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感染发病率的差异迁移和局部变化。

Differential mobility and local variation in infection attack rate.

机构信息

MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom.

Department of Biology, University of Florida, Gainesville, Florida, United States of America.

出版信息

PLoS Comput Biol. 2019 Jan 22;15(1):e1006600. doi: 10.1371/journal.pcbi.1006600. eCollection 2019 Jan.

Abstract

Infectious disease transmission is an inherently spatial process in which a host's home location and their social mixing patterns are important, with the mixing of infectious individuals often different to that of susceptible individuals. Although incidence data for humans have traditionally been aggregated into low-resolution data sets, modern representative surveillance systems such as electronic hospital records generate high volume case data with precise home locations. Here, we use a gridded spatial transmission model of arbitrary resolution to investigate the theoretical relationship between population density, differential population movement and local variability in incidence. We show analytically that a uniform local attack rate is typically only possible for individual pixels in the grid if susceptible and infectious individuals move in the same way. Using a population in Guangdong, China, for which a robust quantitative description of movement is available (a travel kernel), and a natural history consistent with pandemic influenza; we show that local cumulative incidence is positively correlated with population density when susceptible individuals are more connected in space than infectious individuals. Conversely, under the less intuitively likely scenario, when infectious individuals are more connected, local cumulative incidence is negatively correlated with population density. The strength and direction of correlation changes sign for other kernel parameter values. We show that simulation models in which it is assumed implicitly that only infectious individuals move are assuming a slightly unusual specific correlation between population density and attack rate. However, we also show that this potential structural bias can be corrected by using the appropriate non-isotropic kernel that maps infectious-only code onto the isotropic dual-mobility kernel. These results describe a precise relationship between the spatio-social mixing of infectious and susceptible individuals and local variability in attack rates. More generally, these results suggest a genuine risk that mechanistic models of high-resolution attack rate data may reach spurious conclusions if the precise implications of spatial force-of-infection assumptions are not first fully characterized, prior to models being fit to data.

摘要

传染病传播是一个固有空间的过程,宿主的家庭位置和他们的社交混合模式是重要的,具有传染性的个体的混合通常与易感个体的混合不同。尽管人类的发病率数据传统上被聚合到低分辨率数据集,但现代代表性监测系统,如电子医院记录,会生成具有精确家庭位置的大量案例数据。在这里,我们使用任意分辨率的网格化空间传播模型来研究人口密度、差异人口流动和局部发病率变化之间的理论关系。我们分析表明,如果易感个体和传染性个体以相同的方式移动,那么在网格中的单个像素中通常只有均匀的局部攻击率才是可能的。我们使用中国广东省的一个人口,其中有一个关于移动的强大定量描述(一个旅行核),以及一个与大流行流感一致的自然历史;我们表明,当地累积发病率与人口密度呈正相关,当易感个体在空间上比传染性个体更有联系时。相反,在不太直观的情况下,当传染性个体更有联系时,当地累积发病率与人口密度呈负相关。对于其他核参数值,相关变化的强度和方向会改变符号。我们表明,在假设只有传染性个体移动的模拟模型中,假设人口密度和攻击率之间存在略微不寻常的特定相关性。然而,我们还表明,通过使用将传染性个体代码映射到各向同性双移动核的适当非各向同性核,可以纠正这种潜在的结构偏差。这些结果描述了传染性和易感个体的空间社会混合与局部发病率变化之间的精确关系。更一般地说,如果在拟合模型之前,不首先充分描述空间感染率假设的精确含义,那么高分辨率攻击率数据的机制模型可能会得出错误的结论,存在真正的风险。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c20e/6358099/f5fd806a6697/pcbi.1006600.g001.jpg

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