Disease prevention versus data privacy: using landcover maps to inform spatial epidemic models.
Disease prevention versus data privacy: using landcover maps to inform spatial epidemic models.
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DOI：
10.1371/journal.pcbi.1002723
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发表时间：
2012
影响因子：
4.3
通讯作者：
Ryan SJ
中科院分区：
文献类型：
--
作者：
Tildesley MJ;Ryan SJ
传染病暴发早期阶段的流行病学数据的可用性对于模型师对疾病可能传播的准确预测和首选的干预策略至关重要。然而，在一些国家，必要的人口数据只能按总体规模提供。我们研究了家畜传染病模型在数据不完善的情况下预测疫情传播和获得最优控制策略的能力。采用地理信息方法，我们使用土地覆盖数据来预测英国农场的位置，并调查了在口蹄疫爆发时使用这些合成位置数据集对流行病学预测的影响。当广泛分类的土地覆盖数据被用来创建合成农场位置时，模型预测与真实数据模拟的预测显著偏离。然而，当使用更多分辨率较高的亚类土地利用数据时，可以获得对疫情大小、持续时间以及最佳疫苗接种和年轮扑杀策略的中等到高度准确的预测。这表明，在没有个别农场数据的情况下，地理信息方法可能是有用的，以便能够对疾病可能的传播进行预测性分析。这种方法还可用于与政策制定者合作的应急计划，以确定在未来牲畜爆发传染病时首选的控制策略。传染病的数学模型越来越多地被用于为政策决策提供信息。这种模型的优点是，可以快速测试和比较多种控制方案，而不会产生与现场试验相关的风险和成本。然而，要使这些模型成为实际有用的工具，需要详细的数据(包括人口和流行病学)。在许多国家，例如美国，家畜养殖场的个人层面的人口统计信息普遍缺乏。然而，遥感信息(如卫星图像和土地利用地图)提供了产生这些数据或产生替代人口的潜力。在本文中，我们使用土地覆盖数据来预测英国的农场位置，并调查在口蹄疫疫情发生时，农场位置的精确知识对流行病学预测的影响。我们的结果表明，当使用高分辨率的土地覆盖数据来预测农场位置时，可以获得对疫情大小、持续时间和首选干预策略的准确预测。这表明，土地覆盖数据可用于没有个别农场一级数据的国家，以便对未来暴发中疾病可能传播的情况进行分析。
The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock. Mathematical models of infectious diseases are increasingly used to inform policy decisions. The advantages of such models are that multiple control options can be rapidly tested and compared, without the risks and costs associated with field experiments. However, for such models to be practically useful tools detailed data (both in terms of populations and epidemiology) are required. In many countries, such as the USA, individual-level demographic information on livestock farms is generally lacking. However, remotely sensed information (such as satellite images and land-use maps) provides the potential to generate these data or produce surrogate populations. In this paper we use land cover data to predict farm locations in the UK and investigate the effect of a precise knowledge of farm locations upon epidemiological predictions in the event of a foot-and-mouth disease epidemic. Our results show that, when highly resolved land cover data are used to predict farm locations, accurate predictions of epidemic sizes, durations and preferred intervention strategies can be obtained. This suggests that land cover data may be used in countries where individual farm-level data are not available, to allow for analyses to be carried out regarding the likely spread of disease in future outbreaks.
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影响因子：
3.9
作者：
Jewell, C. P.;Keeling, M. J.;Roberts, G. O.
通讯作者：
Roberts, G. O.
影响因子：
56.9
作者：
Ferguson, NM;Donnelly, CA;Anderson, RM
通讯作者：
Anderson, RM
影响因子：
56.9
作者：
Keeling, MJ;Woolhouse, MEJ;Grenfell, BT
通讯作者：
Grenfell, BT
影响因子：
2.4
作者：
HUGHJONES, ME
通讯作者：
HUGHJONES, ME
DOI：
10.1098/rspb.2009.0427
发表时间：
2009-09-22
影响因子：
4.7
作者：
Tildesley, Michael J.;Bessell, Paul R.;Woolhouse, Mark E. J.
通讯作者：
Woolhouse, Mark E. J.