  Under limited available resources, strategies for mitigating the propagation
of an epidemic such as random testing and contact tracing become inefficient.
Here, we propose to accurately allocate the resources by computing over time an
individual risk of infection based on the partial observation of the epidemic
spreading on a contact network; this risk is defined as the probability of
getting infected from any possible transmission chain up to length two,
originating from recently detected individuals. To evaluate the performance of
our method and the effects of some key parameters, we perform comparative
simulated experiments using data generated by an agent-based model.
