  Recently, hypergraphs have attracted considerable interest from the research
community as a generalization of networks capable of encoding higher-order
interactions, which commonly appear in both natural and social systems.
Epidemic dynamics in hypergraphs has been studied by using the simplicial
susceptible-infected-susceptible ($s$-SIS) model; however, the efficient
immunization strategy for epidemics in hypergraphs is not studied despite the
importance of the topic in mathematical epidemiology. Here, we propose an
immunization strategy that immunizes hyperedges with high simultaneous
infection probability (SIP). This strategy can be implemented in general
hypergraphs. We also generalize the edge epidemic importance (EI)-based
immunization strategy, which is the state of the art in complex networks.
However, it does not perform as well as the SIP-based method in hypergraphs
despite its high computational cost. We also show that immunizing hyperedges
with high H-eigenscore effectively contains the epidemics in uniform
hypergraphs. A high SIP of a hyperedge suggests that the hyperedge is a
"hotspot" of the epidemic process. Therefore, SIP can be used as a centrality
measure to quantify a hyperedge's influence on higher-order dynamics in general
hypergraphs. The effectiveness of the immunization strategies suggests the
necessity of scientific, data-driven, systematic policy-making for epidemic
containment.
