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Efficient sentinel surveillance strategies for preventing epidemics on networks
- Source :
- PLoS Computational Biology, PLoS Computational Biology, Vol 15, Iss 11, p e1007517 (2019)
- Publication Year :
- 2019
-
Abstract
- Surveillance plays a crucial role in preventing emerging infectious diseases from becoming epidemic. In circumstances where it is possible to monitor the infection status of certain people, transport hubs, or hospitals, early detection of the disease allows interventions to be implemented before most of the damage can occur, or at least its impact can be mitigated. This paper addresses the question of which nodes we should select in a network of individuals susceptible to some infectious disease in order to minimize the number of casualties. By simulating disease outbreaks on a collection of empirical and synthetic networks we show that the best strategy depends on topological characteristics of the network. For highly modular or spatially embedded networks it is better to place the sentinels on nodes distributed across different regions. However, if the degree heterogeneity is high, then a strategy that targets network hubs is preferred. We further consider the consequences of having an incomplete sample of the network and demonstrate that the value of new information diminishes as more data is collected. Finally we find further marginal improvements using two heuristics informed by known results in graph theory that exploit the fragmented structure of sparse network data.<br />Author summary In a network of individuals susceptible to some infectious disease, what are the best locations to monitor in order to detect the infection before most damage can be done? In this paper we address this question by considering various heuristic strategies for sentinel placement that can potentially be implemented in real-world situations without requiring excessive amounts of computation, or even having perfect data about the structure of the network. We find that strategies that attempt to distribute sentinels over different regions of the network perform best in highly modular or spatially embedded networks, whereas the strategy of targeting the most well connected individuals works best when there is a considerable amount of contact heterogeneity between individuals. Our results may be used as a guideline to help decide when certain strategies should, or should not, be implemented.
- Subjects :
- 0301 basic medicine
Computer science
Epidemiology
Disease
Infographics
Disease Outbreaks
0302 clinical medicine
Medicine and Health Sciences
Public and Occupational Health
Biology (General)
Ecology
Vaccination and Immunization
Infectious Diseases
Computational Theory and Mathematics
Risk analysis (engineering)
Oncology
Modeling and Simulation
Physical Sciences
Disease Susceptibility
Graphs
Network Analysis
Network analysis
Research Article
Computer and Information Sciences
Exploit
Infectious Disease Control
QH301-705.5
Lymphatic Mapping
Immunology
Disease Surveillance
Communicable Diseases
Spatial Networks
03 medical and health sciences
Cellular and Molecular Neuroscience
Diagnostic Medicine
Genetics
Cancer Detection and Diagnosis
Humans
Computer Simulation
Epidemics
Molecular Biology
Ecology, Evolution, Behavior and Systematics
business.industry
Data Visualization
Outbreak
Biology and Life Sciences
Graph theory
Modular design
Models, Theoretical
030104 developmental biology
Infectious disease (medical specialty)
Infectious Disease Surveillance
Graph Theory
Preventive Medicine
Heuristics
business
Sentinel Surveillance
030217 neurology & neurosurgery
Mathematics
Subjects
Details
- Language :
- English
- Volume :
- 15
- Issue :
- No. 11
- Database :
- OpenAIRE
- Journal :
- PLOS Computational Biology
- Accession number :
- edsair.doi.dedup.....9cd9270dfdf6546b182d254bd6cdc2fc