NSF Award
2125430
Opioid overdose is now the leading cause of death for those under 50 in the USA. Cities have followed different strategies to address this problem through various education/training programs. However, the growing scale of the opioid overdose crisis in the USA indicates that more effective data-driven approaches are needed. Opioid abuse and overdose have been identified as a leading method of premature death in the Richmond Region, Virginia. Inadequate data is a major issue for city officials, which prevents them from investigating the scale of the opioid epidemic. Since locality’s economic competitiveness and their ability to recruit businesses and labor are dependent on the image they portray, the data sharing and further analytics in opioid overdoses have seen limited improvement. To this end, this planning project proposes to build a team of researchers and local stakeholders - including those in the leadership roles in all cities and counties in the Richmond Region. The team will work towards a data-driven understanding of the problem and community-involved solutions to address the issue. As responses to the opioid problem are common to other metro regions in the U.S., we hope to scale the model to be exercised in other regions. This project aims to harness the power of data analytics and smart technologies to develop creative solutions for efficient decision-making and planning to improve public health and living standards. Direct broader impacts include evidence-based factors that enhance and impair community responses to the opioid epidemic that can be discussed and refined with community members to drive change, and accordingly, reduce opioid overdose and health inequities across the region.<br/><br/>From the technical perspectives, this project will investigate novel data-driven approaches to treatment policies that can be supported by the community. The intellectual merit of this work includes: (1) developing a fundamental understanding of challenges facing communities due to opioid epidemics, (2) developing a better understanding of the relationship between governance, smart cities, and social innovation, particularly for addressing the opioid problem, (3) collecting relevant types of drug use data in the community, (4) deriving prediction models based on the available data from different sources, and (5) developing smart sensing solutions to accurately monitor and assess the state of drug abuse. Accordingly, this project aims to establish interdisciplinary efforts to develop a data-driven intervention in addressing the opioid overdose crisis, in coordination with community representatives to identify and assess applicable approaches. The team will investigate several predictive models for forecasting drug use/overdoses by considering diverse data on drug-related incidents. The project will also investigate Explainable Machine Learning techniques that will be coupled with developed data-driven models in order to provide estimations of drug use with an explanation of the important factors that justify the predictions made by the model. This will help identify the root causes and the extent of their impact.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
