# An agent-based model of the spread of behavioural risk-factors for cardiovascular disease in city-scale populations

## TL;DR

This paper creates a city-scale model to study how risky behaviors for heart disease spread through social networks and how interventions could reduce heart disease events.

## Contribution

The novel contribution is a validated, open-source agent-based model simulating the spread of four CVD risk behaviors in large populations.

## Key findings

- The model accurately predicts cardiovascular disease events over ten years when compared to real data.
- Workplace interventions modeled in the ABM can effectively reduce the number of CVD events.
- The model remains stable and realistic even with up to 1.2 million agents.

## Abstract

Cardiovascular disease (CVD) is the leading cause of mortality globally, and is the second main cause of mortality in the UK. Four key modifiable behaviours are known to increase CVD risk, namely: tobacco use, unhealthy diet, physical inactivity and harmful use of alcohol. Behaviours that increase the risk of CVD can spread through social networks because individuals consciously and unconsciously mimic the behaviour of others they relate to and admire. Exploiting these social influences may lead to effective and efficient public health interventions to prevent CVD. This project aimed to construct and validate an agent-based model (ABM) of how the four major behavioural risk-factors for CVD spread through social networks in a population, and examine whether the model could be used to identify targets for public health intervention and to test intervention strategies. Previous ABMs have typically focused on a single risk factor or considered very small populations. We created a city-scale ABM to model the behavioural risk-factors of individuals, their social networks (spousal, household, friendship and workplace), the spread of behaviours through these social networks, and the subsequent impact on the development of CVD. We compared the model output (predicted CVD events over a ten year period) to observed data, demonstrating that the model output is realistic. The model output is stable up to at least a population size of 1.2M agents (the maximum tested). We found that there is scope for the modelled interventions targeting the spread of these behaviours to change the number of CVD events experienced by the agents over ten years. Specifically, we modelled the impact of workplace interventions to show that the ABM could be useful for identifying targets for public health intervention. The model itself is Open Source and is available for use or extension by other researchers.