A New Analysis and Prediction Model Based on American Opioid Crisis
- DOI
- 10.2991/msbda-19.2019.20How to use a DOI?
- Keywords
- Opioid crisis, Linear interpolation, Time-varying prediction model, Factor analysis, Coping stratege
- Abstract
Based on the existing data, the diffusion model and time-varying prediction model of opioids are established by linear interpolation method, the transmission characteristics of synthetic opioids and non-synthetic opioids are analyzed, and the specific time and place of each state to start using specific opioids in different states is determined. Then, in order to judge the correlation between the trend of opioid use and the economic data of the United States census, 10 evaluation indexes were selected and empirical analysis was carried out by factor analysis. It was found that the factors of elderly, neonatal number, disability and population size were used to analyze opioid drugs in this region. Use has had a significant impact. In addition, the most widely used opioid groups have the characteristics of lack of health insurance, unemployment, low income and low level of education. Finally, according to these conclusions, this paper presents an effective strategy to combat opioid crisis, and tests the effectiveness of the model. It is found that the fitting degree of the model is about 78%, and the fitting effect of the model is good.
- Copyright
- © 2019, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Zongna Deng AU - Deyi Li AU - Yuanyuan Wang AU - Quan Lv AU - Ning Chen PY - 2019/08 DA - 2019/08 TI - A New Analysis and Prediction Model Based on American Opioid Crisis BT - Proceedings of the 2019 International Conference on Modeling, Simulation and Big Data Analysis (MSBDA 2019) PB - Atlantis Press SP - 124 EP - 132 SN - 2352-538X UR - https://doi.org/10.2991/msbda-19.2019.20 DO - 10.2991/msbda-19.2019.20 ID - Deng2019/08 ER -