Using Internet Search Trends to Forecast Short-term Drug Overdose Deaths: A Case Study on Connecticut - Mukherjee, Sumit; Becker, Nicholas; Weeks, Bill; Lavista Ferres, Juan M. (IEEE International Conference on Machine Learning and Applications (ICMLA), 2020)
In the United States, the opioid epidemic is a serious public health crisis which claimed over 130 lives per day in 2018, according to the CDC. While there are many efforts to design effective interventions to prevent drug related deaths, much of them are focused around better prescribing practices. A promising line of inquiry has focused on utilizing machine learning tools to predict addiction or overdose related hospital admission using prior health record information. However, these are strongly reliant on the private health record information of individuals. Here, we propose using publicly available historic death records along with publicly available internet search trends of drug related search terms to predict the number of overdose deaths in the upcoming week. Our model is able to predict both the number of, and spikes in drug overdose deaths with good accuracy compared to several baselines, demonstrating the utility of search data in forecasting overdose deaths. While we demonstrate this approach as a case study in the State of Connecticut, which collects and publishes overdose data, our findings could encourage other state governments to similarly invest in collection, publication and analysis of such data.