Anopheles atroparvus Density Modeling using MODIS NDVI in a Former Malarious Area in Portugal
Malaria is dependent on environmental factors and considered as potentially re-emerging in temperate regions. Remote sensing data have been used successfully for monitoring environmental conditions that influence the patterns of such arthropod vector-borne diseases. Anopheles atroparvus density data...
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| Veröffentlicht in: | Journal of vector ecology 2011-12, Vol.36 (2), p.279-291 |
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| creator | Lourenço, Pedro M Sousa, Carla A Seixas, Júlia Lopes, Pedro Novo, Maria T Almeida, A. Paulo G |
| description | Malaria is dependent on environmental factors and considered as potentially re-emerging in temperate regions. Remote sensing data have been used successfully for monitoring environmental conditions that influence the patterns of such arthropod vector-borne diseases. Anopheles atroparvus density data were collected from 2002 to 2005, on a bimonthly basis, at three sites in a former malarial area in Southern Portugal. The development of the Remote Vector Model (RVM) was based upon two main variables: temperature and the Normalized Differential Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite. Temperature influences the mosquito life cycle and affects its intra-annual prevalence, and MODIS NDVI was used as a proxy for suitable habitat conditions. Mosquito data were used for calibration and validation of the model. For areas with high mosquito density, the model validation demonstrated a Pearson correlation of 0.68 (p |
| doi_str_mv | 10.1111/j.1948-7134.2011.00168.x |
| format | Article |
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Mosquito data were used for calibration and validation of the model. For areas with high mosquito density, the model validation demonstrated a Pearson correlation of 0.68 (p<0.05) and a modelling efficiency/Nash-Sutcliffe of 0.44 representing the models ability to predict intra- and inter-annual vector density trends. RVM estimates the density of the former malarial vector An. atroparvus as a function of temperature and of MODIS NDVI. RVM is a satellite data-based assimilation algorithm that uses temperature fields to predict the intra- and inter-annual densities of this mosquito species using MODIS NDVI. RVM is a relevant tool for vector density estimation, contributing to the risk assessment of transmission of mosquito-borne diseases and can be part of the early warning system and contingency plans providing support to the decision making process of relevant authorities.</description><identifier>ISSN: 1081-1710</identifier><identifier>EISSN: 1948-7134</identifier><identifier>DOI: 10.1111/j.1948-7134.2011.00168.x</identifier><identifier>PMID: 22129399</identifier><language>eng</language><publisher>Oxford, UK: Society for Vector Ecology</publisher><subject>Algorithms ; Animals ; Anopheles - physiology ; Anopheles atroparvus ; Arthropoda ; Environmental Monitoring - methods ; Insect Vectors ; Life Cycle Stages ; malaria ; Malaria - transmission ; Models, Theoretical ; NDVI ; Population Density ; Population Growth ; Portugal ; Remote Sensing Technology ; Spacecraft ; Temperature</subject><ispartof>Journal of vector ecology, 2011-12, Vol.36 (2), p.279-291</ispartof><rights>2011 The Society for Vector Ecology</rights><rights>2011 The Society for Vector Ecology.</rights><rights>Copyright Society for Vector Ecology Dec 2011</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b4748-9e71a9009e37aa7a87867738189711254c8994f649aa5a1a8ff24b4272e407573</citedby><cites>FETCH-LOGICAL-b4748-9e71a9009e37aa7a87867738189711254c8994f649aa5a1a8ff24b4272e407573</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://bioone.org/doi/pdf/10.1111/j.1948-7134.2011.00168.x$$EPDF$$P50$$Gbioone$$H</linktopdf><link.rule.ids>314,776,780,26957,27903,27904,52341</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22129399$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lourenço, Pedro M</creatorcontrib><creatorcontrib>Sousa, Carla A</creatorcontrib><creatorcontrib>Seixas, Júlia</creatorcontrib><creatorcontrib>Lopes, Pedro</creatorcontrib><creatorcontrib>Novo, Maria T</creatorcontrib><creatorcontrib>Almeida, A. Paulo G</creatorcontrib><title>Anopheles atroparvus Density Modeling using MODIS NDVI in a Former Malarious Area in Portugal</title><title>Journal of vector ecology</title><addtitle>J Vector Ecol</addtitle><description>Malaria is dependent on environmental factors and considered as potentially re-emerging in temperate regions. Remote sensing data have been used successfully for monitoring environmental conditions that influence the patterns of such arthropod vector-borne diseases. Anopheles atroparvus density data were collected from 2002 to 2005, on a bimonthly basis, at three sites in a former malarial area in Southern Portugal. The development of the Remote Vector Model (RVM) was based upon two main variables: temperature and the Normalized Differential Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite. Temperature influences the mosquito life cycle and affects its intra-annual prevalence, and MODIS NDVI was used as a proxy for suitable habitat conditions. Mosquito data were