Impact of meteorological factors on the prevalence of porcine pasteurellosis in the southcentral of Mainland China.
Identifieur interne : 000044 ( PubMed/Corpus ); précédent : 000043; suivant : 000045Impact of meteorological factors on the prevalence of porcine pasteurellosis in the southcentral of Mainland China.
Auteurs : Xiang Gao ; Jianhua Xiao ; Hongyu Qin ; Zheng Cao ; Hongbin WangSource :
- Preventive veterinary medicine [ 1873-1716 ] ; 2016.
English descriptors
- KwdEn :
- Animals, China (epidemiology), Climate, Cluster Analysis, Disease Outbreaks (veterinary), Geographic Information Systems, Pasteurella (physiology), Pasteurella Infections (epidemiology), Pasteurella Infections (microbiology), Pasteurella Infections (veterinary), Prevalence, Swine, Swine Diseases (epidemiology), Swine Diseases (microbiology), Weather.
- MESH :
- epidemiology : China, Pasteurella Infections, Swine Diseases.
- microbiology : Pasteurella Infections, Swine Diseases.
- physiology : Pasteurella.
- veterinary : Disease Outbreaks, Pasteurella Infections.
- Animals, Climate, Cluster Analysis, Geographic Information Systems, Prevalence, Swine, Weather.
Abstract
Using data collected from 2006 to 2014, we applied geographic information system (GIS) mapping and spatial clustering analysis to evaluate prevalence of porcine pasteurellosis in all 31 provinces of Mainland China. All provinces have been affected, but our results show that there is a very high incidence in provinces of the southcentral of Mainland China. Six provinces comprise the area and account for 14082 outbreaks or 74.66% of the total 18862 number: Guangxi (4574), Sichuan (3493), Chongqing (2443), Guangdong (1584), Guizou (1041) and Yunnan (947). This study aims to evaluate the relation between meteorological factors and number of cases of porcine pasteurellosis in the southcentral of Mainland China. Local meteorological variables and case data of porcine pasteurellosis were provided by authorities. Spearman rank correlation analysis and cross-correlation analysis were used to control for collinearity and lag effects. A zero-inflated Poisson model was used to estimate the probability of an impact of meteorological factors in the epidemiology of porcine pasteurellosis. The results of this model indicated that ENSO have a positive effect on the occurrence of the disease. And there is a positive correlation between mean monthly temperature, relative humidity of the current and previous month and the number of cases of the disease. In contrast, average wind speed of the current month negatively correlated to the number of newly reported cases. Our findings indicate that there may exist meteorological conditions in the southcentral of Mainland China that increase the risk for the appearance of porcine pasteurellosis. Moreover, these meteorological variables may be used to estimate the number of disease' cases in this region.
DOI: 10.1016/j.prevetmed.2016.01.002
PubMed: 26796426
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pubmed:26796426Le document en format XML
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Electronic address: hbwang1940@163.com.</nlm:affiliation></affiliation></author></analytic><series><title level="j">Preventive veterinary medicine</title><idno type="eISSN">1873-1716</idno><imprint><date when="2016" type="published">2016</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Animals</term><term>China (epidemiology)</term><term>Climate</term><term>Cluster Analysis</term><term>Disease Outbreaks (veterinary)</term><term>Geographic Information Systems</term><term>Pasteurella (physiology)</term><term>Pasteurella Infections (epidemiology)</term><term>Pasteurella Infections (microbiology)</term><term>Pasteurella Infections (veterinary)</term><term>Prevalence</term><term>Swine</term><term>Swine Diseases (epidemiology)</term><term>Swine Diseases (microbiology)</term><term>Weather</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>China</term><term>Pasteurella Infections</term><term>Swine Diseases</term></keywords><keywords scheme="MESH" qualifier="microbiology" xml:lang="en"><term>Pasteurella Infections</term><term>Swine Diseases</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Pasteurella</term></keywords><keywords scheme="MESH" qualifier="veterinary" xml:lang="en"><term>Disease Outbreaks</term><term>Pasteurella Infections</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Animals</term><term>Climate</term><term>Cluster Analysis</term><term>Geographic Information Systems</term><term>Prevalence</term><term>Swine</term><term>Weather</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Using data collected from 2006 to 2014, we applied geographic information system (GIS) mapping and spatial clustering analysis to evaluate prevalence of porcine pasteurellosis in all 31 provinces of Mainland China. All provinces have been affected, but our results show that there is a very high incidence in provinces of the southcentral of Mainland China. Six provinces comprise the area and account for 14082 outbreaks or 74.66% of the total 18862 number: Guangxi (4574), Sichuan (3493), Chongqing (2443), Guangdong (1584), Guizou (1041) and Yunnan (947). This study aims to evaluate the relation between meteorological factors and number of cases of porcine pasteurellosis in the southcentral of Mainland China. Local meteorological variables and case data of porcine pasteurellosis were provided by authorities. Spearman rank correlation analysis and cross-correlation analysis were used to control for collinearity and lag effects. A zero-inflated Poisson model was used to estimate the probability of an impact of meteorological factors in the epidemiology of porcine pasteurellosis. The results of this model indicated that ENSO have a positive effect on the occurrence of the disease. And there is a positive correlation between mean monthly temperature, relative humidity of the current and previous month and the number of cases of the disease. In contrast, average wind speed of the current month negatively correlated to the number of newly reported cases. Our findings indicate that there may exist meteorological conditions in the southcentral of Mainland China that increase the risk for the appearance of porcine pasteurellosis. Moreover, these meteorological variables may be used to estimate the number of disease' cases in this region.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">26796426</PMID><DateCreated><Year>2016</Year><Month>02</Month><Day>26</Day></DateCreated><DateCompleted><Year>2016</Year><Month>11</Month><Day>14</Day></DateCompleted><DateRevised><Year>2016</Year><Month>12</Month><Day>30</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1873-1716</ISSN><JournalIssue CitedMedium="Internet"><Volume>125</Volume><PubDate><Year>2016</Year><Month>Mar</Month><Day>01</Day></PubDate></JournalIssue><Title>Preventive veterinary medicine</Title><ISOAbbreviation>Prev. Vet. Med.