Modeling the Dynamics of Drug Spreading in China

Haoxiang Tang, Mingtao Li, Xiangyu Yan, Zuhong Lu and Zhongwei Jia
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Haoxiang Tang: Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China
Mingtao Li: School of Mathematics, Taiyuan University of Technology, Taiyuan 030024, China
Xiangyu Yan: School of Public Health, Peking University, Beijing 100191, China
Zuhong Lu: State Key Lab of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
Zhongwei Jia: School of Public Health, Peking University, Beijing 100191, China

IJERPH, 2021, vol. 18, issue 1, 1-25

Abstract: Drug abuse remains one of the major public health issues at the global level. In this article, we propose a drug epidemic model with a complete addiction–rehabilitation–recovery process, which allows the initiation of new users under the influence of drug addicts undergoing treatment and hidden drug addicts. We first conduct qualitative analyses of the dynamical behaviors of the model, including the existence and positivity of the solutions, the basic reproduction number, global asymptotic stabilities of both the drug-free and the drug-persistent equilibria, as well as sensitivity analysis. Then we use the model to predict the drug epidemic in China during 2020–2030. Finally, we numerically simulate the potential impact of intervention strategies on different drug users. The results show that the drug epidemic will decrease significantly during 2020−2030, and the most effective intervention strategy to eliminate drug epidemics is to strengthen the investigation and rehabilitation admission of hidden drug users.

Keywords: drug epidemic model; basic reproduction number; stability; sensitivity; China; numerical simulation (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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