Rapid age-grading and species identification of natural mosquitoes for malaria surveillance

• Published in: Nature communications Vol. 13; no. 1; pp. 1501 - 9
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 21.03.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/601/1466; 639/638/440/527/2257; Age; Age composition; Animals; Anopheles - parasitology; Aquatic insects; 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; Burkina Faso
• DOI: 10.1038/s41467-022-28980-8
• ISSN: 2041-1723

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.