空间传染病模型中的边缘效应

IF 2.1 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Emil Hodzic-Santor , Rob Deardon
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引用次数: 0

摘要

流行病模型是研究疾病在特定人群中表现的有用分析工具。个体水平模型(ILM)可以纳入个体水平的协变量信息,包括空间信息,以考虑人群中的异质性。然而,对个体水平模型进行参数化所需的高层次数据可能通常只能用于较大人群(如特定的县、省或国家)中的一个子人群。因此,参数估计可能会受到来自观察人群之外的感染所造成的边缘效应的影响。在此,我们将探讨在空间 ILM 的背景下,这种边缘效应会如何使参数估计产生偏差,并提出一种方法,在存在边缘效应的情况下,当可以从未被发现的部分人口中获得某种流行病严重程度的全球测量值时,可以改进模型拟合。我们将模型应用于模拟数据以及英国 2001 年口蹄疫疫情数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Edge effects in spatial infectious disease models

Epidemic models serve as a useful analytical tool to study how a disease behaves in a given population. Individual-level models (ILMs) can incorporate individual-level covariate information including spatial information, accounting for heterogeneity within the population. However, the high-level data required to parameterize an ILM may often be available only for a sub-population of a larger population (e.g., a given county, province, or country). As a result, parameter estimates may be affected by edge effects caused by infection originating from outside the observed population. Here, we look at how such edge effects can bias parameter estimates for within the context of spatial ILMs, and suggest a method to improve model fitting in the presence of edge effects when some global measure of epidemic severity is available from the unobserved part of the population. We apply our models to simulated data, as well as data from the UK 2001 foot-and-mouth disease epidemic.

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来源期刊
Spatial and Spatio-Temporal Epidemiology
Spatial and Spatio-Temporal Epidemiology PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
5.10
自引率
8.80%
发文量
63
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