Extending the range of COVID-19 risk factors ina Bayesian network model for personalised riskassessment

Prodhan, G.

A need is emerging for individuals to gauge their own risks of coronavirus infection as it becomes apparent that contact tracing to contain the spread of the virus is not working in many societies. This paper presents an extension of an existing Bayesian network model for an application in which people can add their own personal risk factors to calculate their probability of exposure to the virus and likely severity if they do catch the illness. The data need not be shared with any central authority. In this way, people can become more aware of their individual risks and adjust their behaviour accordingly, as many countries prepare for a second wave of infections or a prolonged pandemic. This has the advantage not only of preserving privacy but also of containing the virus more effectively by allowing users to act without the time lag of waiting to be informed that a contact has been tested and confirmed COVID-19 positive. Through a nuanced assessment of individual risk, it could also release many people from isolation who are judged highly vulnerable using cruder measures, helping to boost economic activity and decrease social isolation without unduly increasing transmission risk. Although much has been written and reported about single risk factors, little has been done to bring these factors together in a user-friendly way to give an overall risk rating. The causal probabilistic model presented here shows the power of Bayesian networks to represent the interplay of multiple, dependent variables and to predict outcomes. The network, designed for use in the UK, is built using detailed data from government and health authorities and the latest research, and is capable of dynamic updates as new information becomes available. The focus of the paper is on the extended set of risk factors.