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

arXiv: https://arxiv.org/abs/1905.04714 · HTML: https://mlsanity.com/paper/1905.04714/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2019.

## Built on by (most cited first, 3 of 3)

- [Policy4OOD: A Knowledge-Guided World Model for Policy Intervention Simulation against the Opioid Overdose Crisis](https://mlsanity.com/paper/2602.12373/) (2026)
- [Decision-aware training of spatiotemporal forecasting models to select a top K subset of sites for intervention](https://mlsanity.com/paper/2503.05622/) (2025)
- [HungerGist: An Interpretable Predictive Model for Food Insecurity](https://mlsanity.com/paper/2311.10953/) (2023)

## Abstract (arXiv)

Opioid overdose is a growing public health crisis in the United States. This crisis, recognized as "opioid epidemic," has widespread societal consequences including the degradation of health, and the increase in crime rates and family problems. To improve the overdose surveillance and to identify the areas in need of prevention effort, in this work, we focus on forecasting opioid overdose using real-time crime dynamics. Previous work identified various types of links between opioid use and criminal activities, such as financial motives and common causes. Motivated by these observations, we propose a novel spatio-temporal predictive model for opioid overdose forecasting by leveraging the spatio-temporal patterns of crime incidents. Our proposed model incorporates multi-head attentional networks to learn different representation subspaces of features. Such deep learning architecture, called "community-attentive" networks, allows the prediction of a given location to be optimized by a mixture of groups (i.e., communities) of regions. In addition, our proposed model allows for interpreting what features, from what communities, have more contributions to predicting local incidents as well as how these communities are captured through forecasting. Our results on two real-world overdose datasets indicate that our model achieves superior forecasting performance and provides meaningful interpretations in terms of spatio-temporal relationships between the dynamics of crime and that of opioid overdose.