Estimating Population Burden of Stroke with an Agent-Based Model

Elizabeth Hunter; John D. Kelleher

doi:10.1007/978-3-031-57785-7_2

Estimating Population Burden of Stroke with an Agent-Based Model

Elizabeth Hunter
, John D. Kelleher

Dublin Institute of Technology
Trinity College Dublin

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

Abstract

Stroke is one of the leading causes of death and disability worldwide but it is believed to be highly preventable. The majority of stroke prevention focuses on targeting high-risk individuals but its is important to understand how the targeting of high-risk individuals might impact the overall societal burden of stroke. We propose using an agent-based model that follows agents through their pre-stroke and stroke journey to assess the impacts of different interventions at the population level. We present a case study looking at the impacts of agents being informed of their stroke risk at certain ages and those agents taking measure to reduce their risk. The results of our study show that if agents are aware of their risk and act accordingly we see a significant reduction in strokes and population DALYs. The case study highlights the importance of individuals understanding their own stroke risk for stroke prevention and the usefulness of agent-based models in assessing the impact of stroke interventions.

Original language	English
Title of host publication	Advances in Social Simulation - Proceedings of the 18th Social Simulation Conference, 2023
Editors	Corinna Elsenbroich, Harko Verhagen
Publisher	Springer Science and Business Media B.V.
Pages	9-20
Number of pages	12
ISBN (Print)	9783031577840
DOIs	https://doi.org/10.1007/978-3-031-57785-7_2
Publication status	Published - 2024
Externally published	Yes
Event	18th Social Simulation Conference, SSC23 - Glasgow, United Kingdom Duration: 4 Sept 2023 → 8 Sept 2023

Publication series

Name	Springer Proceedings in Complexity
ISSN (Print)	2213-8684
ISSN (Electronic)	2213-8692

Conference

Conference	18th Social Simulation Conference, SSC23
Country/Territory	United Kingdom
City	Glasgow
Period	4/09/23 → 8/09/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Agent-based model
Risk Modelling
Stroke

Access to Document

10.1007/978-3-031-57785-7_2

Cite this

Hunter, E., & Kelleher, J. D. (2024). Estimating Population Burden of Stroke with an Agent-Based Model. In C. Elsenbroich, & H. Verhagen (Eds.), Advances in Social Simulation - Proceedings of the 18th Social Simulation Conference, 2023 (pp. 9-20). (Springer Proceedings in Complexity). Springer Science and Business Media B.V.. https://doi.org/10.1007/978-3-031-57785-7_2

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title = "Estimating Population Burden of Stroke with an Agent-Based Model",

abstract = "Stroke is one of the leading causes of death and disability worldwide but it is believed to be highly preventable. The majority of stroke prevention focuses on targeting high-risk individuals but its is important to understand how the targeting of high-risk individuals might impact the overall societal burden of stroke. We propose using an agent-based model that follows agents through their pre-stroke and stroke journey to assess the impacts of different interventions at the population level. We present a case study looking at the impacts of agents being informed of their stroke risk at certain ages and those agents taking measure to reduce their risk. The results of our study show that if agents are aware of their risk and act accordingly we see a significant reduction in strokes and population DALYs. The case study highlights the importance of individuals understanding their own stroke risk for stroke prevention and the usefulness of agent-based models in assessing the impact of stroke interventions.",

keywords = "Agent-based model, Risk Modelling, Stroke",

author = "Elizabeth Hunter and Kelleher, \{John D.\}",

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Hunter, E & Kelleher, JD 2024, Estimating Population Burden of Stroke with an Agent-Based Model. in C Elsenbroich & H Verhagen (eds), Advances in Social Simulation - Proceedings of the 18th Social Simulation Conference, 2023. Springer Proceedings in Complexity, Springer Science and Business Media B.V., pp. 9-20, 18th Social Simulation Conference, SSC23, Glasgow, United Kingdom, 4/09/23. https://doi.org/10.1007/978-3-031-57785-7_2

Estimating Population Burden of Stroke with an Agent-Based Model. / Hunter, Elizabeth; Kelleher, John D.
Advances in Social Simulation - Proceedings of the 18th Social Simulation Conference, 2023. ed. / Corinna Elsenbroich; Harko Verhagen. Springer Science and Business Media B.V., 2024. p. 9-20 (Springer Proceedings in Complexity).

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

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T1 - Estimating Population Burden of Stroke with an Agent-Based Model

AU - Hunter, Elizabeth

AU - Kelleher, John D.

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PY - 2024

Y1 - 2024

N2 - Stroke is one of the leading causes of death and disability worldwide but it is believed to be highly preventable. The majority of stroke prevention focuses on targeting high-risk individuals but its is important to understand how the targeting of high-risk individuals might impact the overall societal burden of stroke. We propose using an agent-based model that follows agents through their pre-stroke and stroke journey to assess the impacts of different interventions at the population level. We present a case study looking at the impacts of agents being informed of their stroke risk at certain ages and those agents taking measure to reduce their risk. The results of our study show that if agents are aware of their risk and act accordingly we see a significant reduction in strokes and population DALYs. The case study highlights the importance of individuals understanding their own stroke risk for stroke prevention and the usefulness of agent-based models in assessing the impact of stroke interventions.

AB - Stroke is one of the leading causes of death and disability worldwide but it is believed to be highly preventable. The majority of stroke prevention focuses on targeting high-risk individuals but its is important to understand how the targeting of high-risk individuals might impact the overall societal burden of stroke. We propose using an agent-based model that follows agents through their pre-stroke and stroke journey to assess the impacts of different interventions at the population level. We present a case study looking at the impacts of agents being informed of their stroke risk at certain ages and those agents taking measure to reduce their risk. The results of our study show that if agents are aware of their risk and act accordingly we see a significant reduction in strokes and population DALYs. The case study highlights the importance of individuals understanding their own stroke risk for stroke prevention and the usefulness of agent-based models in assessing the impact of stroke interventions.

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Estimating Population Burden of Stroke with an Agent-Based Model

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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