Since the beginning of the COVID-19 pandemic, various models of virus spread have been proposed. While most of these models focused on the replication of the interaction processes through which the virus is passed on from infected agents to susceptible ones, less effort has been devoted to the process through which agents modify their behaviour as they adapt to the risks posed by the pandemic. Understanding the way agents respond to COVID-19 spread is important, as this behavioural response affects the dynamics of virus spread by modifying interaction patterns. In this article, we present an agent-based model that includes a behavioural module determining agent testing and isolation propensity in order to understand the role of various behavioural parameters in the spread of COVID-19.

Despite tremendous advancements in population health in recent history, human society currently faces significant challenges from wicked health problems. These are problems where the causal mechanisms at play are obscured and difficult to address, and consequently they have defied efforts to develop effective interventions and policy solutions using traditional population health methods. Systems-based perspectives are vital to the development of effective policy solutions to seemingly intractable health problems like obesity and population aging. ALife in particular is well placed to bring interdisciplinary modeling and simulation approaches to bear on these challenges. This article summarizes the current status of systems-based approaches in population health, and outlines the opportunities that are available for ALife to make a significant contribution to these critical issues.