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

Che-Yi Liao (), Zheng Dong (), Gian-Gabriel P. Garcia (), Kamran Paynabar (), Yao Xie () and Mohammad S. Jalali ()
Additional contact information
Che-Yi Liao: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Zheng Dong: Amazon.com, Inc., Seattle, Washington 98109
Gian-Gabriel P. Garcia: Department of Industrial & Systems Engineering, University of Washington, Seattle, Washington 98195
Kamran Paynabar: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Yao Xie: H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Mohammad S. Jalali: MGH Institute for Technology Assessment, Harvard Medical School, Boston, Massachusetts 02114; and Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02142

Manufacturing & Service Operations Management, 2026, vol. 28, issue 2, 577-593

Abstract: 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.

Keywords: opioid overdose crisis; public health surveillance; point processes; local community cooperation; data-sharing policies (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:28:y:2026:i:2:p:577-593

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