Sampling for global epidemic models and the topology of an international airport network.
Mathematical models that describe the global spread of infectious diseases such as influenza, severe acute respiratory syndrome (SARS), and tuberculosis (TB) often consider a sample of international airports as a network supporting disease spread. However, there is no consensus on how many cities should be selected or on how to select those cities. Using airport flight data that commercial airlines reported to the Official Airline Guide (OAG) in 2000, we have examined the network characteristics of network samples obtained under different selection rules. In addition, we have examined different size samples based on largest flight volume and largest metropolitan populations. We have shown that although the bias in network characteristics increases with the reduction of the sample size, a relatively small number of areas that includes the largest airports, the largest cities, the most-connected cities, and the most central cities is enough to describe the dynamics of the global spread of influenza. The analysis suggests that a relatively small number of cities (around 200 or 300 out of almost 3000) can capture enough network information to adequately describe the global spread of a disease such as influenza. Weak traffic flows between small airports can contribute to noise and mask other means of spread such as the ground transportation.
Bobashev
Georgiy
G
RTI International, Research Triangle Park, North Carolina, United States of America. bobashev@rti.org
Morris
Robert J
RJ
Goedecke
D Michael
DM
D000401:Aircraft; D001359:Aviation; D003141:Communicable Diseases / Q000453:epidemiology* / Q000635:transmission; D004196:Disease Outbreaks; D005843:Geography; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology / Q000635:transmission; D008962:Models, Theoretical; D045169:Severe Acute Respiratory Syndrome / Q000635:transmission; D014195:Travel; D014376:Tuberculosis / Q000453:epidemiology / Q000635:transmission
Eur J Epidemiol. 2003;18(11):1065-72
14620941
Phys Rev E Stat Nonlin Soft Matter Phys. 2007 Apr;75(4 Pt 2):046114
17500968
Phys Rev E Stat Nonlin Soft Matter Phys. 2006 Jan;73(1 Pt 2):016102
16486211
Proc Natl Acad Sci U S A. 2005 Mar 22;102(12):4221-4
15767579
Nature. 2001 Jun 21;411(6840):907-8
11418846
Phys Rev E Stat Nonlin Soft Matter Phys. 2005 Sep;72(3 Pt 2):036118
16241526
PLoS One. 2007 May 02;2(5):e401
17476323
Stat Med. 1998 Sep 30;17(18):2079-97
9789915
PLoS Med. 2006 Jun;3(6):e212
16640458
Phys Rev Lett. 2001 Apr 2;86(14):3200-3
11290142
Microbes Infect. 2003 Feb;5(2):189-96
12650777
Proc Natl Acad Sci U S A. 2004 Oct 19;101(42):15124-9
15477600
Proc Natl Acad Sci U S A. 2005 May 31;102(22):7794-9
15911778
PLoS One. 2008 Jan 23;3(1):e1478
18213386
Nat Med. 2006 May;12(5):497-9
16675989
Proc Natl Acad Sci U S A. 2006 Feb 14;103(7):2015-20
16461461