</ISOAbbreviation></Journal><ArticleTitle>Impact of meteorological factors on the prevalence of porcine pasteurellosis in the southcentral of Mainland China.</ArticleTitle><Pagination><MedlinePgn>75-81</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.prevetmed.2016.01.002</ELocationID><ELocationID EIdType="pii" ValidYN="Y">S0167-5877(16)30004-6</ELocationID><Abstract><AbstractText>Using data collected from 2006 to 2014, we applied geographic information system (GIS) mapping and spatial clustering analysis to evaluate prevalence of porcine pasteurellosis in all 31 provinces of Mainland China. All provinces have been affected, but our results show that there is a very high incidence in provinces of the southcentral of Mainland China. Six provinces comprise the area and account for 14082 outbreaks or 74.66% of the total 18862 number: Guangxi (4574), Sichuan (3493), Chongqing (2443), Guangdong (1584), Guizou (1041) and Yunnan (947). This study aims to evaluate the relation between meteorological factors and number of cases of porcine pasteurellosis in the southcentral of Mainland China. Local meteorological variables and case data of porcine pasteurellosis were provided by authorities. Spearman rank correlation analysis and cross-correlation analysis were used to control for collinearity and lag effects. A zero-inflated Poisson model was used to estimate the probability of an impact of meteorological factors in the epidemiology of porcine pasteurellosis. The results of this model indicated that ENSO have a positive effect on the occurrence of the disease. And there is a positive correlation between mean monthly temperature, relative humidity of the current and previous month and the number of cases of the disease. In contrast, average wind speed of the current month negatively correlated to the number of newly reported cases. Our findings indicate that there may exist meteorological conditions in the southcentral of Mainland China that increase the risk for the appearance of porcine pasteurellosis. Moreover, these meteorological variables may be used to estimate the number of disease' cases in this region.</AbstractText><CopyrightInformation>Copyright © 2016 Elsevier B.V. All rights reserved.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Gao</LastName><ForeName>Xiang</ForeName><Initials>X</Initials><AffiliationInfo><Affiliation>Department of Veterinary Surgery, Northeast Agricultural University, Harbin, Heilongjiang, PR China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Xiao</LastName><ForeName>Jianhua</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>Department of Veterinary Surgery, Northeast Agricultural University, Harbin, Heilongjiang, PR China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Qin</LastName><ForeName>Hongyu</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>Department of Veterinary Surgery, Northeast Agricultural University, Harbin, Heilongjiang, PR China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Cao</LastName><ForeName>Zheng</ForeName><Initials>Z</Initials><AffiliationInfo><Affiliation>Department of Veterinary Surgery, Northeast Agricultural University, Harbin, Heilongjiang, PR China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Hongbin</ForeName><Initials>H</Initials><AffiliationInfo><Affiliation>Department of Veterinary Surgery, Northeast Agricultural University, Harbin, Heilongjiang, PR China. Electronic address: hbwang1940@163.com.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2016</Year><Month>01</Month><Day>06</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Prev Vet Med</MedlineTA><NlmUniqueID>8217463</NlmUniqueID><ISSNLinking>0167-5877</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000818" MajorTopicYN="N">Animals</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002681" MajorTopicYN="N">China</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D002980" MajorTopicYN="N">Climate</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016000" MajorTopicYN="N">Cluster Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D004196" MajorTopicYN="N">Disease Outbreaks</DescriptorName><QualifierName UI="Q000662" MajorTopicYN="Y">veterinary</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D040362" MajorTopicYN="N">Geographic Information Systems</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010325" MajorTopicYN="N">Pasteurella</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D010326" MajorTopicYN="N">Pasteurella Infections</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="N">epidemiology</QualifierName><QualifierName UI="Q000382" MajorTopicYN="N">microbiology</QualifierName><QualifierName UI="Q000662" MajorTopicYN="Y">veterinary</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D015995" MajorTopicYN="N">Prevalence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013552" MajorTopicYN="N">Swine</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013553" MajorTopicYN="N">Swine Diseases</DescriptorName><QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName><QualifierName UI="Q000382" MajorTopicYN="N">microbiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D014887" MajorTopicYN="N">Weather</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Interclass Correlation Coefficient</Keyword><Keyword MajorTopicYN="N">Local Moran's index</Keyword><Keyword MajorTopicYN="N">Meteorological factor</Keyword><Keyword MajorTopicYN="N">Porcine pasteurellosis</Keyword><Keyword MajorTopicYN="N">Southcentral of Mainland China</Keyword><Keyword MajorTopicYN="N">Spatial analysis</Keyword><Keyword MajorTopicYN="N">Spearman rank correlation analysis</Keyword><Keyword MajorTopicYN="N">Zero-inflated Poisson</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2015</Year><Month>07</Month><Day>30</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2015</Year><Month>12</Month><Day>17</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2016</Year><Month>01</Month><Day>03</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2016</Year><Month>1</Month><Day>23</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2016</Year><Month>1</Month><Day>23</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2016</Year><Month>11</Month><Day>15</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">26796426</ArticleId><ArticleId IdType="pii">S0167-5877(16)30004-6</ArticleId><ArticleId IdType="doi">10.1016/j.prevetmed.2016.01.002</ArticleId></ArticleIdList></PubmedData></pubmed></record>Pour manipuler ce document sous Unix (Dilib)
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