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Rapid age-grading and species identification of natural mosquitoes for malaria surveillance
The malaria parasite, which is transmitted by several Anopheles mosquito species, requires more time to reach its human-transmissible stage than the average lifespan of mosquito vectors. Monitoring the species-specific age structure of mosquito populations is critical to evaluating the impact of vec...
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Published in: | Nature communications 2022-03, Vol.13 (1), p.1501-1501, Article 1501 |
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creator | Siria, Doreen J. Sanou, Roger Mitton, Joshua Mwanga, Emmanuel P. Niang, Abdoulaye Sare, Issiaka Johnson, Paul C. D. Foster, Geraldine M. Belem, Adrien M. G. Wynne, Klaas Murray-Smith, Roderick Ferguson, Heather M. González-Jiménez, Mario Babayan, Simon A. Diabaté, Abdoulaye Okumu, Fredros O. Baldini, Francesco |
description | The malaria parasite, which is transmitted by several
Anopheles
mosquito species, requires more time to reach its human-transmissible stage than the average lifespan of mosquito vectors. Monitoring the species-specific age structure of mosquito populations is critical to evaluating the impact of vector control interventions on malaria risk. We present a rapid, cost-effective surveillance method based on deep learning of mid-infrared spectra of mosquito cuticle that simultaneously identifies the species and age class of three main malaria vectors in natural populations. Using spectra from over 40, 000 ecologically and genetically diverse
An. gambiae
,
An. arabiensis
, and
An. coluzzii
females, we develop a deep transfer learning model that learns and predicts the age of new wild populations in Tanzania and Burkina Faso with minimal sampling effort. Additionally, the model is able to detect the impact of simulated control interventions on mosquito populations, measured as a shift in their age structures. In the future, we anticipate our method can be applied to other arthropod vector-borne diseases.
Knowing the age of malaria-transmitting mosquitoes is important to understand transmission risk as only old mosquitoes can transmit the disease. Here, the authors develop a method based on mid-infrared spectra of mosquito cuticle that can rapidly identify the species and age class of main malaria vectors. |
doi_str_mv | 10.1038/s41467-022-28980-8 |
format | article |
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Anopheles
mosquito species, requires more time to reach its human-transmissible stage than the average lifespan of mosquito vectors. Monitoring the species-specific age structure of mosquito populations is critical to evaluating the impact of vector control interventions on malaria risk. We present a rapid, cost-effective surveillance method based on deep learning of mid-infrared spectra of mosquito cuticle that simultaneously identifies the species and age class of three main malaria vectors in natural populations. Using spectra from over 40, 000 ecologically and genetically diverse
An. gambiae
,
An. arabiensis
, and
An. coluzzii
females, we develop a deep transfer learning model that learns and predicts the age of new wild populations in Tanzania and Burkina Faso with minimal sampling effort. Additionally, the model is able to detect the impact of simulated control interventions on mosquito populations, measured as a shift in their age structures. In the future, we anticipate our method can be applied to other arthropod vector-borne diseases.
Knowing the age of malaria-transmitting mosquitoes is important to understand transmission risk as only old mosquitoes can transmit the disease. Here, the authors develop a method based on mid-infrared spectra of mosquito cuticle that can rapidly identify the species and age class of main malaria vectors.</description><identifier>ISSN: 2041-1723</identifier><identifier>EISSN: 2041-1723</identifier><identifier>DOI: 10.1038/s41467-022-28980-8</identifier><identifier>PMID: 35314683</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>631/114/1305 ; 631/601/1466 ; 639/638/440/527/2257 ; Age ; Age composition ; Animals ; Anopheles - parasitology ; Burkina Faso - epidemiology ; Culicidae ; Cuticles ; Deep learning ; Disease transmission ; Epicuticle ; Female ; Health risks ; Humanities and Social Sciences ; Humans ; Infectious diseases ; Infrared spectra ; Life span ; Longevity ; Malaria ; Malaria - epidemiology ; Malaria - parasitology ; Malaria - prevention & control ; Mosquito Control - methods ; Mosquito Vectors - parasitology ; Mosquitoes ; multidisciplinary ; Natural populations ; Parasites ; Populations ; Risk management ; Science ; Science (multidisciplinary) ; Species ; Surveillance ; Transfer learning ; Vector-borne diseases ; Vectors</subject><ispartof>Nature communications, 2022-03, Vol.13 (1), p.1501-1501, Article 1501</ispartof><rights>The Author(s) 2022</rights><rights>2022. The Author(s).