Hill, Edward M 
ORCID: 0000-0002-2992-2004, Ryan, Matthew, Haw, David ORCID: 0000-0002-9228-0542, Lynch, Mark P, McCabe, Ruth, Milne, Alice E, Turner, Matthew S, Vedhara, Kavita, Zeng, Fanqi, Barons, Martine J et al (show 3 more authors)
(2024)
Integrating human behaviour and epidemiological modelling: unlocking the remaining challenges
Mathematics in Medical and Life Sciences, 1 (1).
2429479-.
ISSN 2993-7574, 2993-7574
|
Text
Integrating human behaviour and epidemiological modelling unlocking the remaining challenges.pdf - Open Access published version Available under License Creative Commons Attribution. Download (3MB) | Preview |
Abstract
This paper is part of a special issue on Behavioural Epidemiology. Historically, responses to health-related emergencies (whether public health, veterinary health or plant health related) have exposed the deficiencies of mathematical models to incorporate data-driven and/or theoretical knowledge on outbreak behavioural dynamics. Interdisciplinary collaboration is vital to improve realism in methodological approaches to considering behavioural dynamics in an unfolding situation. We must bring together novel ideas across the behavioural, biological, data and mathematical sciences. The purpose of our article is threefold. We first present our perspective on the vital role of interdisciplinary collaboration to enable the effective integration of the dynamics of human behaviour and epidemiological models – we refer to such integrated models as “epidemiological-behavioural” models. We then summarise issues to be resolved by interdisciplinary teams of experts within four contemporary epidemiological-behavioural modelling challenge areas that we consider to require immediate and sustained research attention: understanding of human behaviour; data; modelling methodologies and parameterisation; how modelling (and communication of its findings) affects behaviour. Lastly, to serve as a resource for research scientists, practitioners and policy makers interested in getting involved in tackling these epidemiological-behavioural modelling challenges, we pose recommendations to make progress in each of the challenge areas and our viewpoint on their potential societal benefits if enacted.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 31 Biological Sciences, 4206 Public Health, 42 Health Sciences, 2.5 Research design and methodologies (aetiology), Generic health relevance |
| Divisions: | Faculty of Health & Life Sciences Faculty of Science & Engineering Faculty of Science & Engineering > School of Physical Sciences Faculty of Health & Life Sciences > Inst. Population Health |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 29 Nov 2024 16:42 |
| Last Modified: | 23 May 2026 09:28 |
| DOI: | 10.1080/29937574.2024.2429479 |
| Open Access URL: | https://doi.org/10.1080/29937574.2024.2429479 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3188951 |
| Disclaimer: | The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate. |
Altmetric
Altmetric