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Factors associated with hemorrhagic fever with renal syndrome based maximum entropy model in Zhejiang Province, China
BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS) is a serious public health problem in China. The geographic distribution has went throughout China, among which Zhejiang Province is an important epidemic area. Since 1963, more than 110,000 cases have been reported. METHODS: We collected the...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9579348/ https://www.ncbi.nlm.nih.gov/pubmed/36275790 http://dx.doi.org/10.3389/fmed.2022.967554 |
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author | Zhang, Rong Zhang, Ning Liu, Ying Liu, Tianxiao Sun, Jimin Ling, Feng Wang, Zhen |
author_facet | Zhang, Rong Zhang, Ning Liu, Ying Liu, Tianxiao Sun, Jimin Ling, Feng Wang, Zhen |
author_sort | Zhang, Rong |
collection | PubMed |
description | BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS) is a serious public health problem in China. The geographic distribution has went throughout China, among which Zhejiang Province is an important epidemic area. Since 1963, more than 110,000 cases have been reported. METHODS: We collected the meteorological factors and socioeconomic indicators of Zhejiang Province, and constructed the HFRS ecological niche model of Zhejiang Province based on the algorithm of maximum entropy. RESULTS: Model AUC from 2009 to 2018, is 0.806–0.901. The high incidence of epidemics in Zhejiang Province is mainly concentrated in the eastern, western and central regions of Zhejiang Province. The contribution of digital elevation model ranged from 2009 to 2018 from 4.22 to 26.0%. The contribution of average temperature ranges from 6.26 to 19.65%, Gross Domestic Product contribution from 7.53 to 21.25%, and average land surface temperature contribution with the highest being 16.73% in 2011. In addition, the average contribution of DMSP/OLS, 20-8 precipitation and 8-20 precipitation were all in the range of 9%. All-day precipitation increases with the increase of rainfall, and the effect curve peaks at 1,250 mm, then decreases rapidly, and a small peak appears again at 1,500 mm. Average temperature response curve shows an inverted v-shape, where the incidence peaks at 17.8(°)C. The response curve of HFRS for GDP and DMSP/OLS shows a positive correlation. CONCLUSION: The incidence of HFRS in Zhejiang Province peaked in areas where the average temperature was 17.8(°)C, which reminds that in the areas where temperature is suitable, personal protection should be taken when going out as to avoid contact with rodents. The impact of GDP and DMSP/OLS on HFRS is positively correlated. Most cities have good medical conditions, but we should consider whether there are under-diagnosed cases in economically underdeveloped areas. |
format | Online Article Text |
id | pubmed-9579348 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95793482022-10-20 Factors associated with hemorrhagic fever with renal syndrome based maximum entropy model in Zhejiang Province, China Zhang, Rong Zhang, Ning Liu, Ying Liu, Tianxiao Sun, Jimin Ling, Feng Wang, Zhen Front Med (Lausanne) Medicine BACKGROUND: Hemorrhagic fever with renal syndrome (HFRS) is a serious public health problem in China. The geographic distribution has went throughout China, among which Zhejiang Province is an important epidemic area. Since 1963, more than 110,000 cases have been reported. METHODS: We collected the meteorological factors and socioeconomic indicators of Zhejiang Province, and constructed the HFRS ecological niche model of Zhejiang Province based on the algorithm of maximum entropy. RESULTS: Model AUC from 2009 to 2018, is 0.806–0.901. The high incidence of epidemics in Zhejiang Province is mainly concentrated in the eastern, western and central regions of Zhejiang Province. The contribution of digital elevation model ranged from 2009 to 2018 from 4.22 to 26.0%. The contribution of average temperature ranges from 6.26 to 19.65%, Gross Domestic Product contribution from 7.53 to 21.25%, and average land surface temperature contribution with the highest being 16.73% in 2011. In addition, the average contribution of DMSP/OLS, 20-8 precipitation and 8-20 precipitation were all in the range of 9%. All-day precipitation increases with the increase of rainfall, and the effect curve peaks at 1,250 mm, then decreases rapidly, and a small peak appears again at 1,500 mm. Average temperature response curve shows an inverted v-shape, where the incidence peaks at 17.8(°)C. The response curve of HFRS for GDP and DMSP/OLS shows a positive correlation. CONCLUSION: The incidence of HFRS in Zhejiang Province peaked in areas where the average temperature was 17.8(°)C, which reminds that in the areas where temperature is suitable, personal protection should be taken when going out as to avoid contact with rodents. The impact of GDP and DMSP/OLS on HFRS is positively correlated. Most cities have good medical conditions, but we should consider whether there are under-diagnosed cases in economically underdeveloped areas. Frontiers Media S.A. 2022-10-05 /pmc/articles/PMC9579348/ /pubmed/36275790 http://dx.doi.org/10.3389/fmed.2022.967554 Text en Copyright © 2022 Zhang, Zhang, Liu, Liu, Sun, Ling and Wang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine Zhang, Rong Zhang, Ning Liu, Ying Liu, Tianxiao Sun, Jimin Ling, Feng Wang, Zhen Factors associated with hemorrhagic fever with renal syndrome based maximum entropy model in Zhejiang Province, China |
title | Factors associated with hemorrhagic fever with renal syndrome based maximum entropy model in Zhejiang Province, China |
title_full | Factors associated with hemorrhagic fever with renal syndrome based maximum entropy model in Zhejiang Province, China |
title_fullStr | Factors associated with hemorrhagic fever with renal syndrome based maximum entropy model in Zhejiang Province, China |
title_full_unstemmed | Factors associated with hemorrhagic fever with renal syndrome based maximum entropy model in Zhejiang Province, China |
title_short | Factors associated with hemorrhagic fever with renal syndrome based maximum entropy model in Zhejiang Province, China |
title_sort | factors associated with hemorrhagic fever with renal syndrome based maximum entropy model in zhejiang province, china |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9579348/ https://www.ncbi.nlm.nih.gov/pubmed/36275790 http://dx.doi.org/10.3389/fmed.2022.967554 |
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