Visualising uncertainty of vaccine data

Qianhui Lin; Peter D. Donnelly; Areti Manataki

doi:10.3233/shti250387

Visualising uncertainty of vaccine data

Qianhui Lin
, Peter D. Donnelly
, Areti Manataki^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

As healthcare data grows more complex, effectively conveying the inherent uncertainties in medical research and clinical decision-making is crucial to avoid misinterpretation. This study explores visualising the effectiveness and safety of the herpes zoster vaccine, focusing on conveying associated uncertainties to the general public. Two visualisation formats (static and interactive) were developed. A questionnaire-based evaluation assessed their effectiveness and impact on vaccination intentions. Both formats effectively conveyed data, with most participants accurately interpreting the information. Vaccination intentions generally declined after viewing the visualisations, indicating that presenting comprehensive information informs decision-making. This study offers a design approach for visualising vaccine data uncertainties, which has the potential to enhance healthcare decision quality regarding vaccination.

Original language	English
Title of host publication	Intelligent health systems – from technology to data and knowledge
Subtitle of host publication	Proceedings of MIE 2025
Editors	Elisavet Andrikopoulou, Parisis Gallos, Theodoros N. Arvanitis, Rosalynn Austin, Arriel Benis, Ronald Cornet, Panagiotis Chatzistergos, Alexander Dejaco, Linda Dusseljee-Peute, Alaa Mohasseb, Pantelis Natsiavas, Haythem Nakkas, Philip Scott
Place of Publication	Amsterdam
Publisher	IOS Press / Sage
Pages	497-501
Number of pages	5
ISBN (Electronic)	9781643685960
DOIs	https://doi.org/10.3233/shti250387
Publication status	Published - 15 May 2025

Publication series

Name	Studies in health technology and informatics
Publisher	IOS Press BV
Volume	327
ISSN (Print)	0926-9630
ISSN (Electronic)	879-8365

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Visualisation
Uncertainty
Vaccine
Risk communication

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10.3233/shti250387Licence: CC BY-NC

Lin-et-al-2025-Visualising-uncertainty-of-vaccine-data-MIE2025-497-CCBYNC
Copyright © 2025 The Authors. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
Final published version, 293 KBLicence: CC BY-NC

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Lin, Q., Donnelly, P. D., & Manataki, A. (2025). Visualising uncertainty of vaccine data. In E. Andrikopoulou, P. Gallos, T. N. Arvanitis, R. Austin, A. Benis, R. Cornet, P. Chatzistergos, A. Dejaco, L. Dusseljee-Peute, A. Mohasseb, P. Natsiavas, H. Nakkas, & P. Scott (Eds.), Intelligent health systems – from technology to data and knowledge: Proceedings of MIE 2025 (pp. 497-501). (Studies in health technology and informatics; Vol. 327). IOS Press / Sage. https://doi.org/10.3233/shti250387

Lin, Qianhui ; Donnelly, Peter D. ; Manataki, Areti. / Visualising uncertainty of vaccine data. Intelligent health systems – from technology to data and knowledge: Proceedings of MIE 2025. editor / Elisavet Andrikopoulou ; Parisis Gallos ; Theodoros N. Arvanitis ; Rosalynn Austin ; Arriel Benis ; Ronald Cornet ; Panagiotis Chatzistergos ; Alexander Dejaco ; Linda Dusseljee-Peute ; Alaa Mohasseb ; Pantelis Natsiavas ; Haythem Nakkas ; Philip Scott. Amsterdam : IOS Press / Sage, 2025. pp. 497-501 (Studies in health technology and informatics).

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title = "Visualising uncertainty of vaccine data",

abstract = "As healthcare data grows more complex, effectively conveying the inherent uncertainties in medical research and clinical decision-making is crucial to avoid misinterpretation. This study explores visualising the effectiveness and safety of the herpes zoster vaccine, focusing on conveying associated uncertainties to the general public. Two visualisation formats (static and interactive) were developed. A questionnaire-based evaluation assessed their effectiveness and impact on vaccination intentions. Both formats effectively conveyed data, with most participants accurately interpreting the information. Vaccination intentions generally declined after viewing the visualisations, indicating that presenting comprehensive information informs decision-making. This study offers a design approach for visualising vaccine data uncertainties, which has the potential to enhance healthcare decision quality regarding vaccination.",

keywords = "Visualisation, Uncertainty, Vaccine, Risk communication",

author = "Qianhui Lin and Donnelly, \{Peter D.\} and Areti Manataki",

year = "2025",

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doi = "10.3233/shti250387",

language = "English",

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editor = "Elisavet Andrikopoulou and Parisis Gallos and Arvanitis, \{Theodoros N.\} and Rosalynn Austin and Arriel Benis and Ronald Cornet and Panagiotis Chatzistergos and Alexander Dejaco and Linda Dusseljee-Peute and Alaa Mohasseb and Pantelis Natsiavas and Haythem Nakkas and Philip Scott",

