from global to local spatial interaction and infectious diseases dispersion
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From Global to Local: Spatial Interaction and Infectious Diseases Dispersion. David Wong Professor of Geography Chair Earth Systems & GeoInformation Sciences With contributions from Dr. Catherine Dibble (U. Maryland-College Park) Karen Owen (GMU – Graduate Student)

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from global to local spatial interaction and infectious diseases dispersion
From Global to Local: Spatial Interaction and Infectious Diseases Dispersion

David Wong

Professor of Geography

Chair

Earth Systems & GeoInformation Sciences

With contributions from

Dr. Catherine Dibble (U. Maryland-College Park)

Karen Owen (GMU – Graduate Student)

Min Li (GMU – Graduate Student)

motivation
Motivation
  • Theme of the Conference: Avian Flu
  • This presentation - broader
  • Spreading of Infectious Diseases from human to human through direct or indirect contacts
  • Worry about Avian Flu because
motivation1
Motivation
  • Worry about Avian Flu because
    • Human contraction (more likely in Asia)
    • Where?
population density1
Population Density

Population Density – interaction - risk

Selected Places:

  • Hong Kong: 6660/sq km
  • Macau: 16068
  • Taiwan: 714
  • Canada: 3.64
  • U.S.: 32.6
  • NY metro: 1702/sq ml = 665/sq km
  • Manhattan: 66940 = 26148/sq km
international migration
International Migration
  • Geographical Literature on Migration:
    • Gravity Models or Spatial Interaction Models
  • Iij – interaction between locations i and j
  • dij – distance between I and j
  • β – distance decay parameter
    • How much interaction will respond to a change in distance?
  • Pi, Pj – characteristics of origin i and destination j
  • K – scaling factor
  • Migration? or Mobility
first 3 weeks of 1918 flu pandemic
First 3 Weeks of 1918 Flu Pandemic
  • Arrived via ports
    • Boston
    • New York
  • Spread to key cities
  • Spread beyond cities
  • Early warning helps
    • Difficult in 1918
    • Almost real-time now

(Crosby, Alfred (1989) America’s Forgotten Pandemic: The Influenza of 1918)

local interaction dispersion
Local Interaction/Dispersion

Domestic Air Travel

  • 1980: 249,158,581 / 226M = 1.09
  • 1990: 428,769,370 / 248M = 1.72
  • 2000: 616,379,536 / 281M = 2.19
  • 2005: 674,025,059 / 295M = 2.28
midas university of maryland geograph epidemic models
MIDAS University of MarylandGeoGraph Epidemic Models
  • Colored bars are cities
  • People travel between cities, spreading the disease
  • Bar charts show health status:
    • Green – healthy
    • Pink – infected
    • Red – infectious
    • White – recovered (immune)
    • Gray – dead
  • Links are roads and airline routes

(Dibble and Feldman (2004) jasss.soc.surrey.ac.uk/7/1/7.html)

university of maryland midas research
University of Maryland MIDAS Research
  • Which cities are at greatest risk?
  • How should limited resources for interventions be allocated geographically in order to protect the most people?
  • Which airline flights, train routes, or highways should be monitored or blocked to reduce risks?

(Dibble and Feldman (2004) jasss.soc.surrey.ac.uk/7/1/7.html)

targeting cities and transportation routes
Targeting Cities and Transportation Routes

(Dibble and Feldman (2004) jasss.soc.surrey.ac.uk/7/1/7.html)

ap avril lavigne has agreed that spitting at the paparazzi is probably not a good thing to do
(AP) Avril Lavigne has agreed that spitting at the paparazzi is probably not a good thing to do
conclusion
Conclusion
  • If gov’t has to take action – already epidemics
  • Methods to control epidemics
  • “Common sense” personal hygiene practice
  • Higher level of consciousness (education) on spreading of diseases – but not be obsessed or phobia
  • Smarter design of structure may help
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