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. 2010 Dec 21;10:778.
doi: 10.1186/1471-2458-10-778.

The impact of mass gatherings and holiday traveling on the course of an influenza pandemic: a computational model

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Free PMC article

The impact of mass gatherings and holiday traveling on the course of an influenza pandemic: a computational model

Pengyi Shi et al. BMC Public Health. .
Free PMC article

Abstract

Background: During the 2009 H1N1 influenza pandemic, concerns arose about the potential negative effects of mass public gatherings and travel on the course of the pandemic. Better understanding the potential effects of temporal changes in social mixing patterns could help public officials determine if and when to cancel large public gatherings or enforce regional travel restrictions, advisories, or surveillance during an epidemic.

Methods: We develop a computer simulation model using detailed data from the state of Georgia to explore how various changes in social mixing and contact patterns, representing mass gatherings and holiday traveling, may affect the course of an influenza pandemic. Various scenarios with different combinations of the length of the mass gatherings or traveling period (range: 0.5 to 5 days), the proportion of the population attending the mass gathering events or on travel (range: 1% to 50%), and the initial reproduction numbers R0 (1.3, 1.5, 1.8) are explored.

Results: Mass gatherings that occur within 10 days before the epidemic peak can result in as high as a 10% relative increase in the peak prevalence and the total attack rate, and may have even worse impacts on local communities and travelers' families. Holiday traveling can lead to a second epidemic peak under certain scenarios. Conversely, mass traveling or gatherings may have little effect when occurring much earlier or later than the epidemic peak, e.g., more than 40 days earlier or 20 days later than the peak when the initial R0 = 1.5.

Conclusions: Our results suggest that monitoring, postponing, or cancelling large public gatherings may be warranted close to the epidemic peak but not earlier or later during the epidemic. Influenza activity should also be closely monitored for a potential second peak if holiday traveling occurs when prevalence is high.

Figures

Figure 1
An example of the contact network. The figure shows an example of the contact network, i.e., how persons interact with each other in households, workplaces, schools, communities, and/or temporary mass gathering locations.
Figure 2
Epidemic curves in the Holiday scenarios. The figure shows the daily prevalence of infection (i.e., proportion of the symptomatic and asymptomatic persons over the entire population) for the entire state of Georgia under the Holiday setting. Here 25% of the population travels during a 5-day traveling or mass gathering period with two initial R0 values: A) R0 = 1.5; B) R0 = 1.3.
Figure 3
Epidemic curves in the Holiday and social distancing scenarios. The figure shows the daily prevalence of infection (i.e., proportion of the symptomatic and asymptomatic persons over the entire population) for the entire state of Georgia under the Holiday and the social distancing settings. Here the initial R0 = 1.5; 25% of the population travels during a 5-day period starting on Day 60 (solid curve), or reduces their peer group mixings ("social distancing") during the same period time (dotted curve).
Figure 4
Peak prevalence value and peak day in Bibb County. The box plots show the range (with maximum and minimum, dotted line), 25% (lower gray line) and 75% (upper gray line) percentile, and the mean value (solid black line) for the peak prevalence (4A) and the peak day (4B) in Bibb County. Here 50% of the population from Bibb County and its nearest 5 counties travels and mixes with 9.5% of the population from other counties in the traveling group. The initial R0 = 1.5, the traveling period lasts for 1 day, and it starts on Day 30, 60, or no traveling (baseline).

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References

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