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

Matthew J. Silk,R. Alexander Bentley,Simon Carrignon,Nina H. Fefferman

doi:10.1186/s12889-021-12353-9

Abstract

BackgroundIndividual behavioural decisions are responses to a person’s perceived social norms that could be shaped 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 the circumstances under which the influence of these different social networks can promote the acceptance of non-pharmaceutical interventions and consequently the adoption of protective behaviours is critical for guiding useful, practical public health messaging.MethodsWe explore how information from both physical contact and social communication layers of a multiplex network can contribute to flattening the epidemic curve in a community. Connections in the physical contact layer represent opportunities for transmission, while connections in the communication layer represent social interactions through which individuals may gain information, e.g. messaging friends.ResultsWe show that maintaining focus on awareness of risk among each individual’s physical 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 (~ ≥ 20%). Information from the social communication layer without was less useful when these connections matched less well with physical contacts and contributed little in combination with accurate information from physical contacts.ConclusionsWe 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.

Highlights

Individual behavioural decisions are responses to a person’s perceived social norms that could be shaped by both their physical and social environment
We consider the potential impact homophily based upon predisposition in either the communication or both layers of the network. These studies offer insight into how Overview We used stochastic, theoretical models to test how the awareness of symptomatic neighbours in either a) the set of people that a person who communicates with on a regular basis, b) the set of people that a person is in close proximity to, or c) both of these sets of contacts can impact epidemic spread of an infection with COVID-19 like dynamics
When social construction is instead strong, it plays an important role in flattening the curve except in the case when the Reassurance Effect is strong, meaning that differences caused by the source of information for the Awareness Effect are only

Summary

Introduction

Individual behavioural decisions are responses to a person’s perceived social norms that could be shaped by both their physical and social environment. Our current best public health recommendations for mitigation of the COVID-19 pandemic rely on using behavioural interventions such as social distancing and mask wearing, and behaviourally driven acceptance of vaccines (where available) to curtail transmission of infection. The success of these policies requires widespread adherence. In the case of COVID19, adoption of and adherence to behavioural interventions are likely to be predicated on perception of two main features: a) individual attitudes and beliefs about personal risk of infection and its consequences [13], and b) the social norms around adherence in the individual’s community [14]. Over time the changing attitudes and beliefs within each person’s network will drive complex, non-linear dynamics in population-level behaviours [15,16,17]

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Discussion

Conclusion