UGA research team receives USDA APHIS funding for development of a rapid diagnostics platform for SARS-CoV-2

A person with COVID-19 might transmit SARS-CoV-2 to domestic cats and dogs (and perhaps other pets) in the same way that an infected animal could possibly transmit it to another individual. In addition, lions, tigers, pumas, snow leopards and non-human primates from zoos or wildlife refuges in the U.S. and other countries have been confirmed infected with SARS-CoV-2, while infections have also been reported in white-tailed deer, both wild and farmed, in the U.S. and Canada. Infection and transmission routes among these animals are not yet well understood. It is clear that animal owners and handlers with or without symptoms of COVID-19 should continue to practice good hygiene while interacting with animals. For effective control and management of potential SARS-CoV-2 infection, a rapid, sensitive, low-cost and accurate diagnostics method for the detection of the virus in animals is highly desirable. 

A multi-disciplinary University of Georgia (UGA) research team led by Yiping Zhao has received a $750,000 cooperative-agreement from the USDA’s Animal and Plant Health Inspection Service, to develop a rapid, diagnostics platform for SARS-CoV-2 in animals using a handheld surface enhanced Raman spectroscopy device and deep learning algorithm.

"This achievement truly reflects the potential of merging innovative techniques in nanotechnology and artificial intelligence with impactful applications," said Zhao, Distinguished Research Professor in the Franklin College of Arts and Sciences department of physics and astronomy. "It also highlights the significance of forging interdisciplinary teams to cultivate a robust research environment at UGA."

This project is the result of an initial research project, "Nanotechnology and Machine Learning Based Rapid Infectious Disease Diagnostics" conducted under a UGA Presidential Seed. In that project, Zhao collaborated with Ralph Tripp, Georgia Research Alliance Chair of Animal Health Vaccine Development in the College of Veterinary Medicine; Xianyan Chen, associate director of Statistical Consulting Center in the Department of Statistics: and Hemant K. Naikare, professor in the College of Veterinary Medicine department of infectious diseases. 

"Our primary aim with the seed grant was to develop a diagnostic method utilizing nano-optics, specifically surface-enhanced Raman scattering (SERS), in conjunction with machine learning techniques," Zhao said. "During the course of the seed grant, we published seven papers, made efforts to facilitate collaboration among interdisciplinary teams to secure external grants, and submitted nine proposals as part of external team initiatives, with three of these external grants funded."

The goal of this research is to develop a rapid diagnostic strategy for detection of SARS-CoV-2 in animal specimens based on surface-enhanced Raman spectroscopy and deep-learning algorithms. The surface-enhanced Raman spectroscopy is a nanotechnology-based highly sensitive detection method that can tell the difference in the spectra collected from different viruses and animal samples, while the deep-learning algorithms can help researchers better differentiate viral signals in the spectra from a complex specimen such as deep nasal swabs, or others. 

“Dealing with high-dimensional spectral data can be quite challenging. Our research shows that deep learning algorithms are better than traditional statistical methods when it comes to accurately identifying viral signals in the spectra,” said Chen. 

In this project, the research team will target three specific aims: optimize SARS-CoV-2 detection in animal specimens such as swine and domestic cats; expand the detection strategy to other animal species such as canine, deer, poultry, equine, and other species; design a prototype point-of-care system and validate the device by blind tests. Eventually a handheld, highly sensitive and accurate SARS-CoV-2 diagnostic system will be developed.

Such a portable diagnostic technology can help to early detect and identify SARS-CoV-2 infected animals so that a rapid response to manage and control the infection in animals can be executed. The new tool will be designed to reduce the infection in farm animals and prevent any potential economic losses to the producers as well as prevent the transmission of the disease from animals to humans.

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