Epidemic predictions in an imperfect world: modelling disease spread with partial data.

Epidemic predictions in an imperfect world: modelling disease spread with partial data.
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DOI:
10.1098/rspb.2015.0205
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发表时间:
2015-06-07
期刊:
Proceedings. Biological sciences
影响因子:
--
通讯作者:
Tildesley MJ
Tildesley MJ
中科院分区:
其他
文献类型:
--
作者:
Dawson PM;Werkman M;Brooks-Pollock E;Tildesley MJ

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“大数据”流行病模型正越来越多地用于影响政府政策,以帮助控制和根除传染病。在牲畜的情况下,详细的运动记录已被用来参数化现实的传播模型。虽然英国和欧盟其他国家的牲畜流动数据很容易获得,但在世界上许多国家,没有这样详细的数据。通过使用英国牛贸易网络的综合数据库,我们实施了各种抽样策略,以确定提供准确流行病学预测所需的网络数据的数量。研究发现,通过针对运动次数最多的节点,可以准确预测流行病的规模和空间传播。这项工作对美国等数据获取有限的国家以及可能缺乏资源收集有关牲畜流动的完整数据集的发展中国家具有重要意义。
‘Big-data’ epidemic models are being increasingly used to influence government policy to help with control and eradication of infectious diseases. In the case of livestock, detailed movement records have been used to parametrize realistic transmission models. While livestock movement data are readily available in the UK and other countries in the EU, in many countries around the world, such detailed data are not available. By using a comprehensive database of the UK cattle trade network, we implement various sampling strategies to determine the quantity of network data required to give accurate epidemiological predictions. It is found that by targeting nodes with the highest number of movements, accurate predictions on the size and spatial spread of epidemics can be made. This work has implications for countries such as the USA, where access to data is limited, and developing countries that may lack the resources to collect a full dataset on livestock movements.
在非常不同的时间量表上的疾病动态:英国牲畜运动网络上的脚和口径。
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