Rapid age-grading and species identification of natural mosquitoes for malaria surveillance.
Nat Commun
; 13(1): 1501, 2022 03 21.
Article
em En
| MEDLINE
| ID: mdl-35314683
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Malária
/
Anopheles
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
/
Screening_studies
Limite:
Animals
/
Female
/
Humans
País como assunto:
Africa
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article