Shweta Bansal1, Babak Pourbohloul, Nathaniel Hupert, Bryan Grenfell, Lauren Ancel Meyers. 1. Center for Infectious Disease Dynamics, Penn State University; Division of Mathematical Modeling, British Columbia Centre for Disease Control; Weill Cornell Medical College (NYC) and Preparedness Modeling Unit, U.S. Centers for Disease Control and Prevention (CDC, Atlanta); Princeton University and The University of Texas at Austin.
Abstract
BACKGROUND: As Pandemic (H1N1) 2009 influenza spreads around the globe, it strikes school-age children more often than adults. Although there is some evidence of pre-existing immunity among older adults, this alone may not explain the significant gap in age-specific infection rates. METHODS & FINDINGS: Based on a retrospective analysis of pandemic strains of influenza from the last century, we show that school-age children typically experience the highest attack rates in primarily naive populations, with the burden shifting to adults during the subsequent season. Using a parsimonious network-based mathematical model which incorporates the changing distribution of contacts in the susceptible population, we demonstrate that new pandemic strains of influenza are expected to shift the epidemiological landscape in exactly this way. CONCLUSIONS: Our results provide a simple demographic explanation for the age bias observed for H1N1/09 attack rates, and a prediction that this bias will shift in coming months. These results also have significant implications for the allocation of public health resources including vaccine distribution policies.
BACKGROUND: As Pandemic (H1N1) 2009 influenza spreads around the globe, it strikes school-age children more often than adults. Although there is some evidence of pre-existing immunity among older adults, this alone may not explain the significant gap in age-specific infection rates. METHODS & FINDINGS: Based on a retrospective analysis of pandemic strains of influenza from the last century, we show that school-age children typically experience the highest attack rates in primarily naive populations, with the burden shifting to adults during the subsequent season. Using a parsimonious network-based mathematical model which incorporates the changing distribution of contacts in the susceptible population, we demonstrate that new pandemic strains of influenza are expected to shift the epidemiological landscape in exactly this way. CONCLUSIONS: Our results provide a simple demographic explanation for the age bias observed for H1N1/09 attack rates, and a prediction that this bias will shift in coming months. These results also have significant implications for the allocation of public health resources including vaccine distribution policies.
Authors: M E Woolhouse; C Dye; J F Etard; T Smith; J D Charlwood; G P Garnett; P Hagan; J L Hii; P D Ndhlovu; R J Quinnell; C H Watts; S K Chandiwana; R M Anderson Journal: Proc Natl Acad Sci U S A Date: 1997-01-07 Impact factor: 11.205
Authors: Raymond Gani; Helen Hughes; Douglas Fleming; Thomas Griffin; Jolyon Medlock; Steve Leach Journal: Emerg Infect Dis Date: 2005-09 Impact factor: 6.883