From Global to Local: Spatial Interaction and Infectious Diseases Dispersion

1. 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)

2. 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

3. Motivation • Worry about Avian Flu because • Human contraction (more likely in Asia) • Where?

5. Dispersion (around sources)

6. Population Density U.S

7. 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

8. Spreading of Avian Flu ?

9. Human-bird Interaction in N.A.

10. Spreading of Avian Flu ?

11. 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

13. 1990 Passenger Trips

14. 1995 Passenger Trips

15. 2000 Passenger Trips

16. 2005 Passenger Trips

18. 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)

19. 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

20. 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)

21. 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)

22. Targeting Cities and Transportation Routes (Dibble and Feldman (2004) jasss.soc.surrey.ac.uk/7/1/7.html)

23. Population by Counties

24. Density by Counties

25. Environmental Conditions(Local “Preventive Measures”)

26. Spitting in …

27. (AP) Avril Lavigne has agreed that spitting at the paparazzi is probably not a good thing to do

28. Sociology of Spitting & … • American Habit?

29. Personal Hygiene

31. Public Health Awareness

32. 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