AIMC Topic: Mosquito Vectors

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Double vision: 2D and 3D mosquito trajectories can be as valuable for behaviour analysis via machine learning.

Parasites & vectors
BACKGROUND: Mosquitoes are carriers of tropical diseases, thus demanding a comprehensive understanding of their behaviour to devise effective disease control strategies. In this article we show that machine learning can provide a performance assessme...

Reagent-free detection of Plasmodium falciparum malaria infections in field-collected mosquitoes using mid-infrared spectroscopy and machine learning.

Scientific reports
Field-derived metrics are critical for effective control of malaria, particularly in sub-Saharan Africa where the disease kills over half a million people yearly. One key metric is entomological inoculation rate, a direct measure of transmission inte...

Predicting the age of field mosquitoes using mass spectrometry and deep learning.

Science advances
Mosquito-borne diseases like malaria are rising globally, and improved mosquito vector surveillance is needed. Survival of mosquitoes is key for epidemiological monitoring of malaria transmission and evaluation of vector control strategies targeting...

Using UAV images and deep learning in investigating potential breeding sites of Aedes albopictus.

Acta tropica
Aedes albopictus (Diptera: Culicidae) plays a crucial role as a vector for mosquito-borne diseases like dengue and zika. Given the limited availability of effective vaccines, the prevention of Aedes-borne diseases mainly relies on extensive efforts i...

Application of computer vision and deep learning models to automatically classify medically important mosquitoes in North Borneo, Malaysia.

Bulletin of entomological research
Mosquito-borne diseases have emerged in North Borneo in Malaysia due to rapid changes in the forest landscape, and mosquito surveillance is key to understanding disease transmission. However, surveillance programmes involving sampling and taxonomic i...

Utilizing a novel high-resolution malaria dataset for climate-informed predictions with a deep learning transformer model.

Scientific reports
Climatic factors influence malaria transmission via the effect on the Anopheles vector and Plasmodium parasite. Modelling and understanding the complex effects that climate has on malaria incidence can enable important early warning capabilities. Dee...

Integrating artificial intelligence and wing geometric morphometry to automate mosquito classification.

Acta tropica
Mosquitoes (Diptera: Culicidae) comprise over 3500 global species, primarily in tropical regions, where the females act as disease vectors. Thus, identifying medically significant species is vital. In this context, Wing Geometric Morphometry (WGM) em...

Deep learning and wing interferential patterns identify Anopheles species and discriminate amongst Gambiae complex species.

Scientific reports
We present a new and innovative identification method based on deep learning of the wing interferential patterns carried by mosquitoes of the Anopheles genus to classify and assign 20 Anopheles species, including 13 malaria vectors. We provide additi...

Artificial intelligence (AI): a new window to revamp the vector-borne disease control.

Parasitology research
Artificial intelligence (AI) facilitates scientists to devise intelligent machines that work and behave like humans to resolve difficulties and problems by utilizing minimal resources. The Healthcare sector has benefited due to this. Mosquito-transmi...

AI-Enabled Mosquito Surveillance and Population Mapping Using Dragonfly Robot.

Sensors (Basel, Switzerland)
Mosquito-borne diseases can pose serious risks to human health. Therefore, mosquito surveillance and control programs are essential for the wellbeing of the community. Further, human-assisted mosquito surveillance and population mapping methods are t...