a School of Science, Beijing University of Civil Engineering and Architecture , Beijing , People's Republic of China.
b Department of Mathematics, Purdue University , West Lafayette , IN , USA.
J Biol Dyn. 2019;13(sup1):31-46. doi: 10.1080/17513758.2018.1484186. Epub 2018 Jun 18.
In meta-population models for infectious diseases, the basic reproduction number can be as much as 70% larger in the case of preferential mixing than that in homogeneous mixing [J.W. Glasser, Z. Feng, S.B. Omer, P.J. Smith, and L.E. Rodewald, , Lancet ID 16 (2016), pp. 599-605. doi: 10.1016/S1473-3099(16)00004-9 ]. This suggests that realistic mixing can be an important factor to consider in order for the models to provide a reliable assessment of intervention strategies. The influence of mixing is more significant when the population is highly heterogeneous. In this paper, another quantity, the final epidemic size ( ) of an outbreak, is considered to examine the influence of mixing and population heterogeneity. Final size relation is derived for a meta-population model accounting for a general mixing. The results show that can be influenced by the pattern of mixing in a significant way. Another interesting finding is that, heterogeneity in various sub-population characteristics may have the opposite effect on and .
在传染病的元种群模型中，与均匀混合相比，优先混合情况下的基本繁殖数 可以大 70%[J.W. Glasser、Z. Feng、S.B. Omer、P.J. Smith 和 L.E. Rodewald，, Lancet ID 16 (2016)，第 599-605 页。doi: 10.1016/S1473-3099(16)00004-9]。这表明，为了使模型能够对干预策略进行可靠评估，实际混合可以成为一个重要的考虑因素。当人群高度异质时，混合的影响更为显著。在本文中，我们还考虑了另一个量，即疫情的最终规模（ ），以检验混合和人口异质性的影响。针对考虑一般混合的元种群模型，我们推导出了最终规模关系。结果表明， 可以受到混合模式的显著影响。另一个有趣的发现是，不同亚种群特征的异质性可能对 和 产生相反的影响。