used for calibration and validation of the model. For areas with high mosquito density, the model validation demonstrated a Pearson correlation of 0.68 (p<0.05) and a modelling efficiency/Nash-Sutcliffe of 0.44 representing the models ability to predict intra- and inter-annual vector density trends. RVM estimates the density of the former malarial vector An. atroparvus as a function of temperature and of MODIS NDVI. RVM is a satellite data-based assimilation algorithm that uses temperature fields to predict the intra- and inter-annual densities of this mosquito species using MODIS NDVI. RVM is a relevant tool for vector density estimation, contributing to the risk assessment of transmission of mosquito-borne diseases and can be part of the early warning system and contingency plans providing support to the decision making process of relevant authorities.</description><subject>Algorithms</subject><subject>Animals</subject><subject>Anopheles - physiology</subject><subject>Anopheles atroparvus</subject><subject>Arthropoda</subject><subject>Environmental Monitoring - methods</subject><subject>Insect Vectors</subject><subject>Life Cycle Stages</subject><subject>malaria</subject><subject>Malaria - transmission</subject><subject>Models, Theoretical</subject><subject>NDVI</subject><subject>Population Density</subject><subject>Population Growth</subject><subject>Portugal</subject><subject>Remote Sensing Technology</subject><subject>Spacecraft</subject><subject>Temperature</subject><issn>1081-1710</issn><issn>1948-7134</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNkUtvEzEUha0K1PdfqCw2rDL4ehw_FiyipKVBDa0E7a6ybtI7ZaLJONgZaP49HlK6YEO9sK90v3N0rMMYB1FAPh-WBThlBwZKVUgBUAgB2hZPe-zwZfEmz8LCAAyIA3aU0lKI0iqr99mBlCBd6dwhux-1Yf2dGkocNzGsMf7sEp9Qm-rNls_CAzV1-8i71N-z68n0K_8yuZvyuuXIL0JcUeQzbDDWIetGkbBf3YS46R6xOWFvK2wSnT6_x-z24vzb-HJwdf1pOh5dDebK5LiODKATwlFpEA1aY7UxpQXrDIAcqoV1TlVaOcQhAtqqkmqupJGkhBma8pi93_muY_jRUdr4VZ0W1DTYUs7l3VBpDULa_5PCOGdB60y--4dchi62-RveyZxOSw0ZsjtoEUNKkSq_jvUK49aD8H1Vfun7RnzfiO-r8n-q8k9Zevbs381X9PAi_NtNBj7ugF91Q9tXG_vPd-fjPGW93unndQgtvT7Zb-1Orxo</recordid><startdate>201112</startdate><enddate>201112</enddate><creator>Lourenço, Pedro M</creator><creator>Sousa, Carla A</creator><creator>Seixas, Júlia</creator><creator>Lopes, Pedro</creator><creator>Novo, Maria T</creator><creator>Almeida, A. 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Paulo G</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Anopheles atroparvus Density Modeling using MODIS NDVI in a Former Malarious Area in Portugal</atitle><jtitle>Journal of vector ecology</jtitle><addtitle>J Vector Ecol</addtitle><date>2011-12</date><risdate>2011</risdate><volume>36</volume><issue>2</issue><spage>279</spage><epage>291</epage><pages>279-291</pages><issn>1081-1710</issn><eissn>1948-7134</eissn><abstract>Malaria is dependent on environmental factors and considered as potentially re-emerging in temperate regions. Remote sensing data have been used successfully for monitoring environmental conditions that influence the patterns of such arthropod vector-borne diseases. Anopheles atroparvus density data were collected from 2002 to 2005, on a bimonthly basis, at three sites in a former malarial area in Southern Portugal. The development of the Remote Vector Model (RVM) was based upon two main variables: temperature and the Normalized Differential Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra satellite. Temperature influences the mosquito life cycle and affects its intra-annual prevalence, and MODIS NDVI was used as a proxy for suitable habitat conditions. Mosquito data were used for calibration and validation of the model. For areas with high mosquito density, the model validation demonstrated a Pearson correlation of 0.68 (p<0.05) and a modelling efficiency/Nash-Sutcliffe of 0.44 representing the models ability to predict intra- and inter-annual vector density trends. RVM estimates the density of the former malarial vector An. atroparvus as a function of temperature and of MODIS NDVI. RVM is a satellite data-based assimilation algorithm that uses temperature fields to predict the intra- and inter-annual densities of this mosquito species using MODIS NDVI. RVM is a relevant tool for vector density estimation, contributing to the risk assessment of transmission of mosquito-borne diseases and can be part of the early warning system and contingency plans providing support to the decision making process of relevant authorities.</abstract><cop>Oxford, UK</cop><pub>Society for Vector Ecology</pub><pmid>22129399</pmid><doi>10.1111/j.1948-7134.2011.00168.x</doi><tpages>13</tpages></addata></record> |
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| ispartof | Journal of vector ecology, 2011-12, Vol.36 (2), p.279-291 |
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| subjects | Algorithms Animals Anopheles - physiology Anopheles atroparvus Arthropoda Environmental Monitoring - methods Insect Vectors Life Cycle Stages malaria Malaria - transmission Models, Theoretical NDVI Population Density Population Growth Portugal Remote Sensing Technology Spacecraft Temperature |
| title | Anopheles atroparvus Density Modeling using MODIS NDVI in a Former Malarious Area in Portugal |
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