tetano
Editor, Senior Moderator
Vaccination against 2009 pandemic H1N1 in a population dynamical model of Vancouver, Canada: timing is everything
Jessica M Conway, Ashleigh R Tuite, David N Fisman, Nathaniel Hupert, Rafael Meza, Bahman Davoudi, Krista English, P van den Driessche, Fred Brauer, Junling Ma, Lauren Ancel Meyers, Marek Smieja, Amy Greer, Danuta M Skowronski, David L Buckeridge, Jeffrey C Kwong, Jianhong Wu, Seyed M Moghadas, Daniel Coombs, Robert C Brunham and Babak Pourbohloul
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BMC Public Health 2011, 11:932 doi:10.1186/1471-2458-11-932
Published: 14 December 2011
Abstract (provisional)
Background
Much remains unknown about the effect of timing and prioritization of vaccination against pandemic (pH1N1) 2009 virus on health outcomes. We adapted a city-level contact network model to study different campaigns on influenza morbidity and mortality.
Methods
We modeled different distribution strategies initiated between July and November 2009 using a compartmental epidemic model that includes age structure and transmission network dynamics. The model represents the Greater Vancouver Regional District, a major North American city and surrounding suburbs with a population of 2 million, and is parameterized using data from the British Columbia Ministry of Health, published studies, and expert opinion. Outcomes are expressed as the number of infections and deaths averted due to vaccination.
Results
The model output was consistent with provincial surveillance data. Assuming a basic reproduction number = 1.4, an 8-week vaccination campaign initiated two weeks before the epidemic onset reduced morbidity and mortality by 79-91% and 80-87%, respectively, compared to no vaccination. Prioritizing children and parents for vaccination may have reduced transmission compared to actual practice, but the mortality benefit of this strategy appears highly sensitive to campaign timing. Modeling the actual late October start date resulted in modest reductions in morbidity and mortality (13-25% and 16-20%, respectively) with little variation by prioritization scheme.
Conclusion
Delays in vaccine production due to technological or logistical barriers may reduce potential benefits of vaccination for pandemic influenza, and these temporal effects can outweigh any additional theoretical benefits from population targeting. Careful modeling may provide decision makers with estimates of these effects before the epidemic peak to guide production goals and inform policy. Integration of real-time surveillance data with mathematical models holds the promise of enabling public health planners to optimize the community benefits from proposed interventions before the pandemic peak.
http://www.biomedcentral.com/content/pdf/1471-2458-11-932.pdf
Jessica M Conway, Ashleigh R Tuite, David N Fisman, Nathaniel Hupert, Rafael Meza, Bahman Davoudi, Krista English, P van den Driessche, Fred Brauer, Junling Ma, Lauren Ancel Meyers, Marek Smieja, Amy Greer, Danuta M Skowronski, David L Buckeridge, Jeffrey C Kwong, Jianhong Wu, Seyed M Moghadas, Daniel Coombs, Robert C Brunham and Babak Pourbohloul
For all author emails, please log on.
BMC Public Health 2011, 11:932 doi:10.1186/1471-2458-11-932
Published: 14 December 2011
Abstract (provisional)
Background
Much remains unknown about the effect of timing and prioritization of vaccination against pandemic (pH1N1) 2009 virus on health outcomes. We adapted a city-level contact network model to study different campaigns on influenza morbidity and mortality.
Methods
We modeled different distribution strategies initiated between July and November 2009 using a compartmental epidemic model that includes age structure and transmission network dynamics. The model represents the Greater Vancouver Regional District, a major North American city and surrounding suburbs with a population of 2 million, and is parameterized using data from the British Columbia Ministry of Health, published studies, and expert opinion. Outcomes are expressed as the number of infections and deaths averted due to vaccination.
Results
The model output was consistent with provincial surveillance data. Assuming a basic reproduction number = 1.4, an 8-week vaccination campaign initiated two weeks before the epidemic onset reduced morbidity and mortality by 79-91% and 80-87%, respectively, compared to no vaccination. Prioritizing children and parents for vaccination may have reduced transmission compared to actual practice, but the mortality benefit of this strategy appears highly sensitive to campaign timing. Modeling the actual late October start date resulted in modest reductions in morbidity and mortality (13-25% and 16-20%, respectively) with little variation by prioritization scheme.
Conclusion
Delays in vaccine production due to technological or logistical barriers may reduce potential benefits of vaccination for pandemic influenza, and these temporal effects can outweigh any additional theoretical benefits from population targeting. Careful modeling may provide decision makers with estimates of these effects before the epidemic peak to guide production goals and inform policy. Integration of real-time surveillance data with mathematical models holds the promise of enabling public health planners to optimize the community benefits from proposed interventions before the pandemic peak.
http://www.biomedcentral.com/content/pdf/1471-2458-11-932.pdf