Computational analysis of drug addiction epidemiology by integrating molecular mapping and social media signals

Abstract

Drug abuse is amongst the most significant factors impacting the health of Americans. The dynamic nature of this problem is characterized by a number of issues including the continual penetration of novel chemical entities into the abuse-dependency cycle, recognition of dependency elicited by entities, such as opioids, that were hitherto considered to be harmless, the multistage nature of the addiction process in an individual, and finally the spread to ever-different sections of the populace. The interplay of these factors makes early identification of emerging substance use trends, studying the epidemiology, and designing effective interventions especially complex. This research seeks to ameliorate this complexity by integrating two methodological directions: molecular maps that help contextualize the chemical etiology of addiction and creation of dynamic models of addiction through extraction, modeling and analysis of human-factors related information from a relatively new source, namely, social media.