Using Innovations in Data Analytics and Smart Technologies to Fight Opioid Overdose Crisis

Abstract

Drug overdose is now the leading cause of death for those under 50 in the United States. Inadequate data present a challenge for city officials, which prevents them from investigating the scale of the opioid overdose crisis. Various factors need to be considered in the prediction model for estimating the level of drug consumption, type of drug, and the location of the affected area. The aim of this project is to investigate several prediction and analysis models for forecasting drug use and overdoses by considering diverse data obtained from different sources, including sewage-based drug epidemiology, healthcare data, social networks data mining, and police data. Such analysis will help to formulate more effective policies and programs to combat fatal opioid overdoses.