Abstract
Epidemic spreading is well understood when a disease propagates around a contact graph. In a stochastic susceptible–infected–susceptible setting, spectral conditions characterize whether the disease vanishes. However, modelling human interactions using a graph is a simplification which only considers pairwise relationships. This does not fully represent the more realistic case where people meet in groups. Hyperedges can be used to record higher order interactions, yielding more faithful and flexible models and allowing for the rate of infection of a node to depend on group size and also to vary as a nonlinear function of the number of infectious neighbours. We discuss different types of contagion models in this hypergraph setting and derive spectral conditions that characterize whether the disease vanishes. We study both the exact individual-level stochastic model and a deterministic mean field ODE approximation. Numerical simulations are provided to illustrate the analysis. We also interpret our results and show how the hypergraph model allows us to distinguish between contributions to infectiousness that (i) are inherent in the nature of the pathogen and (ii) arise from behavioural choices (such as social distancing, increased hygiene and use of masks). This raises the possibility of more accurately quantifying the effect of interventions that are designed to contain the spread of a virus.
Highlights
Compartmental models for disease propagation have a long and illustrious history [1,2], and they remain a2021 The Authors
A key advantage of the mean field approximation is that it gives rise to a deterministic autonomous dynamical system for which there exists a rich theory to study the asymptotic stability of equilibrium points
We provide below spectral conditions which imply that the infection-free solution 0 ∈ Rn is a locally or globally asymptotically stable equilibrium of (4.1) and (4.2)
Summary
Compartmental models for disease propagation have a long and illustrious history [1,2], and they remain a. The authors in [5,6] consider a collective contagion model, where infection spreads within a hyperdge only if a certain threshold of infectious vertices is reached in that hyperedge. A collective contagion model may be represented, for example, via the functions f1 : x → x and fk : x → c2,k1(x ≥ c1,k), k ∈ {2, . . . , K}
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