COVID-19: An Adaptive System
Systems thinking can help policymakers understand and influence the spread of infection and its multifaceted consequences across the community since society is itself a complex adaptive system. It can provide a framework to look beyond the chain of infection and better understand the multiple implications of decisions and (in)actions in face of such a complex situation involving many interconnected factors. Causal loop diagrams (CLDs) are tools to depict the causal connections between components of a system, and illustrate how changes in one component cascade in changes in others and back to itself, via feedback loops, potentially affecting the status of the entire system. The figure presents a simple CLD as an example of some important interacting components in a society that is responding to the threat of COVID-19.
A reinforcing feedback loop is responsible for causing exponential growth in the number of infected people (in red). However, the risk of transmission (often expressed as the basic reproduction number, R0) is seen to be a factor of the context, not simply a characteristic of the virus, resulting from a long chain of dynamic interactions involving components otherwise seen as distant or disconnected, such as the public’s trust in authorities and stigma.
Risk communication influences people’s capability and motivation to perform protective behaviors. However, public alarm about a novel hazard and low trust in authorities may result in ‘outrage’. In our illustrative CLD, this may give rise to stigma, reducing detection of infectious people, and therefore reducing the intensity of individual and societal responses. As the novelty of the situation declines, so may outrage, risk perception and individual protective behaviors.