Rivers and flooded areas identified by medium-resolution remote sensing improve risk prediction of the highly pathogenic avian influenza H5N1 in Thailand.
Identifieur interne : 000266 ( Main/Merge ); précédent : 000265; suivant : 000267Rivers and flooded areas identified by medium-resolution remote sensing improve risk prediction of the highly pathogenic avian influenza H5N1 in Thailand.
Auteurs : Weerapong Thanapongtharm ; Thomas P. Van Boeckel ; Chandrashekhar Biradar ; Xiang-Ming Xiao ; Marius GilbertSource :
- Geospatial health [ 1970-7096 ] ; 2013.
Descripteurs français
- Wicri :
- geographic : Thaïlande.
English descriptors
- KwdEn :
- Agriculture, Animals, Chickens (virology), Disease Outbreaks, Ducks (virology), Floods, Geographic Information Systems, Humans, Influenza A Virus, H5N1 Subtype, Influenza in Birds (epidemiology), Influenza in Birds (virology), Influenza, Human (epidemiology), Influenza, Human (virology), Models, Statistical, Population Density, Poultry Diseases (epidemiology), Poultry Diseases (virology), Risk Assessment, Risk Factors, Rivers, Satellite Imagery, Thailand (epidemiology).
- MESH :
- geographic , epidemiology : Thailand.
- epidemiology : Influenza in Birds, Influenza, Human, Poultry Diseases.
- virology : Chickens, Ducks, Influenza in Birds, Influenza, Human, Poultry Diseases.
- Agriculture, Animals, Disease Outbreaks, Floods, Geographic Information Systems, Humans, Influenza A Virus, H5N1 Subtype, Models, Statistical, Population Density, Risk Assessment, Risk Factors, Rivers, Satellite Imagery.
Abstract
Thailand experienced several epidemic waves of the highly pathogenic avian influenza (HPAI) H5N1 between 2004 and 2005. This study investigated the role of water in the landscape, which has not been previously assessed because of a lack of high-resolution information on the distribution of flooded land at the time of the epidemic. Nine Landsat 7 - Enhanced Thematic Mapper Plus scenes covering 174,610 km(2) were processed using k-means unsupervised classification to map the distribution of flooded areas as well as permanent lakes and reservoirs at the time of the main epidemic HPAI H5N1 wave of October 2004. These variables, together with other factors previously identified as significantly associated with risk, were entered into an autologistic regression model in order to quantify the gain in risk explanation over previously published models. We found that, in addition to other factors previously identified as associated with risk, the proportion of land covered by flooding along with expansion of rivers and streams, derived from an existing, sub-district level (administrative level no. 3) geographical information system database, was a highly significant risk factor in this 2004 HPAI epidemic. These results suggest that water-borne transmission could have partly contributed to the spread of HPAI H5N1 during the epidemic. Future work stemming from these results should involve studies where the actual distribution of small canals, rivers, ponds, rice paddy fields and farms are mapped and tested against farm-level data with respect to HPAI H5N1.
DOI: 10.4081/gh.2013.66
PubMed: 24258895
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Van Boeckel</name></author><author><name sortKey="Biradar, Chandrashekhar" sort="Biradar, Chandrashekhar" uniqKey="Biradar C" first="Chandrashekhar" last="Biradar">Chandrashekhar Biradar</name></author><author><name sortKey="Xiao, Xiang Ming" sort="Xiao, Xiang Ming" uniqKey="Xiao X" first="Xiang-Ming" last="Xiao">Xiang-Ming Xiao</name></author><author><name sortKey="Gilbert, Marius" sort="Gilbert, Marius" uniqKey="Gilbert M" first="Marius" last="Gilbert">Marius Gilbert</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2013">2013</date><idno type="RBID">pubmed:24258895</idno><idno type="pmid">24258895</idno><idno type="doi">10.4081/gh.2013.66</idno><idno type="wicri:Area/PubMed/Corpus">000157</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Corpus" wicri:corpus="PubMed">000157</idno><idno type="wicri:Area/PubMed/Curation">000157</idno><idno type="wicri:explorRef" wicri:stream="PubMed" wicri:step="Curation">000157</idno><idno type="wicri:Area/PubMed/Checkpoint">000157</idno><idno type="wicri:explorRef" wicri:stream="Checkpoint" wicri:step="PubMed">000157</idno><idno type="wicri:Area/Ncbi/Merge">000462</idno><idno type="wicri:Area/Ncbi/Curation">000462</idno><idno type="wicri:Area/Ncbi/Checkpoint">000462</idno><idno type="wicri:Area/Main/Merge">000266</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Rivers and flooded areas identified by medium-resolution remote sensing improve risk prediction of the highly pathogenic avian influenza H5N1 in Thailand.