TY - JOUR
T1 - Simulation-Optimization Approaches for the Network Immunization Problem with Quarantining
AU - Hoogervorst, Rowan
AU - van der Hurk, Evelien
AU - Pisinger, David
PY - 2025
Y1 - 2025
N2 - 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, as well as the costs of vaccination
campaigns, might 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 algorithm based on
sampling infection forests from the simulation. Second, we propose a genetic
algorithm that is tailored to the immunization problem and 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 the proposed methods are able to 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.
AB - 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, as well as the costs of vaccination
campaigns, might 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 algorithm based on
sampling infection forests from the simulation. Second, we propose a genetic
algorithm that is tailored to the immunization problem and 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 the proposed methods are able to 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.
M3 - Journal article
JO - arXiv physics e-prints
JF - arXiv physics e-prints
ER -