Edge Deletion Algorithms for Minimizing Spread in SIR Epidemic Models

This paper studies algorithmic strategies to effectively reduce the number of infections in susceptible-infected-recovered (SIR) epidemic models. We consider a Markov chain SIR model and its two instantiations deterministic (D-SIR) independent cascade (IC-SIR) model. investigate problem minimizing by restricting contacts under realistic constraints. Under moderate assumptions on reproduction number, we prove that infection numbers are bounded supermodular functions D-SIR IC-SIR for large classes random networks. propose efficient algorithms with approximation guarantees minimize infections. The theoretical results illustrated numerical simulations.