Vaidya N K, Morgan M, Jones T, Miller L, Lapin S, Schwartz E J
Department of Mathematics and Statistics,University of Missouri - Kansas City,Kansas City,MO,USA.
Department of Mathematics,School of Biological Sciences,Washington State University,Pullman,WA,USA.
Epidemiol Infect. 2015 Jun;143(8):1610-20. doi: 10.1017/S0950268814002568. Epub 2014 Oct 17.
Knowledge of mechanisms of infection in vulnerable populations is needed in order to prepare for future outbreaks. Here, using a unique dataset collected during a 2009 outbreak of influenza A(H1N1)pdm09 in a university town, we evaluated mechanisms of infection and identified that an epidemiological model containing partial protection of susceptibles best describes H1N1 dynamics in a rural university environment. We found that the protected group was over 14 times less susceptible to H1N1 infection than unprotected susceptibles. Our estimates show that the basic reproductive rate, R 0, was 5·96 (95% confidence interval 5·83-6·61), and, importantly, R 0 could be decreased to below 1 and similar epidemics could be avoided by increasing the proportion of the initial protected group. Moreover, several weeks into the epidemic, this protected group generated more new infections than the unprotected susceptible group, and thus, such protected groups should be taken into account while studying influenza epidemics in similar settings.
为应对未来的疫情爆发，需要了解弱势群体的感染机制。在此，我们利用在一个大学城收集的关于2009年甲型H1N1流感大流行期间的独特数据集，评估了感染机制，并确定一个包含对易感人群部分保护的流行病学模型最能描述农村大学环境中的H1N1动态。我们发现，受保护群体感染H1N1的易感性比未受保护的易感人群低14倍以上。我们的估计表明，基本繁殖数R0为5.96（95%置信区间5.83 - 6.61），重要的是，通过增加初始受保护群体的比例，R0可降至1以下，从而避免类似疫情。此外，在疫情爆发几周后，这个受保护群体产生的新感染病例比未受保护的易感群体更多，因此，在研究类似环境中的流感疫情时应考虑此类受保护群体。
