A new UNLV-led study highlights the use of AI in wastewater surveillance to identify emerging viruses faster than ever, paving the way for proactive public health interventions.
A team of researchers led by the University of Nevada, Las Vegas (UNLV) has made significant strides in early virus detection by integrating artificial intelligence (AI) with wastewater surveillance. This innovative approach, published in the journal Nature Communications, could revolutionize public health responses to emerging virus outbreaks.
During the COVID-19 pandemic, wastewater surveillance emerged as a pivotal method for tracking virus mutations and spread patterns. However, this new study promises to enhance detection even further, potentially identifying new virus variants before patients exhibit symptoms.
Lead author Xiaowei Zhuang, a neuroscience graduate student at UNLV, developed an AI algorithm capable of analyzing wastewater samples to detect various pathogens, including influenza, RSV, mpox, measles, gonorrhea and Candida auris.
“Imagine identifying the next outbreak even before the first patient enters a clinic. This research shows how we can make this possible,” co-author Edwin Oh, a professor with the Nevada Institute of Personalized Medicine at UNLV, said in a news release.
By leveraging AI, the research team can map virus emergence, mutation and transmission faster than traditional methods. This could significantly improve public health officials’ ability to enact rapid, targeted interventions.
“Through the use of AI we can determine how a pathogen is evolving without even testing a single human being,” Oh added.
The research team, which includes collaborators from Desert Research Institute and Southern Nevada Water Authority (SNWA), validated their findings by analyzing around 3,700 wastewater samples collected in Southern Nevada between 2021 and 2023.
The AI system demonstrated an impressive ability to identify virus variants with just two to five samples, far earlier than existing technologies.
Traditionally, wastewater detection methods required prior knowledge of a variant’s genetic makeup and leaned heavily on clinical data, often resulting in reactive rather than proactive public health responses.
“Wastewater surveillance has enabled more timely and proactive public health responses through monitoring disease emergence and spread at a population level in real time,” added Zhuang.
The new AI method enhances early outbreak detection, allowing for the identification of novel threats without prior knowledge or patient testing data, Zhuang explained.
The study underscores the potential for AI-enhanced wastewater surveillance, particularly in improving disease monitoring in rural and low-resource settings.
“The tool could especially be useful in improving disease surveillance in rural communities, empowering health workers in low-resource settings,” added co-author Duane Moser, a research professor at Desert Research Institute.
Since 2021, a coalition comprising UNLV, SNWA, the Southern Nevada Health District and Desert Research Institute has used a public wastewater surveillance dashboard to track COVID-19 and other viruses’ spread. This effort has led to over 30 studies, with the latest one marking a pioneering application of AI in wastewater intelligence.
“Wastewater surveillance has proven to be an effective tool for filling critical data gaps and understanding public health conditions within a community,” added co-author Daniel Gerrity, the principal research microbiologist at SNWA. “The ongoing wastewater surveillance effort is a great example of how collaboration between SNWA, UNLV and other partners can lead to positive impacts for the local community and beyond.”
Source: University of Nevada, Las Vegas