Bees can be trained to identify SARS-CoV-2 infected samples

Area of Science:

• Veterinary Medicine
• Animal Health
• Infectious Disease Diagnostics

Background:

• The COVID-19 pandemic highlighted the need for rapid, reliable diagnostics for zoonotic viral diseases in both humans and animals.
• Pathologies alter an animal's volatile organic compound (VOC) profile, offering a potential target for non-invasive diagnostic tests.

Purpose of the Study:

• To train honeybees (Apis mellifera) to detect SARS-CoV-2 infected minks (Neovison vison) using Pavlovian conditioning.
• To evaluate different training protocols for optimal learning rate, accuracy, and memory retention in honeybees for disease detection.
• To assess the potential of a honeybee-based diagnostic system as a rapid, low-input addition to existing SARS-CoV-2 testing methods.

Main Methods:

• Honeybees were trained using Pavlovian conditioning protocols to identify SARS-CoV-2 infected mink odors.
• Two distinct training protocols were tested to compare learning rates, accuracy, and memory retention.
• A non-invasive rapid test was designed, allowing parallel testing of multiple bees on the same samples.
• Diagnostic efficacy was simulated using training data, calculating sensitivity and specificity.

Main Results:

• Honeybees were successfully trained to specifically respond to odors from SARS-CoV-2 infected minks.
• Simulated diagnostic evaluation predicted a sensitivity of 92% and a specificity of 86% for the honeybee-based test.
• The non-invasive, parallel testing approach provided reliable results regarding the subjects' health status.

Conclusions:

• Honeybee-based diagnostics offer a reliable, rapid, and low-input method for detecting SARS-CoV-2 in minks.
• This approach can be integrated into wider diagnostic systems for managing zoonotic viral diseases.
• Honeybee diagnostics are particularly promising for remote or resource-limited communities lacking advanced testing infrastructure.