</title><author><name sortKey="Thanapongtharm, Weerapong" sort="Thanapongtharm, Weerapong" uniqKey="Thanapongtharm W" first="Weerapong" last="Thanapongtharm">Weerapong Thanapongtharm</name></author><author><name sortKey="Van Boeckel, Thomas P" sort="Van Boeckel, Thomas P" uniqKey="Van Boeckel T" first="Thomas P" last="Van Boeckel">Thomas P. Van Boeckel</name></author><author><name sortKey="Biradar, Chandrashekhar" sort="Biradar, Chandrashekhar" uniqKey="Biradar C" first="Chandrashekhar" last="Biradar">Chandrashekhar Biradar</name></author><author><name sortKey="Xiao, Xiang Ming" sort="Xiao, Xiang Ming" uniqKey="Xiao X" first="Xiang-Ming" last="Xiao">Xiang-Ming Xiao</name></author><author><name sortKey="Gilbert, Marius" sort="Gilbert, Marius" uniqKey="Gilbert M" first="Marius" last="Gilbert">Marius Gilbert</name></author></analytic><series><title level="j">Geospatial health</title><idno type="eISSN">1970-7096</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Agriculture</term><term>Animals</term><term>Chickens (virology)</term><term>Disease Outbreaks</term><term>Ducks (virology)</term><term>Floods</term><term>Geographic Information Systems</term><term>Humans</term><term>Influenza A Virus, H5N1 Subtype</term><term>Influenza in Birds (epidemiology)</term><term>Influenza in Birds (virology)</term><term>Influenza, Human (epidemiology)</term><term>Influenza, Human (virology)</term><term>Models, Statistical</term><term>Population Density</term><term>Poultry Diseases (epidemiology)</term><term>Poultry Diseases (virology)</term><term>Risk Assessment</term><term>Risk Factors</term><term>Rivers</term><term>Satellite Imagery</term><term>Thailand (epidemiology)</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Thailand</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Influenza in Birds</term><term>Influenza, Human</term><term>Poultry Diseases</term></keywords><keywords scheme="MESH" qualifier="virology" xml:lang="en"><term>Chickens</term><term>Ducks</term><term>Influenza in Birds</term><term>Influenza, Human</term><term>Poultry Diseases</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Agriculture</term><term>Animals</term><term>Disease Outbreaks</term><term>Floods</term><term>Geographic Information Systems</term><term>Humans</term><term>Influenza A Virus, H5N1 Subtype</term><term>Models, Statistical</term><term>Population Density</term><term>Risk Assessment</term><term>Risk Factors</term><term>Rivers</term><term>Satellite Imagery</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Thaïlande</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Thailand experienced several epidemic waves of the highly pathogenic avian influenza (HPAI) H5N1 between 2004 and 2005. This study investigated the role of water in the landscape, which has not been previously assessed because of a lack of high-resolution information on the distribution of flooded land at the time of the epidemic. Nine Landsat 7 - Enhanced Thematic Mapper Plus scenes covering 174,610 km(2) were processed using k-means unsupervised classification to map the distribution of flooded areas as well as permanent lakes and reservoirs at the time of the main epidemic HPAI H5N1 wave of October 2004. These variables, together with other factors previously identified as significantly associated with risk, were entered into an autologistic regression model in order to quantify the gain in risk explanation over previously published models. We found that, in addition to other factors previously identified as associated with risk, the proportion of land covered by flooding along with expansion of rivers and streams, derived from an existing, sub-district level (administrative level no. 3) geographical information system database, was a highly significant risk factor in this 2004 HPAI epidemic. These results suggest that water-borne transmission could have partly contributed to the spread of HPAI H5N1 during the epidemic. Future work stemming from these results should involve studies where the actual distribution of small canals, rivers, ponds, rice paddy fields and farms are mapped and tested against farm-level data with respect to HPAI H5N1.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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