Pills with people inside them being poured out of a prescription bottle.

NSF grant will help fight the opioid epidemic across communities

How can machine learning help stop the opioid epidemic? It begins with gathering enough large-scale data to develop predictive treatment policies that can be supported across communities through a targeted approach.

That’s the aim of a nearly $149,000 National Science Foundation grant awarded to Virginia Commonwealth University researchers in the College of Engineering, the Wilder School of Government and Public Affairs, and the VCU School of Medicine Department of Family Medicine & Population Health. 

The multidisciplinary team’s goal is that better analytics will help leaders to make more effective decisions on how to best allocate limited resources to fight opioid addiction. 

Data collection will come from multiple sources, some quite unexpected. One unique method will be the deployment of robots in municipal sewage systems to test wastewater for traces of drug metabolites. Researchers will also mine online data to track the geographic concentration of drug-related keywords used on social media and internet searches. These methods will help serve as macro barometers to indicate overall opioid trends across communities.

“The team expects to develop a methodology to integrate these various data sources to identify geographic hotspots and high-risk zones, understand the characteristics of the target population, identify areas for deploying appropriate community-based services, and also understand the root causes of the increase, or decrease, in drug abuse,” said co-principal investigator Sarin Adhikari, Ph.D., an adjunct faculty researcher at the Wilder School of Government and Public Affairs.

By utilizing machine learning to derive predictive models to forecast opioid-related overdoses, researchers are hopeful that computerized decision-making tools will help leaders make more effective decisions on allocating resources to fight opioid addiction. 

“We hope to uncover new insights into opioid abuse in the Richmond region through this study,” Adhikari said.  “Identifying the socio-economic and demographic determinants of opioid abuse at the neighborhood-level could help local governments and nonprofits to reallocate resources, provide customized services and respond with appropriate policy interventions.”

Through innovative, population-level solutions, the team is optimistic that they’ll be able to make impacts that save lives and improve health for Virginians.

Fall 2022 / In this issue