Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York, United States of America.
PLoS Comput Biol. 2020 Oct 14;16(10):e1008233. doi: 10.1371/journal.pcbi.1008233. eCollection 2020 Oct.
Past work has shown that models incorporating human travel can improve the quality of influenza forecasts. Here, we develop and validate a metapopulation model of twelve European countries, in which international translocation of virus is driven by observed commuting and air travel flows, and use this model to generate influenza forecasts in conjunction with incidence data from the World Health Organization. We find that, although the metapopulation model fits the data well, it offers no improvement over isolated models in forecast quality. We discuss several potential reasons for these results. In particular, we note the need for data that are more comparable from country to country, and offer suggestions as to how surveillance systems might be improved to achieve this goal.
过去的研究表明,纳入人类旅行的模型可以提高流感预测的质量。在这里,我们开发并验证了一个包含十二个欧洲国家的人口迁移模型,其中病毒的国际转移是由观察到的通勤和航空旅行流量驱动的,并结合世界卫生组织的发病率数据使用该模型生成流感预测。我们发现,尽管人口迁移模型很好地拟合了数据,但它在预测质量方面并没有比孤立模型有所提高。我们讨论了这些结果的几个潜在原因。特别是,我们注意到需要更具国家间可比性的数据,并就如何改进监测系统以实现这一目标提出了建议。