# Modeling the Co-evolution of Substance Use behavior and peer Networks of risk/support (CoSUN)

> **NIH NIH P20** · UNIVERSITY OF NEBRASKA LINCOLN · 2022 · $249,082

## Abstract

Drug overdose deaths in the U.S. have continued to increase over the years, with over 70,000 deaths 
in 2019. The economic cost of substance use and abuse related crimes, healthcare, and loss in work 
productivity in the U.S. exceeds $600 billion each year. In order to aid individuals and reduce the 
associated cost effectively, we must allocate appropriate resources to individuals in the greatest 
emerging need (e.g., those of high future substance use). However, there are currently no data-driven 
tools that allow stakeholders to (stochastically) forecast an individual's substance use. Current 
methodologies only rely on trend analyses, establishing correlations between risk factors (e.g., friends 
that use drugs) and substance use. A key challenge we are facing when modelling future substance use 
lies in the co-evolution of behaviors (i.e., drug use) and peer networks of risk/support, which can 
change over time and may depend on each other. To address this scientific obstacle, we consider an 
innovative approach that decouples the co-evolution process of substance use and peer risk/support 
networks by (Aim 1A) first modelling how individual attributes (e.g., drug use and adverse childhood 
history) along with their peer networks (e.g., the extent of peer and confidant drug use) impact the 
individual’s substance use behavior and (Aim 1B) then modeling how individuals’ peer risk/support 
network links form or break in response to similarities or differences in the endpoints’ attributes (e.g., 
their drug use behaviors). Thus, for the first time, this project seeks to develop stochastic forecasting 
models for future substance use (FSU) and future peer risk/support networks (FPN) at long timescales 
within months (Aims 1-2) and for FSU at short timescales within days (Aim 3) using data on covariates 
of individual attributes and peer network features. We will use successful machine-learning methods to 
build these models and rigorously assess model generalizability/prediction performance (Aims 1-3) by 
making use of data that is held-out from the model building process. The Aims will provide a foundation 
for a future innovative NIH R01 that develops stochastic simulations of realistic SUD-related behavioral 
contagion in plausible dynamic networks, to inform resource allocation and contingency planning. This 
project also lays the groundwork for just-in-time interventions to detect imminent increases in FSU and 
use these risk forecasts to trigger the delivery of social and behavioral interventions.

## Key facts

- **NIH application ID:** 10557937
- **Project number:** 5P20GM130461-04
- **Recipient organization:** UNIVERSITY OF NEBRASKA LINCOLN
- **Principal Investigator:** Hau Chan
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $249,082
- **Award type:** 5
- **Project period:** 2022-03-01 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10557937

## Citation

> US National Institutes of Health, RePORTER application 10557937, Modeling the Co-evolution of Substance Use behavior and peer Networks of risk/support (CoSUN) (5P20GM130461-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10557937. Licensed CC0.

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