I listen to the radio everyday on my drive to work and I’ve been hearing a lot about COVID-19 (Coronavirus). Tales of its spread across the globe, proposed plans for preventative action, and tips for staying busy in self-quarantine are now mainstays of my commute. All this despite the risk of infection where I live in Canada remaining low, at the time of writing.
Yet, the level of risk doesn’t seem to correlate with the behavior of many Canadians.
We’ve all seen social media posts or news reports of shelves across the country empty of hand sanitizer and facemasks, pasta and toilet paper. A friend shared on Twitter that her hair salon has cancelled her appointment, citing risk of coronavirus as the reason for this self-imposed shutdown (despite only 2 reported cases in the entire province at the time).
What is driving this seemingly disproportionate response?
It is well established that the response to the threat of disease is driven by people’s perception of risk (1). That is, however great or small we personally believe our risk to be is often a better predictor of our behavior than is an objective metric of that risk (2). In turn, our perceptions of risk are influenced by information we encounter in the media (1). Yet, as mentioned, the broadcasters here have consistently drawn attention to the relatively low risk at present. What then can explain the disparity?
At the start of an epidemic (or now, a pandemic) information received about the crisis by the media greatly influences our behavior (3), which in turn affects the efficacy of societal responses necessary to contain the spread. However, the information we receive is no longer like what it once was: dependent on geography (4). Most of us regularly use social media — I personally am on Twitter and Instagram multiple times a day — and receive much of our information and news headlines from both friends and strangers, at home and across the globe. This information can be consumed by anyone, and although it is accessed within a cluster of ‘followers’, these ‘followers’ are not close in physical proximity (4). Though a pandemic is a global struggle, the transmission risk across different countries is not equal, and so the perception of risk and the corresponding responses should not be either. For those of us on social media, we are constantly exposed to information that does not necessarily pertain to our local communities, and therefore our reactions may not correlate to local disease risk (4).
In a pandemic, information on the risk of infection is often coupled with information on prevention behaviors — and the former can impact the uptake of the latter. Both types of information have been visible on social media in recent weeks. More recently, social media posts have encouraged social distancing by deeming those who continue going out to bars and restaurants as “irresponsible”. My exposure over the past few days to this content resulted in feelings of guilt that led me to cancel a little getaway to a neighbouring community.
Whether it was aligned with actual risk or not, the quantity of the information being shared, and who is sharing this information, has undoubtedly influenced my uptake of preventative measures. Research suggests that such promotion of disease prevention behaviors on social media is actually quite effective (4,5). This is thought to be due to both ‘homophily’ in and ‘clustering’ of our virtual social networks (5). Homophily is “the tendency of people to associate with those who resemble them” (5), while clustering is the “tendency for people’s friends to be connected to each other through redundant ties” (5). In other words, we are more likely to adopt a health behavior if we know someone similar to us has done so, too (5).
Behavioral Science, Democratized
We make 35,000 decisions each day, often in environments that aren’t conducive to making sound choices.
At TDL, we work with organizations in the public and private sectors—from new startups, to governments, to established players like the Gates Foundation—to debias decision-making and create better outcomes for everyone.
The AI Governance Challenge
What is the problem if people in low-risk environments start washing their hands more frequently and practicing social distancing? Other than the economic impact, the potential issue on public health is that the influence of media on our uptake of preventative behavior is not uniform across the duration of an epidemic. A study by Xiao and co-authors from 2015 illustrates this point. Therein, the authors investigate the media impact of infectious disease transmission during the 2009 A/H1N1 influenza, or swine flu, outbreak. The analysis shows that media coverage significantly decreased the severity of the outbreak (3) — however, the effect was not uniform. Media coverage had the greatest effect during the early stage of the outbreak, but had no significant impact at the peak (3). This is because uptake of behavior change is most influenced by the “rate of change of case numbers” rather than the absolute number of cases (3). This means that knowing how fast the disease is spreading, influences our uptake of preventive behavior more so than knowing how many people are sick. At the onset of an epidemic, the spread of disease is rapid, but at the peak, the number of new cases is relatively the same and so the rate of change is (by definition) zero. Consequently, how and when we receive information about an epidemic influences our individual response.
Early preventative measures can be seen as mostly positive, even if they are not directly correlated with risk levels. However, if we are both exposed to and affected by information from across the globe, will our responses also be affected by conditions elsewhere? Specifically, as countries and communities approach the epidemic peak (and thus the lower rate of change) at different times, will the uptake of necessary preventive behavior be sustained in areas where disease spread is still rapid? This is the underlying challenge posed by the ubiquity of social media during a pandemic. The best advice is to follow globally but react locally.
Featured image source: Reuters/Stringer
- Tchuenche, Jean M., et al. “The impact of media coverage on the transmission dynamics of human influenza.” BMC Public Health 11.S1 (2011): S5.
- Paek, H. J., & Hove, T. (2017). Risk perceptions and risk characteristics. In Oxford Research Encyclopedia of Communication.
- Xiao, Yanni, Sanyi Tang, and Jianhong Wu. “Media impact switching surface during an infectious disease outbreak.” Scientific reports 5 (2015): 7838.
- Verelst, Frederik, Lander Willem, and Philippe Beutels. “Behavioural change models for infectious disease transmission: a systematic review (2010–2015).” Journal of The Royal Society Interface 13.125 (2016): 20160820.
- Laranjo, Liliana, et al. “The influence of social networking sites on health behavior change: a systematic review and meta-analysis.” Journal of the American Medical Informatics Association 22.1 (2015): 243-256.
About the Author
Sara is an Epidemiologist working in Nunavut. She obtained her MSc in Public Health from McGill University. Prior to her career in public health, she was a biochemist, and holds an MSc in Biochemistry from McGill University. Her interest in social determinants of health, and health behaviour change led to this career transition. She is also the co-founder of Climbable, a Montreal based non-profit focused on involving Canadians in climate action.