Drug dissemination strategy with an SEIR-based SUC model

TL;DR

This paper develops an SEIR-based SUC model to predict drug addiction spread, identify drug origins, and analyze socio-economic factors, providing insights for targeted anti-drug strategies and emphasizing the importance of control measures.

Contribution

It introduces a novel SEIR-based SUC model for drug spread prediction and combines socio-economic data analysis with clustering to inform intervention strategies.

Findings

Drug addiction will fluctuate and eventually stabilize.

Identified key drug origins in specific US states.

Model effectiveness confirmed under certain parameter conditions.

Abstract

According to the features of drug addiction, this paper constructs an SEIR-based SUC model to describe and predict the spread of drug addiction. Predictions are that the number of drug addictions will continue to fluctuate with reduced amplitude and eventually stabilize. To seek the fountainhead of heroin, we identified the most likely origins of drugs in Philadelphia, PA, Cuyahoga and Hamilton, OH, Jefferson, KY, Kanawha, WV, and Bedford, VA. Based on the facts, advised concentration includes the spread of Oxycodone, Hydrocodone, Heroin, and Buprenorphine. In other words, drug transmission in the two states of Ohio and Pennsylvania require awareness. According to the propagation curve predicted by our model, the transfer of KY state is still in its early stage, while that of VA, WV is in the middle point, and OH, PA in its latter ones. As a result of this, the number of drug addictions in KY, OH, and VA is projected to increase in three years. For methodology, with the Principal component analysis technique, 22 variables in socio-economic data related to the continuous use of Opioid drugs was filtered, where the 'Relationship' Part deserves a highlight. Based on them, by using the K-means algorithm, 464 counties were categorized into three baskets. To combat the opioid crisis, a specific action will discuss in the sensitivity analysis section. After modeling and analytics, innovation is required to control addicts and advocate anti-drug news campaigns. This part also verified the effectiveness of model when $d_1<0.2; r_1,r_2,r_3<0.3; 15<\beta_1,\beta_2,\beta_3<25$. In other words, if such boundary exceeded, the number of drug addictions may rocket and peak in a short period.