Back to Search
Start Over
Targeted pandemic containment through identifying local contact network bottlenecks
- Source :
- PLoS Computational Biology, PLoS Computational Biology, Vol 17, Iss 8, p e1009351 (2021)
- Publication Year :
- 2021
- Publisher :
- Public Library of Science, 2021.
-
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.<br />Comment: 38 pages, 21 figures
- 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
Subjects
Details
- Language :
- English
- ISSN :
- 15537358 and 1553734X
- Volume :
- 17
- Issue :
- 8
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....9f36548a515d6d61aa57dd0468c88270