10/30/22, 5:16 PM L9: Effective Distance: Network Science – CS-7280-O01
L9: Effec ve Distance
Can we use geographical distance to predict the time that an epidemic will arrive at a state or country?
Again, we can use epidemic models such as GLEAM to answer such questions.
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Figure 10.32 from Network Science by Albert-L¨szl¨ Barab¨si
The plot at the left shows the geographic distance between Mexico and many other countries at the x-axis, while the y-axis shows the time that the H1N1 pandemic arrived in that country (defined as the number of days between the first confirmed case in that country and the beginning of the outbreak on March 17, 2009).
Clearly there is not any strong correlation between the two quantities.
Let us now define a different kind of distance, based on mobility data rather than geography:
Suppose that we have data from airlines, trains, busses, trucks, etc, showing how many travelers go from city i to city j.
The fraction of travelers that leave city i and arrive at city j is denoted by . https://gatech. instructure. com/courses/265324/pages/l9-eff ective-distance?module_item_id=2520774
10/30/22, 5:16 PM L9: Effective Distance: Network Science – CS-7280-O01
The effective distance between the two cities is then calculated as .
The plot at the right replaces geographic distance with ¡°effective distance¡±, and it shows that that the arrival day of this pandemic from Mexico was actually quite predictable based on strictly mobility data.
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