</rights><rights>The Author(s) 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c540t-43b0aa1b83ea598ce252786d0cd85b59d2d184e308017106ce71924a5634e2de3</citedby><cites>FETCH-LOGICAL-c540t-43b0aa1b83ea598ce252786d0cd85b59d2d184e308017106ce71924a5634e2de3</cites><orcidid>0000-0003-3196-3064 ; 0000-0003-1799-3830 ; 0000-0002-5904-4070 ; 0000-0003-4228-7962 ; 0000-0001-6663-7520 ; 0000-0002-9625-5176 ; 0000-0002-4949-1117 ; 0000-0002-9521-8234 ; 0000-0002-8853-0588 ; 0000-0002-9666-9395 ; 0000-0002-5305-5940</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2641601839/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2641601839?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35314683$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Siria, Doreen J.</creatorcontrib><creatorcontrib>Sanou, Roger</creatorcontrib><creatorcontrib>Mitton, Joshua</creatorcontrib><creatorcontrib>Mwanga, Emmanuel P.</creatorcontrib><creatorcontrib>Niang, Abdoulaye</creatorcontrib><creatorcontrib>Sare, Issiaka</creatorcontrib><creatorcontrib>Johnson, Paul C. D.</creatorcontrib><creatorcontrib>Foster, Geraldine M.</creatorcontrib><creatorcontrib>Belem, Adrien M. G.</creatorcontrib><creatorcontrib>Wynne, Klaas</creatorcontrib><creatorcontrib>Murray-Smith, Roderick</creatorcontrib><creatorcontrib>Ferguson, Heather M.</creatorcontrib><creatorcontrib>González-Jiménez, Mario</creatorcontrib><creatorcontrib>Babayan, Simon A.</creatorcontrib><creatorcontrib>Diabaté, Abdoulaye</creatorcontrib><creatorcontrib>Okumu, Fredros O.</creatorcontrib><creatorcontrib>Baldini, Francesco</creatorcontrib><title>Rapid age-grading and species identification of natural mosquitoes for malaria surveillance</title><title>Nature communications</title><addtitle>Nat Commun</addtitle><addtitle>Nat Commun</addtitle><description>The malaria parasite, which is transmitted by several
Anopheles
mosquito species, requires more time to reach its human-transmissible stage than the average lifespan of mosquito vectors. Monitoring the species-specific age structure of mosquito populations is critical to evaluating the impact of vector control interventions on malaria risk. We present a rapid, cost-effective surveillance method based on deep learning of mid-infrared spectra of mosquito cuticle that simultaneously identifies the species and age class of three main malaria vectors in natural populations. Using spectra from over 40, 000 ecologically and genetically diverse
An. gambiae
,
An. arabiensis
, and
An. coluzzii
females, we develop a deep transfer learning model that learns and predicts the age of new wild populations in Tanzania and Burkina Faso with minimal sampling effort. Additionally, the model is able to detect the impact of simulated control interventions on mosquito populations, measured as a shift in their age structures. In the future, we anticipate our method can be applied to other arthropod vector-borne diseases.
Knowing the age of malaria-transmitting mosquitoes is important to understand transmission risk as only old mosquitoes can transmit the disease. Here, the authors develop a method based on mid-infrared spectra of mosquito cuticle that can rapidly identify the species and age class of main malaria vectors.</description><subject>631/114/1305</subject><subject>631/601/1466</subject><subject>639/638/440/527/2257</subject><subject>Age</subject><subject>Age composition</subject><subject>Animals</subject><subject>Anopheles - parasitology</subject><subject>Burkina Faso - epidemiology</subject><subject>Culicidae</subject><subject>Cuticles</subject><subject>Deep learning</subject><subject>Disease transmission</subject><subject>Epicuticle</subject><subject>Female</subject><subject>Health risks</subject><subject>Humanities and Social Sciences</subject><subject>Humans</subject><subject>Infectious diseases</subject><subject>Infrared spectra</subject><subject>Life span</subject><subject>Longevity</subject><subject>Malaria</subject><subject>Malaria - epidemiology</subject><subject>Malaria - parasitology</subject><subject>Malaria - prevention & control</subject><subject>Mosquito Control - methods</subject><subject>Mosquito Vectors - parasitology</subject><subject>Mosquitoes</subject><subject>multidisciplinary</subject><subject>Natural populations</subject><subject>Parasites</subject><subject>Populations</subject><subject>Risk management</subject><subject>Science</subject><subject>Science (multidisciplinary)</subject><subject>Species</subject><subject>Surveillance</subject><subject>Transfer learning</subject><subject>Vector-borne diseases</subject><subject>Vectors</subject><issn>2041-1723</issn><issn>2041-1723</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNp9kk9rFjEQxoMottR-AQ-y4MXL1vzdzV4EKWoLBUH05CHMJrNrXnaTt8luod_e9N1aWw_mkpD5zTMzyUPIa0bPGBX6fZZMNm1NOa-57jSt9TNyzKlkNWu5eP7ofEROc97RskTHtJQvyZFQomRrcUx-foO9dxWMWI8JnA9jBcFVeY_WY668w7D4wVtYfAxVHKoAy5pgquaYr1e_xAINMVUzTJA8VHlNN-inCYLFV-TFAFPG0_v9hPz4_On7-UV99fXL5fnHq9oqSZdaip4CsF4LBNVpi1zxVjeOWqdVrzrHXWkbBdWUtYw2FlvWcQmqERK5Q3FCLjddF2Fn9snPkG5NBG8OFzGNBtLi7YSG28E1qOSguZJNqWl7RBSCWatdq4ai9WHT2q_9jM6W8cu0T0SfRoL_ZcZ4Y3QntFRtEXh3L5Di9Yp5MbPPFu9eBOOaDW8k040UTBT07T_oLq4plKc6UA1lWnSF4htlU8w54fDQDKPmzgpms4IpVjAHKxhdkt48HuMh5c_HF0BsQC6hMGL6W_s_sr8B7Ye_rA</recordid><startdate>20220321</startdate><enddate>20220321</enddate><creator>Siria, Doreen J.