Article ; Online: Predicting the potential risk area of illegal vaccine trade in China.
2017 Volume 7, Issue 1, Page(s) 3883
Abstract: ... 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 scientifically and scrupulously predict the possible geographic distribution of illegal vaccines in China, and ...
| Abstract | 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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| MeSH term(s) | Area Under Curve ; China ; Criminal Behavior ; Drug Trafficking ; Geography ; Humans ; ROC Curve ; Risk ; Socioeconomic Factors ; Vaccines/adverse effects |
| Chemical Substances | Vaccines |
| Language | English |
| Publishing date | 2017-06-20 |
| Publishing country | England |
| Document type | Journal Article ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 2615211-3 |
| ISSN | 2045-2322 ; 2045-2322 |
| ISSN (online) | 2045-2322 |
| ISSN | 2045-2322 |
| DOI | 10.1038/s41598-017-03512-3 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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