Agent-Based Modelling Laboratory, York University, Toronto, Ontario, Canada.
Prev Med. 2013 Dec;57(6):910-3. doi: 10.1016/j.ypmed.2013.04.011. Epub 2013 Apr 28.
To evaluate the impact of age-specific cross-reactive antibody protection levels on the outcomes of a pandemic outbreak of new variants of H3N2 influenza A viruses (H3N2v).
We calibrated a previously validated agent-based model of human-to-human transmission of influenza viruses to project the outcomes of various protection levels in a remote and isolated Canadian community, when demographics are drawn from the Statistics Canada census data. We then compared the outcomes with a scenario in which demographic variables were shifted to resemble an urban structure. This comparative evaluation was conducted using in-silico computer simulations, where the epidemiological data were drawn from relevant estimates in published literature.
Simulations, using estimates of transmissibility for the 2009 H1N1 pandemic strain in the study population, show that the epidemic size is primarily affected by the cross-reactive protection levels of young children. A lower number of secondary infections at the early stages of an outbreak does not necessarily correspond to a lower epidemic size.
Demographic variables could play a significant role in determining the outcomes of an outbreak. The findings strongly suggest that, when an H3N2v-specific vaccine becomes available, children below the age of 17 should be prioritized for vaccination. This prioritization is essential in population settings with a low average age, including aboriginal communities in northern latitudes.
评估针对新型 H3N2 甲型流感病毒(H3N2v)的变异株的特定年龄交叉反应性抗体保护水平对大流行爆发结果的影响。
我们校准了一个先前验证的基于代理的人类传播流感病毒的模型,以预测在偏远和孤立的加拿大社区中各种保护水平的结果,该社区的人口统计学数据取自加拿大统计局的人口普查数据。然后,我们将结果与一个人口统计学变量类似于城市结构的情景进行了比较。这种比较评估是通过使用计算机模拟进行的,其中流行病学数据取自已发表文献中的相关估计值。
使用研究人群中 2009 年 H1N1 大流行株的传染性估计值进行的模拟表明,流行规模主要受年轻儿童的交叉反应性保护水平影响。爆发早期的继发感染数量较少并不一定对应于较小的流行规模。
人口统计学变量可能在确定爆发结果方面发挥重要作用。研究结果强烈表明,当出现 H3N2v 特异性疫苗时,应优先为 17 岁以下的儿童接种疫苗。在平均年龄较低的人群中,包括北方的土著社区,这种优先接种疫苗至关重要。