Sean SK Yap, Wei Jun Choy, Rick YH Tan and Maurice HT Ling*

School of Applied Science, Temasek Polytechnic, Singapore

*Corresponding Author: Maurice HT Ling, School of Applied Science, Temasek Polytechnic, Singapore.

Received: December 05, 2023; Published: December 21, 2023

Abstract

Substance use/abuse is a public health concern with a long history and mathematical modelling is an important tool to study its epidemiology. Recently, a study showed that adding 2 processes into a 6-compartment model with 15 processes can drastically affect the conclusions, illustrating the importance of a more complete but complicated model. A systematic review in 2022 presented 24 ordinary differential equations (ODE) models of substance use/abuse epidemiology. This study aims to assemble these 24 ODE models, for single substance use only, by stepwise analysis and assembly. Multiple substance uses and comorbidities are deemed out of scope. The assembled model consists of 11 compartments [(i) susceptible without or refusing health education (S), (ii) susceptible with or accepted health education (C), (iii) light drug users (L), (iv) heavy drug users (H), (v) users under in-patient treatment (Ti), (vi) users under out-patient treatment (To), (vii) users in remission (Re), (viii) drug sellers (D), (ix) susceptible who matured (M), (x) users who quit permanently (Q), and (xi) removed (R)] with 42 processes and 40 parameters. We present the assembled model, SubstanceUseModel, as a Python command-line script where model parameters can be changed using command-line arguments, to improve its usability. This can form the basis for further model development in the field.

 Keywords: Substance Use Epidemiology; Substance Abuse Epidemiology; Ordinary Differential Equations (ODE) Models; 5^th Order Dormand-Prince; Python Command-line tool

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Citation

Citation: Maurice HT Ling., et al. “Assembly of Single Substance Use Epidemiological Models”.Acta Scientific Medical Sciences 8.1 (2024): 43-50.

Copyright

Copyright: © 2024 Maurice HT Ling., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.