The Cattle Tracing System database is an online recording system for cattle births, deaths and between--herd movements in the United Kingdom. Although it has been thoroughly examined, the most recently reported movement analysis is from 2009. This article uses the database to construct weighted directed monthly movement networks for two distinct periods of time, 2004--2006 and 2015--2017, to quantify by how much the underlying structure of the network has changed. Substantial changes in network structure may influence policy--makers directly or may influence models built upon the network data, and these in turn could impact policy--makers and their assessment of risk. Four general network measures are used (total number of nodes with movements, movements, births and deaths), in conjunction with network metrics to describe each monthly network. Two updates of the database were examined to determine by how much the movement data stored for a particular time period had been cleansed between updates. Statistical models show that there is a statistically significant effect of the time period (2004--2006 vs 2015--2017) in the values of all network measures and six of nine network metrics. Changes in the sizes of both the Giant and Weakly Strongly Connected components predict reductions in the upper and lower bounds of the maximum epidemic size. Examination of the updates of the database show that there are differences in records between updates and therefore evidence of historical data changing between updates. Accurate modelling of disease spread through a network requires representative descriptions of the network. The authors recommend that where possible the most recent available data always be used for network modelling and that methods of network prediction be examined to mitigate for the time required for data to become available.
翻译:牛群追踪系统数据库是联合王国牛出生、死亡和牧群之间流动的在线记录系统,虽然已经对该系统进行了彻底审查,但最近报告的移动分析是2009年进行的。本文章使用数据库,在两个不同的时期(2004-2006年和2015-2017年)建立加权定向每月移动网络,以量化网络基本结构的变化程度。网络结构的重大变化可能直接影响决策者,或可能影响基于网络数据的模型,而这反过来又可能影响决策者及其风险评估。使用了四种一般网络措施(最近与移动、移动、出生和死亡有关的节点总数),同时使用网络衡量每个月网络的情况。对数据库的两个更新进行了审查,以确定在两个不同时期(2004-2006年和2015-2017年)储存的移动数据有多少经过清理。统计模型显示,在时间段期间(2004-2006年和2015-2017年)所有网络措施的价值和9个网络指标中的6个都会产生重要影响。在数据库中,Giant和Weak最近期的网络数据总数量变化,因此,在数据库中,现有数据更新的时间要从上到历史数据更新中,将尽可能缩小。