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Volume 13, Number 7—July 2007
Research

Effects of Internal Border Control on Spread of Pandemic Influenza

James G. Wood*†Comments to Author , Nasim Zamani†, C. Raina MacIntyre*†, and Niels G. Becker‡
Author affiliations: *National Centre for Immunisation Research and Surveillance of Vaccine Preventable Diseases, Sydney, New South Wales, Australia; †The University of Sydney, Sydney, New South Wales, Australia; ‡Australian National University, Canberra, Australian Capital Territory, Australia;

Main Article

Table 1

Summary of parameter values, assumptions, and sources used in models of the effect of travel restrictions on pandemic influenza in Australia*

Variable/concept Value (range)/assumption Source/interpretation
Reproduction no. (R0) 1.5–3.5 Mills (14)
Infectivity function (ρ) Flat or peaked† Longini, Ferguson (7,8)
Latent period 1 (1–2 in sensitivity analysis) d(s) Ferguson (6)
Infectious period 5 d Literature suggests 4–7 d in adults (6,7)
Mixing Homogenous (within city) Modeling literature (15)
Propensity to travel Everyone equal Assumption
Populations Sydney (4.2 million), Melbourne 
(3.6 million), Darwin (110,000) ABS figures (16)
Travel rate‡ Sydney ↔ Melbourne (weighted by stay length) (4.7 × 103, 8.9 × 103) BTRE figures (17,18), NSW, and 
Victoria Tourism reports (19,20)
Travel rate‡ Sydney ↔ Darwin 
(weighted by stay length) (9.2 × 104, 4.4 × 103) BTRE figures (17,18), NSW, and 
NT Tourism reports (19,21)
Travel restrictions 20%,10%, or 1% of current levels Assumption
Time between 20 current cases 
in city 1 and city 2 (T20) Random variable (T20), different for 
each simulation. Median value over 
all simulations is given by m20. Output variables used to measure 
effect of travel restrictions

*ABS, Australian Bureau of Statistics; BTRE, Bureau of Transport and Regional Economics; NSW, New South Wales; NT, Northern Territory.
†See Figure 1, panel C, for shapes used.
‡This assumes a constant travel rate over the year with no seasonal variation in travel volumes.

Main Article

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