AIMC Topic: Insect Vectors

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Phytophagous, blood-suckers or predators? Automated identification of Chagas disease vectors and similar bugs using convolutional neural network algorithms.

Acta tropica Apr 26, 2025
Correct identification of blood-sucking bugs, such as triatomines, is important because they are vectors of Chagas' disease. Identifying these insects is often difficult for non-specialists. Deep learning is emerging as a solution for automated ident...
Neural Networks, Computer Algorithms Feeding Behavior Chagas Disease Insect Vectors Triatominae Animals Deep Learning Convolutional Neural Networks
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Using deep learning artificial intelligence for sex identification and taxonomy of sand fly species.

PloS one Apr 3, 2025
Sandflies are vectors for several tropical diseases such as leishmaniasis, bartonellosis, and sandfly fever. Moreover, sandflies exhibit species-specificity in transmitting particular pathogen species, with females being responsible for disease trans...
Phlebotomus Psychodidae Deep Learning Species Specificity Insect Vectors Animals Male Neural Networks, Computer Female
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Automated identification of Chagas disease vectors using AlexNet pre-trained convolutional neural networks.

Medical and veterinary entomology Dec 13, 2024
The 158 bug species that make up the subfamily Triatominae are the potential vectors of Trypanosoma cruzi, the etiological agent of Chagas disease. Despite recent progress in developing a picture-based automated system for identification of triatomin...
Insect Vectors Triatominae Rhodnius Neural Networks, Computer Convolutional Neural Networks Animals Trypanosoma cruzi Deep Learning Chagas Disease
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Modelling bluetongue and African horse sickness vector (Culicoides spp.) distribution in the Western Cape in South Africa using random forest machine learning.

Parasites & vectors Aug 21, 2024
BACKGROUND: Culicoides biting midges exhibit a global spatial distribution and are the main vectors of several viruses of veterinary importance, including bluetongue (BT) and African horse sickness (AHS). Many environmental and anthropological factor...
Cattle South Africa Random Forest Sheep Animals Ecosystem Disease Outbreaks Ceratopogonidae African Horse Sickness Bluetongue Bluetongue virus Horses Climate Insect Vectors Machine Learning
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Machine-learning model led design to experimentally test species thermal limits: The case of kissing bugs (Triatominae).

PLoS neglected tropical diseases Mar 8, 2021
Species Distribution Modelling (SDM) determines habitat suitability of a species across geographic areas using macro-climatic variables; however, micro-habitats can buffer or exacerbate the influence of macro-climatic variables, requiring links betwe...
Chagas Disease Insect Vectors Microclimate Rhodnius Triatominae Panstrongylus Animals Models, Biological Trypanosoma cruzi Machine Learning
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Modelling the monthly abundance of Culicoides biting midges in nine European countries using Random Forests machine learning.

Parasites & vectors Apr 15, 2020
BACKGROUND: Culicoides biting midges transmit viruses resulting in disease in ruminants and equids such as bluetongue, Schmallenberg disease and African horse sickness. In the past decades, these diseases have led to important economic losses for far...
Machine Learning Animals Ecosystem Europe Population Dynamics Farms Models, Theoretical Ceratopogonidae Seasons Climate Insect Vectors
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An AI-based approach in determining the effect of meteorological factors on incidence of malaria.

Frontiers in bioscience (Landmark edition) Mar 1, 2020
This study presents the classification of malaria-prone zones based on (a) meteorological factors, (b) demographics and (c) patient information. Observations are performed on extended features in dataset over the spiking and non-spiking classifiers i...
Insect Vectors Meteorological Concepts Reproducibility of Results Animals Incidence Support Vector Machine Humans India Neural Networks, Computer Malaria Models, Theoretical Algorithms
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Machine learning approaches in GIS-based ecological modeling of the sand fly Phlebotomus papatasi, a vector of zoonotic cutaneous leishmaniasis in Golestan province, Iran.

Acta tropica Sep 7, 2018
The distribution and abundance of Phlebotomus papatasi, the primary vector of zoonotic cutaneous leishmaniasis in most semi-/arid countries, is a major public health challenge. This study compares several approaches to model the spatial distribution ...
Machine Learning Environment Ecosystem Animals Leishmaniasis, Cutaneous Insect Vectors Geographic Information Systems Area Under Curve Phlebotomus Iran
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Machine Learning for Characterization of Insect Vector Feeding.

PLoS computational biology Nov 10, 2016
Insects that feed by ingesting plant and animal fluids cause devastating damage to humans, livestock, and agriculture worldwide, primarily by transmitting pathogens of plants and animals. The feeding processes required for successful pathogen transmi...
Models, Biological Pattern Recognition, Automated Feeding Behavior Insect Vectors Citrus Machine Learning Plethysmography, Impedance Computer Simulation Sucking Behavior Animals
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Identifying targets of selection in mosaic genomes with machine learning: applications in Anopheles gambiae for detecting sites within locally adapted chromosomal inversions.

Molecular ecology Apr 20, 2016
Chromosomal inversions are important structural changes that may facilitate divergent selection when they capture co-adaptive loci in the face of gene flow. However, identifying selection targets within inversions can be challenging. The high degrees...
Selection, Genetic Cameroon Adaptation, Physiological Machine Learning Genome, Insect Polymorphism, Single Nucleotide Anopheles Insect Vectors Recombination, Genetic Animals Chromosome Inversion Models, Genetic Ecosystem
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