Rapid age-grading and species identification of natural mosquitoes for malaria surveillance.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
21 03 2022
21 03 2022
Historique:
received:
09
06
2020
accepted:
19
02
2022
entrez:
22
3
2022
pubmed:
23
3
2022
medline:
13
4
2022
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 35314683
doi: 10.1038/s41467-022-28980-8
pii: 10.1038/s41467-022-28980-8
pmc: PMC8938457
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1501Subventions
Organisme : Medical Research Council
ID : MR/P025501/1
Pays : United Kingdom
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Wellcome Trust
ID : WT102350/Z/13
Pays : United Kingdom
Informations de copyright
© 2022. The Author(s).
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