Sampling for Global Epidemic Models and the Topology of an International Airport Network
Author Information
Author(s): Georgiy Bobashev, Robert J. Morris, D. Michael Goedecke
Primary Institution: RTI International, Research Triangle Park, North Carolina, United States of America
Hypothesis
How does sampling affect the main network characteristics of the entire network?
Conclusion
A relatively small number of cities (around 200 or 300) can capture enough network information to adequately describe the global spread of a disease such as influenza.
Supporting Evidence
- A small sample size can produce biased network characteristics.
- Volume-based samples tend to better describe sample characteristics but may lack regional coverage.
- First passage times for disease spread can vary significantly based on sample size and selection method.
- Models based on a small number of cities may underestimate the role of individual cities in disease spread.
Takeaway
The study shows that we don't need to look at every airport to understand how diseases spread globally; just a few key cities can give us a good idea.
Methodology
The study analyzed airport flight data to examine network characteristics under different sampling rules.
Potential Biases
Using too few cities can lead to missing important pathways for disease spread.
Limitations
The analysis is based on the airline network, which may limit generalizability to other types of networks.
Digital Object Identifier (DOI)
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