  According to the Center of Disease Control (CDC), the Opioid epidemic has
claimed more than 72,000 lives in the US in 2017 alone. In spite of various
efforts at the local, state and federal level, the impact of the epidemic is
becoming progressively worse, as evidenced by the fact that the number of
Opioid related deaths increased by 12.5\% between 2016 and 2017. Predictive
analytics can play an important role in combating the epidemic by providing
decision making tools to stakeholders at multiple levels - from health care
professionals to policy makers to first responders. Generating Opioid incidence
heat maps from past data, aid these stakeholders to visualize the profound
impact of the Opioid epidemic. Such post-fact creation of the heat map provides
only retrospective information, and as a result, may not be as useful for
preventive action in the current or future time-frames. In this paper, we
present a novel deep neural architecture, which learns subtle spatio-temporal
variations in Opioid incidences data and accurately predicts future heat maps.
We evaluated the efficacy of our model on two open source datasets- (i) The
Cincinnati Heroin Overdose dataset, and (ii) Connecticut Drug Related Death
Dataset.
