Where to learn to flatten the curve: a modelling study
preprint
OA: gold
CC-BY-NC-ND-4.0
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This modeling study found that focusing on awareness of disease risk within physical contact networks, rather than social communication networks, most effectively reduces disease spread.
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Abstract
Background Individual behavioural decisions are responses to both a person’s perceived social norms and could be driven by both their physical and social environment. In the context of the COVID-19 pandemic, these environments correspond to epidemiological risk from contacts and the social construction of risk by communication within networks of friends. Understanding when, and under which circumstances, each modality of influence can foster the widespread adoption of protective behaviours is critical for shaping useful, practical public health messaging that will best enhance the public response. Methods We use a multiplex network approach to explore how information from both physical contact and social communication networks is driving a mitigating behavioural response to disease risk. Findings We show that maintaining focus on awareness of risk in each individual’s physical layer contacts promotes the greatest reduction in disease spread, but only when an individual is aware of the symptoms of a non-trivial proportion of their physical contacts (approximately 20% or more). Information from the communication layer was less useful when these connections matched less well with physical contacts and contributed little in combination with accurate information from the physical layer. Interpretation We conclude that maintaining social focus on local outbreak status will allow individuals to structure their perceived social norms appropriately and respond more rapidly when risk increases. Finding ways to relay accurate local information from trusted community leaders could improve mitigation even where more intrusive/costly strategies, such as contact-tracing, are not possible.
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- europepmc
- last seen: 2026-05-19T01:45:01.086888+00:00
- unpaywall
- last seen: 2026-05-21T05:10:58.409756+00:00
License: CC-BY-NC-ND-4.0