Preserving privacy whilst maintaining robust epidemiological predictions.
Preserving privacy whilst maintaining robust epidemiological predictions.
复制标题
DOI：
10.1016/j.epidem.2016.10.004
复制
发表时间：
2016-12
期刊：
影响因子：
3.8
通讯作者：
M. Werkman;M. Tildesley;E. Brooks-Pollock;M. Keeling
中科院分区：
文献类型：
--
作者：
M. Werkman;M. Tildesley;E. Brooks-Pollock;M. Keeling
数学模型是量化潜在流行病和在爆发时制定最佳控制策略的宝贵工具。最先进的模型越来越需要详细的基于农场的个人敏感数据，由于缺乏数据收集能力或隐私问题，这些数据可能无法获得。然而，在许多情况下，可以使用汇总数据。在本研究中，我们以英国2001年口蹄疫流行病为案例研究，系统地研究了通过不同数据聚合初始化的数学模型所做预测的准确性。我们考虑的情况下，唯一可用的数据被聚合到空间网格单元，并开发一个集合种群模型，其中单个农场在一个单一的子种群被假定为行为一致，随机传输。我们还适应这个标准的集合种群模型捕捉异质性的农场规模和组成，使用农场普查数据。我们的研究结果表明，基于聚集数据的同质模型高估了最终的流行规模，但可以很好地预测空间传播。认识到农场规模的异质性，可以改善对最终疫情规模的预测，确定风险区域，确定疫情起飞的可能性，并确定最佳控制策略。总之，在无法获得单个农场数据的情况下，模型仍然可以产生有意义的预测，尽管在解释和使用时必须小心。
Mathematical models are invaluable tools for quantifying potential epidemics and devising optimal control strategies in case of an outbreak. State-of-the-art models increasingly require detailed individual farm-based and sensitive data, which may not be available due to either lack of capacity for data collection or privacy concerns. However, in many situations, aggregated data are available for use. In this study, we systematically investigate the accuracy of predictions made by mathematical models initialised with varying data aggregations, using the UK 2001 Foot-and-Mouth Disease Epidemic as a case study. We consider the scenario when the only data available are aggregated into spatial grid cells, and develop a metapopulation model where individual farms in a single subpopulation are assumed to behave uniformly and transmit randomly. We also adapt this standard metapopulation model to capture heterogeneity in farm size and composition, using farm census data. Our results show that homogeneous models based on aggregated data overestimate final epidemic size but can perform well for predicting spatial spread. Recognising heterogeneity in farm sizes improves predictions of the final epidemic size, identifying risk areas, determining the likelihood of epidemic take-off and identifying the optimal control strategy. In conclusion, in cases where individual farm-based data are not available, models can still generate meaningful predictions, although care must be taken in their interpretation and use.