量化英国牛运动网络的变化
Quantifying changes in the British cattle movement network
摘要：
牛追踪系统数据库是一个在线记录系统，记录英国牛的出生、死亡和牛群之间的流动情况。虽然已经过彻底审查，但最近报告的流动分析是2009年的。本文使用该数据库构建了2004- 2006年和2015- 2017年两个不同时期的加权有向月度运动网络，以量化网络的底层结构发生了多大变化。网络结构的实质性变化可能会直接影响政策制定者，或者可能会影响基于网络数据建立的模型，而这些反过来又可能影响政策制定者及其风险评估。使用四种一般网络度量（具有移动、移动、出生和死亡的节点总数），结合网络度量来描述每个月的网络。对数据库的两次更新进行了审查，以确定在两次更新之间清理了多少特定时期储存的流动数据。统计模型显示，在所有网络指标和九个网络指标中的六个指标的值中，时间段（2004- 2006年与2015- 2017年）具有统计学显著影响。巨连通和弱强连通分量的大小的变化预示着最大流行规模的上界和下界的减小。对数据库更新的审查表明，更新之间的记录存在差异，因此有证据表明历史数据在更新之间发生了变化。疾病通过网络传播的准确建模需要对网络进行有代表性的描述。作者建议，在可能的情况下，网络建模应始终使用最新的可用数据，并应审查网络预测方法，以减少数据可用所需的时间。
英文摘要：
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.
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