Abstract
Decision-making about pandemic mitigation often relies upon simulation modelling. Models of disease transmission through networks of contacts-between individuals or between population centres-are increasingly used for these purposes. Real-world contact networks are rich in structural features that influence infection transmission, such as tightly-knit local communities that are weakly connected to one another. In this paper, we propose a new flow-based edge-betweenness centrality method for detecting bottleneck edges that connect nodes in contact networks. In particular, we utilize convex optimization formulations based on the idea of diffusion with p-norm network flow. Using simulation models of COVID-19 transmission through real network data at both individual and county levels, we demonstrate that targeting bottleneck edges identified by the proposed method reduces the number of infected cases by up to 10% more than state-of-the-art edge-betweenness methods. Furthermore, the proposed method is orders of magnitude faster than existing methods.
Highlights
Mathematical and computer simulation models of COVID-19 transmission are being widely used during the COVID-19 pandemic for their ability to project future cases of infection under various possible scenarios for mitigation strategies [1,2,3,4]
Because neither high degree centralities (HD) nor eigenvector centralities (EG) naturally applies to quantify edge importance, our simulation studies reveal that HD and EG are not suitable for edge interventions that we consider in this work
In the context of pandemic mitigation, on the one hand, LF betweenness can be straightforwardly used to identify good targets for static intervention strategies similar to what we considered ; on the other hand, it can be incorporated into more complex and dynamic intervention methods, for example, sequentially remove nodes or edges similar to [43], or continuously adjust the percentage of edge weight reduction depending on the resulting LF betweenness measure of weighted networks
Summary
Mathematical and computer simulation models of COVID-19 transmission are being widely used during the COVID-19 pandemic for their ability to project future cases of infection under various possible scenarios for mitigation strategies [1,2,3,4]. The nodes of the network represent individuals or population centres, and the edges represent contacts through which SARS-CoV-2 (the virus that causes COVID-19) can spread. These models are often parameterized with data on demographic features, COVID-19 epidemiology, and population movement patterns [8, 9]. Network models are relevant to COVID-19 control through physical distancing measures. These measures are effective but socially and economically costly. Physical distancing that targets the smallest number of nodes or edges of a contact network required to achieve public health goals is desirable
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