1. Targeted pandemic containment through identifying local contact network bottlenecks
- Author
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Chris T. Bauch, Di Wang, Shenghao Yang, Priyabrata Senapati, and Kimon Fountoulakis
- Subjects
FOS: Computer and information sciences ,Viral Diseases ,Facebook ,Computer science ,Epidemiology ,Distributed computing ,Social Sciences ,01 natural sciences ,Systems Science ,Oregon ,Medical Conditions ,Sociology ,Agent-Based Modeling ,Medicine and Health Sciences ,Centrality ,Biology (General) ,Computer Networks ,0303 health sciences ,education.field_of_study ,Ecology ,Simulation and Modeling ,Quebec ,Social Communication ,Computer Science - Social and Information Networks ,Infectious Diseases ,Computational Theory and Mathematics ,Social Networks ,Modeling and Simulation ,Convex optimization ,Physical Sciences ,Network Analysis ,Algorithms ,Research Article ,Physics - Physics and Society ,Computer and Information Sciences ,QH301-705.5 ,Population ,FOS: Physical sciences ,Physics and Society (physics.soc-ph) ,Research and Analysis Methods ,Models, Biological ,Bottleneck ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,0103 physical sciences ,Genetics ,Humans ,Computer Simulation ,Quantitative Biology - Populations and Evolution ,010306 general physics ,education ,Molecular Biology ,Pandemics ,Ecology, Evolution, Behavior and Systematics ,030304 developmental biology ,Social and Information Networks (cs.SI) ,Simulation modeling ,Populations and Evolution (q-bio.PE) ,COVID-19 ,Covid 19 ,Flow network ,Communications ,Transmission (telecommunications) ,Flow (mathematics) ,FOS: Biological sciences ,Social Media ,Mathematics - 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., Comment: 38 pages, 21 figures
- Published
- 2021