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Predicting the potential risk area of illegal vaccine trade in China

Since the disclosure of the “Illegal vaccine operation series case in Jinan, Shandong” in March, 2016, this issue has attracted a great deal of attention and has led to public concerns about the safety and efficacy of the vaccines involved in this case. The main purpose of this paper is to scientifi...

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Autores principales: Liao, Yilan, Lei, Yanhui, Ren, Zhoupeng, Chen, Huiyan, Li, Dongyue
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5478654/
https://www.ncbi.nlm.nih.gov/pubmed/28634332
http://dx.doi.org/10.1038/s41598-017-03512-3
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author Liao, Yilan
Lei, Yanhui
Ren, Zhoupeng
Chen, Huiyan
Li, Dongyue
author_facet Liao, Yilan
Lei, Yanhui
Ren, Zhoupeng
Chen, Huiyan
Li, Dongyue
author_sort Liao, Yilan
collection PubMed
description Since the disclosure of the “Illegal vaccine operation series case in Jinan, Shandong” in March, 2016, this issue has attracted a great deal of attention and has led to public concerns about the safety and efficacy of the vaccines involved in this case. The main purpose of this paper is to scientifically and scrupulously predict the possible geographic distribution of illegal vaccines in China, and provide a foundation to guide future governmental policies and actions. A species distribution model was used because of the advantages of using presence/pseudo-absence or presence-only data, and it performs well with incomplete species distribution data. A series of socioeconomic variables were used to simulate habitat suitability distribution. Maxent (Maximum Entropy Model) and GARP (Genetic Algorithm for Rule set Production) were used to predict the risks of illegal vaccines in China, and define the spatial distribution and significant factors of the area at risk from illegal vaccines. Jackknife tests were used to evaluate the relative importance of socioeconomic variables. It was found that: (1) Shandong, Hebei, Henan, Jiangsu and Anhui are the main high-risk areas impacted by the vaccines involved in Jinan case. (2) Population density and industrial structure are the main socioeconomic factors affecting areas which may be at risk from illegal vaccines.
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spelling pubmed-54786542017-06-23 Predicting the potential risk area of illegal vaccine trade in China Liao, Yilan Lei, Yanhui Ren, Zhoupeng Chen, Huiyan Li, Dongyue Sci Rep Article Since the disclosure of the “Illegal vaccine operation series case in Jinan, Shandong” in March, 2016, this issue has attracted a great deal of attention and has led to public concerns about the safety and efficacy of the vaccines involved in this case. The main purpose of this paper is to scientifically and scrupulously predict the possible geographic distribution of illegal vaccines in China, and provide a foundation to guide future governmental policies and actions. A species distribution model was used because of the advantages of using presence/pseudo-absence or presence-only data, and it performs well with incomplete species distribution data. A series of socioeconomic variables were used to simulate habitat suitability distribution. Maxent (Maximum Entropy Model) and GARP (Genetic Algorithm for Rule set Production) were used to predict the risks of illegal vaccines in China, and define the spatial distribution and significant factors of the area at risk from illegal vaccines. Jackknife tests were used to evaluate the relative importance of socioeconomic variables. It was found that: (1) Shandong, Hebei, Henan, Jiangsu and Anhui are the main high-risk areas impacted by the vaccines involved in Jinan case. (2) Population density and industrial structure are the main socioeconomic factors affecting areas which may be at risk from illegal vaccines. Nature Publishing Group UK 2017-06-20 /pmc/articles/PMC5478654/ /pubmed/28634332 http://dx.doi.org/10.1038/s41598-017-03512-3 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Liao, Yilan
Lei, Yanhui
Ren, Zhoupeng
Chen, Huiyan
Li, Dongyue
Predicting the potential risk area of illegal vaccine trade in China
title Predicting the potential risk area of illegal vaccine trade in China
title_full Predicting the potential risk area of illegal vaccine trade in China
title_fullStr Predicting the potential risk area of illegal vaccine trade in China
title_full_unstemmed Predicting the potential risk area of illegal vaccine trade in China
title_short Predicting the potential risk area of illegal vaccine trade in China
title_sort predicting the potential risk area of illegal vaccine trade in china
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5478654/
https://www.ncbi.nlm.nih.gov/pubmed/28634332
http://dx.doi.org/10.1038/s41598-017-03512-3
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