• Press release

Malaria: a new method for identifying the age of mosquito vectors using AI and mass spectrometry

Malaria is a disease transmitted only by the oldest mosquitoes. An innovative method, combining artificial intelligence with protein analysis in malaria mosquito vectors, has made it possible to estimate their age with an accuracy of 2 days. This method, developed by Cécile Nabet, an associate professor and researcher, Noshine Mohammad, a PhD in Biostatistics and Biomathematics and their collaborators at the Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLesp)  and the Medical Informatics and Knowledge Engineering Laboratory for e-health (LIMICS)  opens up new perspectives in the fight against malaria and the understanding of vector biology. This innovative method is the subject of an article published in Science Advances.

Mosquito-borne diseases are on the rise worldwide, and climate change could exacerbate the situation. Malaria remains a threat, with over 200 million cases and almost half a million deaths every year. Vector control is therefore crucial, and knowing the age of the mosquitoes is of particular importance, as only mosquitoes that survive long enough (more than 10 days) can transmit malaria. Yet accurately estimating their age in the wild remains a major challenge; available methods (such as ovary dissection or mosquito tagging) are imprecise, require considerable expertise and are inapplicable on a large scale. What's more, growing resistance to insecticides, once effective in reducing mosquito survival, makes the need to develop effective monitoring and control methods even more urgent.

With this in mind, Cécile Nabet, Noshine Mohammad and their collaborators at the iPLesp and the LIMICS have developed an innovative method for estimating the age of mosquitoes. This approach combines mass spectrometry  with artificial intelligence to detect variations in the protein profiles of mosquitoes as a function of their age. In collaboration with the Vectorial and Parasitic Ecology Laboratory at Cheikh Anta-Diop University in Dakar, Senegal, this method was developed using Anopheles mosquitoes (malaria vectors) collected at the larval stage in various urban and rural environments in Senegal, then reared in an insectarium to determine their age.

The performance of the algorithm (test dataset) enabled mosquito age to be estimated with remarkable accuracy, to within 2 days, over the entire lifespan of the mosquito. Simulations confirm that this method of age estimation could reveal changes in the age structure of mosquito populations following effective interventions such as insecticide-treated bed nets.

"This new method could be used even in resource-limited regions, as it is fast, inexpensive and easy to implement in the field. It could play a crucial role in malaria surveillance and in assessing the effectiveness of vector control interventions in vulnerable regions, particularly when testing new strategies", explains Dr Cécile Nabet.  

This method, currently applicable to Anopheles mosquitoes, could also be extended to other mosquito vectors, such as Aedes, opening up the fight against diseases such as Zika, Chikungunya and Dengue.

This research was supported by the Ile-de-France Region and Cerba Healthcare,
as part of the DIM One Health project.

  1 Co-supervised by Sorbonne University, AP-HP and Inserm
  2 Co-supervised by Sorbonne University, AP-HP and Inserm

3Technique de séparation des protéines selon leur masse couramment utilisée en microbiologie

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