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We develop a Spatio-TEMporal Mutually Exciting point process with Dynamicnetwork (STEMMED), i.e., a point process network wherein each node models aunique community-drug event stream with a dynamic mutually-exciting structure,accounting for influences from other nodes. We show that STEMMED can bedecomposed node-by-node, suggesting a tractable distributed learning procedure.Simulation shows that this learning algorithm can accurately recover knownparameters of STEMMED, especially for small networks and long data-horizons.Next, we turn this node-by-node decomposition into an online cooperativemulti-period forecasting framework, which is asymptotically robust tooperational errors, to facilitate Opioid-related overdose death (OOD) trendsforecasting among neighboring communities. In our numerical study, weparameterize STEMMED using individual-level OOD data and county-leveldemographics in Massachusetts. For any node, we observe that OODs within thesame drug class from nearby locations have the greatest influence on future OODtrends. Furthermore, the expected proportion of OODs triggered by historicalevents varies greatly across counties, ranging between 30%-70%. Finally, in apractical online forecasting setting, STEMMED-based cooperative frameworkreduces prediction error by 60% on average, compared to well-establishedforecasting models. Leveraging the growing abundance of public healthsurveillance data, STEMMED can provide accurate forecasts of local OOD trendsand highlight complex interactions between OODs across communities and drugtypes. Moreover, STEMMED enhances synergies between local and federalgovernment entities, which is critical to designing impactful policyinterventions.

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