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Lin, Q, Donnelly, PD & Manataki, A 2025, Visualising uncertainty of vaccine data. in E Andrikopoulou, P Gallos, TN Arvanitis, R Austin, A Benis, R Cornet, P Chatzistergos, A Dejaco, L Dusseljee-Peute, A Mohasseb, P Natsiavas, H Nakkas & P Scott (eds), Intelligent health systems – from technology to data and knowledge: Proceedings of MIE 2025. Studies in health technology and informatics, vol. 327, IOS Press / Sage, Amsterdam, pp. 497-501. https://doi.org/10.3233/shti250387

Visualising uncertainty of vaccine data. / Lin, Qianhui; Donnelly, Peter D.; Manataki, Areti.
Intelligent health systems – from technology to data and knowledge: Proceedings of MIE 2025. ed. / Elisavet Andrikopoulou; Parisis Gallos; Theodoros N. Arvanitis; Rosalynn Austin; Arriel Benis; Ronald Cornet; Panagiotis Chatzistergos; Alexander Dejaco; Linda Dusseljee-Peute; Alaa Mohasseb; Pantelis Natsiavas; Haythem Nakkas; Philip Scott. Amsterdam: IOS Press / Sage, 2025. p. 497-501 (Studies in health technology and informatics; Vol. 327).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Visualising uncertainty of vaccine data

AU - Lin, Qianhui

AU - Donnelly, Peter D.

AU - Manataki, Areti

PY - 2025/5/15

Y1 - 2025/5/15

N2 - As healthcare data grows more complex, effectively conveying the inherent uncertainties in medical research and clinical decision-making is crucial to avoid misinterpretation. This study explores visualising the effectiveness and safety of the herpes zoster vaccine, focusing on conveying associated uncertainties to the general public. Two visualisation formats (static and interactive) were developed. A questionnaire-based evaluation assessed their effectiveness and impact on vaccination intentions. Both formats effectively conveyed data, with most participants accurately interpreting the information. Vaccination intentions generally declined after viewing the visualisations, indicating that presenting comprehensive information informs decision-making. This study offers a design approach for visualising vaccine data uncertainties, which has the potential to enhance healthcare decision quality regarding vaccination.

AB - As healthcare data grows more complex, effectively conveying the inherent uncertainties in medical research and clinical decision-making is crucial to avoid misinterpretation. This study explores visualising the effectiveness and safety of the herpes zoster vaccine, focusing on conveying associated uncertainties to the general public. Two visualisation formats (static and interactive) were developed. A questionnaire-based evaluation assessed their effectiveness and impact on vaccination intentions. Both formats effectively conveyed data, with most participants accurately interpreting the information. Vaccination intentions generally declined after viewing the visualisations, indicating that presenting comprehensive information informs decision-making. This study offers a design approach for visualising vaccine data uncertainties, which has the potential to enhance healthcare decision quality regarding vaccination.

KW - Visualisation

KW - Uncertainty

KW - Vaccine

KW - Risk communication

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DO - 10.3233/shti250387

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T3 - Studies in health technology and informatics

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BT - Intelligent health systems – from technology to data and knowledge

A2 - Andrikopoulou, Elisavet

A2 - Gallos, Parisis

A2 - Arvanitis, Theodoros N.

A2 - Austin, Rosalynn

A2 - Benis, Arriel

A2 - Cornet, Ronald

A2 - Chatzistergos, Panagiotis

A2 - Dejaco, Alexander

A2 - Dusseljee-Peute, Linda

A2 - Mohasseb, Alaa

A2 - Natsiavas, Pantelis

A2 - Nakkas, Haythem

A2 - Scott, Philip

PB - IOS Press / Sage

CY - Amsterdam

ER -

Lin Q, Donnelly PD , Manataki A. Visualising uncertainty of vaccine data. In Andrikopoulou E, Gallos P, Arvanitis TN, Austin R, Benis A, Cornet R, Chatzistergos P, Dejaco A, Dusseljee-Peute L, Mohasseb A, Natsiavas P, Nakkas H, Scott P, editors, Intelligent health systems – from technology to data and knowledge: Proceedings of MIE 2025. Amsterdam: IOS Press / Sage. 2025. p. 497-501. (Studies in health technology and informatics). doi: 10.3233/shti250387

Visualising uncertainty of vaccine data

Abstract

Publication series

UN SDGs

Keywords

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