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

Test allocation based on risk of infection from first and second order contact tracing