Vaccination against 2009 pandemic H1N1 in a population dynamical model of Vancouver, Canada: timing is everything
1 Division of Mathematical Modeling, University of British Columbia Centre for Disease Control, 655 West 12th Avenue, V5Z 4R4 Vancouver, British Columbia, Canada
2 Department of Mathematics, University of British Columbia, Vancouver, British Columbia, Canada
3 Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
4 Departments of Public Health and Medicine, Weill Medical College of Cornell University, New York, NY, USA
5 New York-Presbyterian Hospital, New York, NY, USA
6 Department of Mathematics and Statistics, University of Victoria, Victoria, British Columbia, Canada
7 Section of Integrative Biology, The University of Texas at Austin, Austin, TX, USA
8 Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
9 Centre for Communicable Diseases and Infection Control, Public Health Agency of Canada, Toronto, Ontario, Canada
10 Epidemiology Services, British Columbia Centre for Disease Control, Vancouver, British Columbia, Canada
11 Surveillance Lab, Department of Epidemiology and Biostatistics, McGill University, Montreal, Québec, Canada
12 Bureau de surveillance épidémiologique, Direction de santé publique de Montréal, Montréal, Québec, Canada
13 Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
14 Centre for Disease Modelling, York University, Toronto, Ontario, Canada
15 School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, Canada
BMC Public Health 2011, 11:932 doi:10.1186/1471-2458-11-932Published: 14 December 2011
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.
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.
The model output was consistent with provincial surveillance data. Assuming a basic reproduction number = 1.4, an 8-week vaccination campaign initiated 2 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.
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.