Tides Need STEMMED: A Locally Operating Spatiotemporal Mutually Exciting Point Process with Dynamic Network for Improving Opioid Overdose Death Prediction

• 成果类型: Article
• 署名作者: Liao, Che-Yi; Dong, Zheng; Garcia, Gian-Gabriel P.; Paynabar, Kamran; Xie, Yao; Jalali, Mohammad S.
• 署名单位: University System of Georgia; Georgia Institute of Technology; Amazon.com; University of Washington; University of Washington Seattle; Harvard University; Harvard Medical School; Harvard University Medical Affiliates; Massachusetts General Hospital; Massachusetts Institute of Technology (MIT)
• 刊物名称: M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1523-4614
• DOI: 10.1287/msom.2024.0946
• 发表日期: 2026
• 关键词: UNITED-STATES; hawkes processes; DRUG; patterns; Covid-19

摘要

Problem definition: Efforts to mitigate the U.S. opioid crisis have been complicated by ever-changing trends in opioid overdose deaths (OODs) across communities and drug types. Public health surveillance efforts are hampered by these challenges, making prediction of local OOD trends and coordination across communities critical. In this research, we design a model-based public health surveillance system capable of leveraging implicit connections between past and future OODs, thereby operating across locales and providing accurate local and global forecasts and unique insights of OOD trends. Methodology/results: We develop a spatiotemporal mutually exciting point process with dynamic network (STEMMED): a point process network wherein each node models a unique community-drug event stream with a dynamic mutually exciting structure, accounting for influences from other nodes. STEMMED can be decomposed node-by-node, suggesting that it can be tractably parameterized via distributed learning. Leveraging this decomposability, we outline an online cooperative forecasting procedure among local communities and characterize data-sharing approaches among local entities, including strategies based on drug types, geographical affiliations, and proximity. We then conduct a numerical study wherein we parameterize STEMMED using individual-level OOD data and city-level demographics in Massachusetts. In our off-line analysis, we identify a notable cluster formation process of OODs centered around Boston. Additionally, our analysis indicates a growing link between fentanyl and psychostimulants over time. Then, in the online model deployment setting, we find that STEMMED outperforms well-established forecasting models and that drug-based data-sharing policies across cities offer advantages over distance-based county-based and distance-based data-sharing policies. Further, a STEMMED-based OOD surveillance system achieves more than 40% improvement in detection delays relative to evidence-based surveillance systems that are currently hindered by considerable data lags. Managerial implications: STEMMED provides accurate forecasts of local OOD trends and highlights complex interactions between OODs across communities and drug types, informing the design and facilitating the timing of impactful policy interventions.