Literature DB >> 34284634

Improving pandemic mitigation policies across communities through coupled dynamics of risk perception and infection.

M J Silk1, S Carrignon2,3,4, R A Bentley3, N H Fefferman5,6.   

Abstract

Capturing the coupled dynamics between individual behavioural decisions that affect disease transmission and the epidemiology of outbreaks is critical to pandemic mitigation strategy. We develop a multiplex network approach to model how adherence to health-protective behaviours that impact COVID-19 spread are shaped by perceived risks and resulting community norms. We focus on three synergistic dynamics governing individual behavioural choices: (i) social construction of concern, (ii) awareness of disease incidence, and (iii) reassurance by lack of disease. We show why policies enacted early or broadly can cause communities to become reassured and therefore unwilling to maintain or adopt actions. Public health policies for which success relies on collective action should therefore exploit the behaviourally receptive phase; the period between the generation of sufficient concern to foster adoption of novel actions and the relaxation of adherence driven by reassurance fostered by avoidance of negative outcomes over time.

Entities:  

Keywords:  behavioural epidemiology; behaviourally receptive phase; disease risk perception; social norms

Mesh:

Year:  2021        PMID: 34284634     DOI: 10.1098/rspb.2021.0834

Source DB:  PubMed          Journal:  Proc Biol Sci        ISSN: 0962-8452            Impact factor:   5.349


  4 in total

1.  Group size and modularity interact to shape the spread of infection and information through animal societies.

Authors:  Julian C Evans; David J Hodgson; Neeltje J Boogert; Matthew J Silk
Journal:  Behav Ecol Sociobiol       Date:  2021-11-27       Impact factor: 2.980

2.  Observations and conversations: how communities learn about infection risk can impact the success of non-pharmaceutical interventions against epidemics.

Authors:  Matthew J Silk; Simon Carrignon; R Alexander Bentley; Nina H Fefferman
Journal:  BMC Public Health       Date:  2022-01-05       Impact factor: 3.295

3.  Balancing timeliness of reporting with increasing testing probability for epidemic data.

Authors:  Alexander J Pritchard; Matthew J Silk; Simon Carrignon; R Alexander Bentley; Nina H Fefferman
Journal:  Infect Dis Model       Date:  2022-04-06

4.  How reported outbreak data can shape individual behavior in a social world.

Authors:  Alexander J Pritchard; Matthew J Silk; Simon Carrignon; R Alexander Bentley; Nina H Fefferman
Journal:  J Public Health Policy       Date:  2022-08-10       Impact factor: 3.526

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.