521/
https://www.ncbi.nlm.nih.gov/pubmed/30081828
http://dx.doi.org/10.1186/s12879-018-3271-x
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author Yu, Huiyan
Sun, Changkui
Liu, Wendong
Li, Zhifeng
Tan, Zhongming
Wang, Xiaochen
Hu, Jianli
Shi, Shanqiu
Bao, Changjun
author_facet Yu, Huiyan
Sun, Changkui
Liu, Wendong
Li, Zhifeng
Tan, Zhongming
Wang, Xiaochen
Hu, Jianli
Shi, Shanqiu
Bao, Changjun
author_sort Yu, Huiyan
collection PubMed
description BACKGROUND: With the increasing incidence of scrub typhus in recent years, it is of great value to analyse the spatial and temporal distribution of scrub typhus by applying micro-geographical studies at a reasonably fine scale, and to guide the control and management. METHODS: We explored the use of maximum entropy modelling method to confirm the spatial and temporal distribution of scrub typhus according to the occurrence locations of human cases in Jiangsu Province. The risk prediction map under specific environmental factors was therefore drawn by projecting the training model across China. The area under the curve and the omission rate were used to validate the model. Meanwhile, Jackknife tests were applied to enumerate the contribution of different environmental variables, then to predict the final model. The predicted results were validated by using China’s known occurrence locations. RESULTS: A total of 566 occurrence locations with known 4865 scrub typhus occurrence records were used in our study. The number of female cases was higher than male cases, with a proportion of 1.17:1, and people in any age group could be infected. The number of cases presented an inverted-U relation with age. The percentage of cases aged from 60 to 69 years old was the highest, accounting for 30.50% of all cases. Ecological niche modelling results indicated that the locations of scrub typhus cases, which was of great importance in the disease transmission cycle, had a certain ecological niche with environmental elements in many dimensions. Moreover, the key environmental factors for determining scrub typhus occurrence were temperature (including temperature seasonality, min temperature of coldest month, mean diurnal range, and monthly mean temperature), precipitation of wettest month, and land cover types. The risk prediction maps indicated that mid-eastern China was the potential risk areas for scrub typhus of “autumn type”. Meanwhile, in our results, Guangdong Province was the high-risk region for “autumn type” scrub typhus, where cases were mainly reported as “summer type”. CONCLUSION: The combination of climatic and geographic factors with GIS methods is an appropriate option to analyse and estimate the spatial and temporal distribution of scrub typhus.
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spelling pubmed-60805212018-08-09 Scrub typhus in Jiangsu Province, China: epidemiologic features and spatial risk analysis Yu, Huiyan Sun, Changkui Liu, Wendong Li, Zhifeng Tan, Zhongming Wang, Xiaochen Hu, Jianli Shi, Shanqiu Bao, Changjun BMC Infect Dis Research Article BACKGROUND: With the increasing incidence of scrub typhus in recent years, it is of great value to analyse the spatial and temporal distribution of scrub typhus by applying micro-geographical studies at a reasonably fine scale, and to guide the control and management. METHODS: We explored the use of maximum entropy modelling method to confirm the spatial and temporal distribution of scrub typhus according to the occurrence locations of human cases in Jiangsu Province. The risk prediction map under specific environmental factors was therefore drawn by projecting the training model across China. The area under the curve and the omission rate were used to validate the model. Meanwhile, Jackknife tests were applied to enumerate the contribution of different environmental variables, then to predict the final model. The predicted results were validated by using China’s known occurrence locations. RESULTS: A total of 566 occurrence locations with known 4865 scrub typhus occurrence records were used in our study. The number of female cases was higher than male cases, with a proportion of 1.17:1, and people in any age group could be infected. The number of cases presented an inverted-U relation with age. The percentage of cases aged from 60 to 69 years old was the highest, accounting for 30.50% of all cases. Ecological niche modelling results indicated that the locations of scrub typhus cases, which was of great importance in the disease transmission cycle, had a certain ecological niche with environmental elements in many dimensions. Moreover, the key environmental factors for determining scrub typhus occurrence were temperature (including temperature seasonality, min temperature of coldest month, mean diurnal range, and monthly mean temperature), precipitation of wettest month, and land cover types. The risk prediction maps indicated that mid-eastern China was the potential risk areas for scrub typhus of “autumn type”. Meanwhile, in our results, Guangdong Province was the high-risk region for “autumn type” scrub typhus, where cases were mainly reported as “summer type”. CONCLUSION: The combination of climatic and geographic factors with GIS methods is an appropriate option to analyse and estimate the spatial and temporal distribution of scrub typhus. BioMed Central 2018-08-06 /pmc/articles/PMC6080521/ /pubmed/30081828 http://dx.doi.org/10.1186/s12879-018-3271-x Text en © The Author(s). 2018 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
Yu, Huiyan
Sun, Changkui
Liu, Wendong
Li, Zhifeng
Tan, Zhongming
Wang, Xiaochen
Hu, Jianli
Shi, Shanqiu
Bao, Changjun
Scrub typhus in Jiangsu Province, China: epidemiologic features and spatial risk analysis
title Scrub typhus in Jiangsu Province, China: epidemiologic features and spatial risk analysis
title_full Scrub typhus in Jiangsu Province, China: epidemiologic features and spatial risk analysis
title_fullStr Scrub typhus in Jiangsu Province, China: epidemiologic features and spatial risk analysis
title_full_unstemmed Scrub typhus in Jiangsu Province, China: epidemiologic features and spatial risk analysis
title_short Scrub typhus in Jiangsu Province, China: epidemiologic features and spatial risk analysis
title_sort scrub typhus in jiangsu province, china: epidemiologic features and spatial risk analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6080521/
https://www.ncbi.nlm.nih.gov/pubmed/30081828
http://dx.doi.org/10.1186/s12879-018-3271-x
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