  The ongoing COVID-19 pandemic calls for a multi-faceted public health
response comprising complementary interventions to control the spread of the
disease while vaccines and therapies are developed. Many of these interventions
need to be informed by epidemic risk predictions given available data,
including symptoms, contact patterns, and environmental factors. Here we
propose a novel probabilistic formalism based on Individual-Level Models (ILMs)
that offers rigorous formulas for the probability of infection of individuals,
which can be parameterised via Maximum Likelihood Estimation (MLE) applied on
compartmental models defined at the population level. We describe an approach
where individual data collected in real-time is integrated with overall case
counts to update the a predictor of the susceptibility of infection as a
function of individual risk factors.
