Gaurav Singh

The RAMS model of risk

In May 2023 I got the chance to speak with Dr. Santosh Vijayakumar, a health and risk communication scientist and associate professor of Psychology at Northumbria University in Newcastle. Through those exchanges I came across a paper where he and his colleagues looked at how social media shapes public risk perception during infectious-disease outbreaks. Building on earlier frameworks and using the 2009 H1N1 pandemic as a case study, they set out the Risk Amplification through Media Spread (RAMS) model [1]. It maps how social media amplifies our sense of risk and suggests communication strategies for an outbreak. The paper argues for more work on the model, which is what caught my interest.

Put simply, RAMS explains how our sense of risk around an event, like a disease outbreak, spreads and shifts through communication channels, mostly social media. It starts from primary information sources, chiefly the public health community and the general public; in an event like COVID-19, bodies such as the WHO and CDC become the main informants. It then names the "amplification stations" that boost risk information: people sharing on social media, influencers weighing in, and traditional news broadcasting updates. In our time a single tweet or article can reach a huge audience fast, raising the perceived risk. Finally the model looks at outcomes, the real and felt effects of all this: changes in how individuals and communities behave, spikes in fear, and shifts in institutional policy.

Studying RAMS with real data shows how it plays out in practice, and treating it mathematically helps pin down the detail. Used together, the empirical side gives the big picture and the maths fills it in. That combination seems like the way to develop the model further.

  1. Vijaykumar, Santosh, Jin, Yan and Nowak, Glen. "Social Media and the Virality of Risk: The Risk Amplification through Media Spread (RAMS) Model." Journal of Homeland Security and Emergency Management, vol. 12, no. 3, 2015, pp. 653-677. https://doi.org/10.1515/jhsem-2014-0072 ↩︎