Restocchi, Valerio

Goddard, Benjamin

Prabhakaran, Adarsh

PHASE network

Artificial Intelligence Applications Institute, University of Edinburgh

2024-11-20T14:28:19Z

2024-11-20T14:28:19Z

2024-11-20

Tobacco use remains one of the leading public health threats worldwide. Despite the implementation of various policies, the tobacco epidemic still persists, particularly in deprived communities. The tobacco use environment is a complex system, with multiple factors interacting with each other. These factors include not only the interactions among individuals that contribute to the spread of smoking behaviour but also the influence of socio-economic and spatial environments on individual smoking habits. This thesis employs a complex systems perspective to enhance our understanding of the tobacco use environment. Utilising agent-based models (ABM), we aim to capture the various factors contributing to different smoking processes and behaviours. Through these models, we investigate the spread of smoking behaviour, evaluate the impact of network topology on these behaviours, develop novel network-based interventions to curb smoking, explore the socio-economic and spatial complexities affecting smoking initiation, and assess the diverse long-term impacts of tobacco regulations. The first part of the thesis focuses on the spread of tobacco use through interactions between individuals, using a network-based ABM. We demonstrate that network topology and specifically network degree, is a significant factor in the spread of smoking behaviour. Furthermore, we suggest alternative synthetic networks for instances when network data is unavailable. Utilising the model, we can devise network interventions aimed at leveraging the contagious nature of quitting behaviour. In the subsequent chapter, we shift our focus to the development of a simple, cost-effective network intervention to enhance the effectiveness of smoking cessation clinics. Our proposed approach is based on the ‘friendship paradox’, which suggests one’s friends are likely to have more friends than oneself. Our findings indicate that this friendship paradox based strategy outperforms existing methods employed in smoking cessation programmes, both in lowering overall smoking rates and in reducing the disparity between smoking levels in deprived and non-deprived areas. The third chapter introduces a novel spatial ABM that captures the complexities of smoking initiation. This framework considers not just interactions between individuals but also how socio-economic and spatial environments influence smoking behaviour. We simulate a realistic neighbourhood in Scotland, replicating daily commutes, social interactions, and smoking patterns among agents. Our model draws upon extensive data from Scotland to craft a comprehensive socio-economic landscape, incorporating elements such as population density, retailer distribution, cigarette pricing, age demographics, school and workplace densities, income brackets, and existing smoking rates. With a focus on the 15 to 24 age group, a critical demographic for smoking initiation, this chapter provides an adaptable simulation platform for assessing the long-term consequences of various types of regulatory strategies. In the final chapter, the spatial ABM developed is employed to assess the impact of four distinct tobacco control policies: raising cigarette prices, enforcing a ban on tobacco sales around schools, increasing the legal age of sales to 21, and a strategy that combines all three. To assess the impact of these policies, we examine multiple tobacco indicators such as prevalence, tobacco consumption, tobacco sales, and financial spending on tobacco. We also compare the effects of these policies across different socio-economic neighbourhoods. We show that a multifaceted approach not only enhances the impact of individual policies but also effectively reduces the widening of socio-economic inequalities. Collectively, this thesis showcases the potential of ABMs as a dynamic testbed for comprehending, simulating, and shaping tobacco control strategies. The findings presented herein contribute to the advancement of evidence-based tobacco control policies by offering a multifaceted perspective on tobacco use dynamics and the complexities of the regulatory environment.

https://hdl.handle.net/1842/42672

http://dx.doi.org/10.7488/era/5366

en

The University of Edinburgh

Adarsh Prabhakaran, Valerio Restocchi, and Benjamin D Goddard. Networks for smoking dynamics. In The 11th International Conference on Complex Networks and their Applications 2022, 2022

Adarsh Prabhakaran, Valerio Restocchi, and Benjamin D Goddard. Improving tobacco social contagion models using agent-based simulations on networks. Applied Network Science, 8(1):1–21, 2023

Tobacco

tobacco use environment

agent-based models

network-based ABM

spatial ABM

Scotland

15 to 24 age group

smoking initiation

tobacco control policies

Applications of agent-based models in tobacco control

Thesis or Dissertation

Doctoral

PhD Doctor of Philosophy