  We consider the dynamic infection spread model that is based on the discrete
SIR model which assumes infections to be spread over time via infected and
non-isolated individuals. In our system, the main objective is not to minimize
the number of required tests to identify every infection, but instead, to
utilize the available, given testing capacity $T$ at each time instance to
efficiently control the infection spread. We introduce and study a novel
performance metric, which we coin as $\epsilon$-disease control time. This
metric can be used to measure how fast a given algorithm can control the spread
of a disease. We characterize the performance of dynamic individual testing
algorithm and introduce a novel dynamic SAFFRON based group testing algorithm.
We present theoretical results and implement the proposed algorithms to compare
their performances.
