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Vector control programmes are a strategic priority in the fight against malaria. However, vector control interventions require rigorous monitoring. Entomological tools for characterizing malaria transmission drivers are limited and are difficult to establish in the field. To predict  Anopheles  drivers of malaria transmission, such as mosquito age, blood feeding and  Plasmodium  infection, we evaluated artificial neural networks (ANNs) coupled to matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry (MS) and analysed the impact on the proteome of laboratory-reared  Anopheles stephensi  mosquitoes. ANNs were sensitive to  Anopheles  proteome changes and specifically recognized spectral patterns associated with mosquito age (0–10 days, 11–20 days and 21–28 days), blood feeding and  P. berghei  infection, with best prediction accuracies of 73%, 89% and 78%, respectively. This study illustrates that MALDI-TOF MS coupled to ANNs can be used to predict entomological drivers of malaria transmission, providing potential new tools for vector control. Future studies must assess the field validity of this new approach in wild-caught adult  Anopheles . A similar approach could be envisaged for the identification of blood meal source and the detection of insecticide resistance in  Anopheles  and to other arthropods and pathogens.

Prediction of malaria transmission drivers in Anopheles mosquitoes using artificial intelligence coupled to MALDI-TOF mass spectrometry