</creator><creator>Sanou, Roger</creator><creator>Mitton, Joshua</creator><creator>Mwanga, Emmanuel P.</creator><creator>Niang, Abdoulaye</creator><creator>Sare, Issiaka</creator><creator>Johnson, Paul C. 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D. ; Foster, Geraldine M. ; Belem, Adrien M. 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D.</au><au>Foster, Geraldine M.</au><au>Belem, Adrien M. G.</au><au>Wynne, Klaas</au><au>Murray-Smith, Roderick</au><au>Ferguson, Heather M.</au><au>González-Jiménez, Mario</au><au>Babayan, Simon A.</au><au>Diabaté, Abdoulaye</au><au>Okumu, Fredros O.</au><au>Baldini, Francesco</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Rapid age-grading and species identification of natural mosquitoes for malaria surveillance</atitle><jtitle>Nature communications</jtitle><stitle>Nat Commun</stitle><addtitle>Nat Commun</addtitle><date>2022-03-21</date><risdate>2022</risdate><volume>13</volume><issue>1</issue><spage>1501</spage><epage>1501</epage><pages>1501-1501</pages><artnum>1501</artnum><issn>2041-1723</issn><eissn>2041-1723</eissn><abstract>The malaria parasite, which is transmitted by several
Anopheles
mosquito species, requires more time to reach its human-transmissible stage than the average lifespan of mosquito vectors. Monitoring the species-specific age structure of mosquito populations is critical to evaluating the impact of vector control interventions on malaria risk. We present a rapid, cost-effective surveillance method based on deep learning of mid-infrared spectra of mosquito cuticle that simultaneously identifies the species and age class of three main malaria vectors in natural populations. Using spectra from over 40, 000 ecologically and genetically diverse
An. gambiae
,
An. arabiensis
, and
An. coluzzii
females, we develop a deep transfer learning model that learns and predicts the age of new wild populations in Tanzania and Burkina Faso with minimal sampling effort. Additionally, the model is able to detect the impact of simulated control interventions on mosquito populations, measured as a shift in their age structures. In the future, we anticipate our method can be applied to other arthropod vector-borne diseases.
Knowing the age of malaria-transmitting mosquitoes is important to understand transmission risk as only old mosquitoes can transmit the disease. Here, the authors develop a method based on mid-infrared spectra of mosquito cuticle that can rapidly identify the species and age class of main malaria vectors.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>35314683</pmid><doi>10.1038/s41467-022-28980-8</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0003-3196-3064</orcidid><orcidid>https://orcid.org/0000-0003-1799-3830</orcidid><orcidid>https://orcid.org/0000-0002-5904-4070</orcidid><orcidid>https://orcid.org/0000-0003-4228-7962</orcidid><orcidid>https://orcid.org/0000-0001-6663-7520</orcidid><orcidid>https://orcid.org/0000-0002-9625-5176</orcidid><orcidid>https://orcid.org/0000-0002-4949-1117</orcidid><orcidid>https://orcid.org/0000-0002-9521-8234</orcidid><orcidid>https://orcid.org/0000-0002-8853-0588</orcidid><orcidid>https://orcid.org/0000-0002-9666-9395</orcidid><orcidid>https://orcid.org/0000-0002-5305-5940</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 2041-1723 |
ispartof | Nature communications, 2022-03, Vol.13 (1), p.1501-1501, Article 1501 |
issn | 2041-1723 2041-1723 |
language | eng |
recordid | cdi_doaj_primary_oai_doaj_org_article_2cfd6e54f825461b8cbeee331cc8d75f |
source | Access via ProQuest (Open Access); Nature; PubMed Central; Springer Nature - nature.com Journals - Fully Open Access |
subjects | 631/114/1305 631/601/1466 639/638/440/527/2257 Age Age composition Animals Anopheles - parasitology Burkina Faso - epidemiology Culicidae Cuticles Deep learning Disease transmission Epicuticle Female Health risks Humanities and Social Sciences Humans Infectious diseases Infrared spectra Life span Longevity Malaria Malaria - epidemiology Malaria - parasitology Malaria - prevention & control Mosquito Control - methods Mosquito Vectors - parasitology Mosquitoes multidisciplinary Natural populations Parasites Populations Risk management Science Science (multidisciplinary) Species Surveillance Transfer learning Vector-borne diseases Vectors |
title | Rapid age-grading and species identification of natural mosquitoes for malaria surveillance |
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