Measured voluntary avoidance behaviour during the 2009 A/H1N1 epidemic

Abstract

Managing infectious disease is among the foremost challenges for public health policy. Interpersonal contacts play a critical role in infectious disease transmission, and recent advances in epidemiological theory suggest a central role for adaptive human behaviour with respect to changing contact patterns. However, theoretical studies cannot answer the following question: are individual responses to disease of sufficient magnitude to shape epidemiological dynamics and infectious disease risk? We provide empirical evidence that Americans voluntarily reduced their time spent in public places during the 2009 A/H1N1 swine flu, and that these behavioural shifts were of a magnitude capable of reducing the total number of cases. We simulate 10 years of epidemics (2003–2012) based on mixing patterns derived from individual time-use data to show that the mixing patterns in 2009 yield the lowest number of total infections relative to if the epidemic had occurred in any of the other nine years. The World Health Organization and other public health bodies have emphasized an important role for ‘distancing’ or non-pharmaceutical interventions. Our empirical results suggest that neglect for voluntary avoidance behaviour in epidemic models may overestimate the public health benefits of public social distancing policies.

Behavioural change models for infectious disease transmission: a systematic review (2010–2015)

“…[57,58], Wang et al . [59], Bayham et al [60]Chen et al . [61]PrEP antivirals——Liao & You [40]Liao & You [40], Marathe et al .…”

“…The largest category embodies 76 references managing information as a dynamic parameter [3,25,34,40,43,47–50,53,55,60,61,65–68,70,71,74,77,80,82,86,92,94–96,99,100,102,104–109,113,115–117,119–126,128–131,133,134,137,148,149,153,157,161,165,166,168,171–178,184–186,188,189]. In this category, instead of a threshold, the information is a continuous input in the decision-making process of individuals.…”

“…Retrospective studies on disease emergence are particularly useful when real-time data on behavioural change and disease transmission during an outbreak are available over a sufficiently long time. Social media data and other electronic sources of information are also increasingly used, thus creating opportunities for ‘big data’ collection on disease transmission, behaviour formation and spatial location [25,60,66]. Next, we briefly describe studies constructing their models using observational data, i.e.…”

“…[56] used television viewing habits (average viewing time) as a proxy for social distancing, although this proxy is far removed from a direct estimation of social distancing in an outbreak situation. More promising sources of information include: survey data using, for instance, the HBM framework (also see [191,195,196]) or time-use surveys [3,14,23,24,45,48,60,72,85,127,183] or digital sources such as social media [25,60,66,146,197]. Real-life data collection during the influenza A/H1N1 pandemic in 2009 has been a milestone for the parametrization of BCMs with increased collection of both behaviour and disease-related information.…”

“…While social media require reconsidering how information spread is modelled, they also present an opportunity to gather data on behaviour and behavioural changes. A number of studies we identified already integrated social media data [25,60,66,197]. We expect future modelling studies to increasingly use social media as a data source to parametrize BCMs.…”