Newly emerging infectious diseases, such as the coronavirus (COVID-19), create new challenges for public healthcare systems. Before effective treatments, countering the spread of these infections depends on mitigating, protective behaviours such as social distancing, respecting lockdown, wearing masks, frequent handwashing, travel restrictions, and vaccine acceptance. Previous work has shown that the enacting protective behaviours depends on beliefs about individual vulnerability, threat severity, and one's ability to engage in such protective actions. However, little is known about the genesis of these beliefs in response to an infectious disease epidemic, and the cognitive mechanisms that may link these beliefs to decision making. Active inference (AI) is a recent approach to behavioural modelling that integrates embodied perception, action, belief updating, and decision making. This approach provides a framework to understand the behaviour of agents in situations that require planning under uncertainty. It assumes that the brain infers the hidden states that cause sensations, predicts the perceptual feedback produced by adaptive actions, and chooses actions that minimize expected surprise in the future. In this paper, we present a computational account describing how individuals update their beliefs about the risks and thereby commit to protective behaviours. We show how perceived risks, beliefs about future states, sensory uncertainty, and outcomes under each policy can determine individual protective behaviours. We suggest that these mechanisms are crucial to assess how individuals cope with uncertainty during a pandemic, and we show the interest of these new perspectives for public health policies.