Simulation-optimization approaches for the network immunization problem with quarantining

Vaccination has played an important role in preventing the spread of infectious diseases. However, the limited availability of vaccines and personnel at the roll-out of a new vaccine and the costs of vaccination campaigns often limit how many people can be vaccinated. Network immunization thus focuses on selecting a fixed-size subset of individuals to vaccinate so as to minimize the disease spread. In this paper, we consider simulation-optimization approaches for this selection problem. Here, the simulation of disease spread in an activity-based contact graph allows us to consider the effect of contact tracing and a limited willingness to test and quarantine. First, we develop a stochastic programming heuristic based on sampling infection forests from the simulation. Second, we propose a genetic algorithm tailored to the immunization problem that combines simulation runs of different sizes to balance the time needed to find promising solutions with the uncertainty resulting from simulation. Both approaches are tested on data from a major university in Denmark and disease characteristics representing those of COVID-19. Our results show that the proposed methods are competitive with a large number of centrality-based measures over a range of disease parameters and that especially the stochastic programming heuristic can outperform them for a considerable number of these instances. Finally, we compare network immunization against our previously proposed approach of limiting distinct contacts. Although, independently, network immunization has a larger impact in reducing disease spread, we show that the combination of both methods reduces the disease spread even further.