A Dynamic Model of Opioid Epidemic based on Cellular Automata and Principal Component Analysis
Authors: 
Yufeng Wang, Lezhi Chen, Junyi He, Lingna Chen, Junxi Chen, Liangwei JiangAuthors Info & Claims
Yufeng Wang, Lezhi Chen, Junyi He, Lingna Chen, Junxi Chen, Liangwei JiangAuthors Info & ClaimsPages 126 - 129
Abstract
The United States is currently experiencing an opioid epidemic. We applied a dynamic model to predict the pathway of opioid transmission. We classify 69 drugs from the NFLIS data into 12 categories and display the relation of the latitude and longitude of each county and the number of Drug Reports. We construct the dynamic model based on cellular automata and principal component analysis. Moreover, we take the socio-economic indicators into consideration and perform sensitivity analysis. The simulation results show that for Ohio and Pennsylvania the sharp increase will appear in 2019. We can observe that the opioid crisis of five states in 2019 will be more severely located in the border area between Kentucky and Ohio, and in the northwest of West Virginia and West Virginia. This dynamic model will provide references for the further study of opioid epidemic.
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Index Terms
- A Dynamic Model of Opioid Epidemic based on Cellular Automata and Principal Component Analysis
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Published In
October 2019
141 pages
ISBN:9781450372510
DOI:10.1145/3369166
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In-Cooperation
- Beijing University of Technology
- Harbin Inst. Technol.: Harbin Institute of Technology
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 January 2020
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- Research-article
- Research
- Refereed limited
Funding Sources
- innovation training program of hunan province
- Hunan science foundation project
- Outstanding youth project of hunan education department
Conference
ICBBS 2019
ICBBS 2019: 2019 8th International Conference on Bioinformatics and Biomedical Science
October 23 - 25, 2019
Beijing, China
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