Dynamic Curing and Network Design in SIS Epidemic Processes

TL;DR

This paper develops efficient algorithms for dynamic curing and network design to rapidly eliminate SIS epidemic spread, with approximation guarantees and fairness considerations, applicable to large networks.

Contribution

It introduces computationally efficient algorithms for dynamic curing and network design in SIS models, with approximation guarantees and fairness considerations.

Findings

Proposed a curing algorithm with approximation guarantees.

Developed network design algorithms to reduce infection rates.

Algorithms are effective and computationally efficient for large networks.

Abstract

This paper studies efficient algorithms for dynamic curing policies and the corresponding network design problems to guarantee the fast extinction of epidemic spread in a susceptible-infected-susceptible (SIS) model. We consider a Markov process-based SIS epidemic model. We provide a computationally efficient curing algorithm based on the curing policy proposed by Drakopoulos, Ozdaglar, and Tsitsiklis (2014). Since the corresponding optimization problem is NP-hard, finding optimal policies is intractable for large graphs. We provide approximation guarantees on the curing budget of the proposed dynamic curing algorithm. We also present a curing algorithm fair to demographic groups. When the total infection rate is high, the original curing policy includes a waiting period in which no measure is taken to mitigate the spread until the rate slows down. To avoid the waiting period, we study network design problems to reduce the total infection rate by deleting edges or reducing the weight of edges. Then the curing processes become continuous since the total infection rate is restricted by network design. We provide algorithms with provable guarantees for the considered network design problems. In summary, the proposed curing and network design algorithms together provide an effective and computationally efficient approach that mitigates SIS epidemic spread in networks.