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Opioid overdose is a growing public health crisis in the United States. Thiscrisis, recognized as "opioid epidemic," has widespread societal consequencesincluding the degradation of health, and the increase in crime rates and familyproblems. To improve the overdose surveillance and to identify the areas inneed of prevention effort, in this work, we focus on forecasting opioidoverdose using real-time crime dynamics. Previous work identified various typesof links between opioid use and criminal activities, such as financial motivesand common causes. Motivated by these observations, we propose a novelspatio-temporal predictive model for opioid overdose forecasting by leveragingthe spatio-temporal patterns of crime incidents. Our proposed modelincorporates multi-head attentional networks to learn different representationsubspaces of features. Such deep learning architecture, called"community-attentive" networks, allows the prediction of a given location to beoptimized by a mixture of groups (i.e., communities) of regions. In addition,our proposed model allows for interpreting what features, from whatcommunities, have more contributions to predicting local incidents as well ashow these communities are captured through forecasting. Our results on tworeal-world overdose datasets indicate that our model achieves superiorforecasting performance and provides meaningful interpretations in terms ofspatio-temporal relationships between the dynamics of crime and that of opioidoverdose.

CASTNet: Community-Attentive Spatio-Temporal Networks for Opioid Overdose Forecasting