|
![]()
| Mathematical modelling of human response behaviour during pandemics Interest1 #701146 Mathematical modelling of human response behaviour, opinion dynamics, and social influence during pandemics. COVID-19 showed that understanding human response to intervention is essential in mitigating disease spread and forming policy. We are particularly interested in understanding how opinion influence affects vaccine and NPI hesitancy. This project aims to incorporate a broader understanding of intervention and control, which embodies the entire theme. | - Co-Project Investigators: Iain Moyles (York University) and Rebecca Tyson (University of British Columbia).
|
+Citations (1) - CitationsAdd new citationList by: CiterankMapLink[1] Transient prophylaxis and multiple epidemic waves
Author: Rebecca C. Tyson, Noah D. Marshall, Bert O. Baumgaertner
Publication date: 10 January 2022
Publication info: AIMS Mathematics, 2022, Volume 7, Issue 4: 5616-5633.
Cited by: David Price 4:18 PM 15 November 2023 GMT
Citerank: (6) 679842Mark LewisProfessor Mark Lewis, Kennedy Chair in Mathematical Biology at the University of Victoria and Emeritus Professor at the University of Alberta.10019D3ABAB, 679867Rebecca TysonDr. Rebecca C. Tyson is an Associate Professor in Mathematical Biology at the University of British Columbia Okanagan.10019D3ABAB, 701020CANMOD – PublicationsPublications by CANMOD Members144B5ACA0, 701222OMNI – Publications144B5ACA0, 704045Covid-19859FDEF6, 715328Nonpharmaceutical Interventions (NPIs)859FDEF6
URL:
DOI: https://doi.org/10.3934/math.2022311
| | Excerpt / Summary [AIMS Mathematics, 10 January 2022]
Public opinion and opinion dynamics can have a strong effect on the transmission rate of an infectious disease for which there is no vaccine. The coupling of disease and opinion dynamics however, creates a dynamical system that is complex and poorly understood. We present a simple model in which susceptible groups adopt or give up prophylactic behaviour in accordance with the influence related to pro- and con-prophylactic communication. This influence varies with disease prevalence. We observe how the speed of the opinion dynamics affects the total size and peak size of the epidemic. We find that more reactive populations will experience a lower peak epidemic size, but possibly a larger final size and more epidemic waves, and that an increase in polarization results in a larger epidemic. |
|
|