Utilizing general human movement models to predict the spread of emerging infectious diseases in resource poor settings
Utilizing general human movement models to predict the spread of emerging infectious diseases in resource poor settings
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DOI：
10.1038/s41598-019-41192-3
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发表时间：
2019-03-26
影响因子：
4.6
通讯作者：
Reiner, R. C., Jr.
中科院分区：
文献类型：
--
作者：
Kraemer, M. U. G.;Golding, N.;Reiner, R. C., Jr.
人口流动是传染性病原体地理传播的重要驱动力。但是，很难获得关于疾病暴发期间人员流动的详细资料，在今后的流行病期间可能无法获得。2014年至2016年期间在西非爆发的埃博拉病毒病（EVD）表明，在一次跨物种传播事件后，病原体可以迅速传播到大城市中心。在这里，我们描述了一个灵活的传播模型，以测试通用的人类运动模型在估计EVD病例和空间传播的爆发过程中的效用。与没有这一组件的模型相比，包括人类流动性一般模型的传播模型显着提高了EVD发病率的预测。人员流动不仅在以前没有疾病的地方引发流行病，而且在整个流行病过程中发挥着重要作用。我们还展示了各国在人口混合和归因于移动指标的改进预测方面的重要差异。由于相对稀少，在未来流行病或大流行病发生之前，不太可能存在从当地获得的流动数据。我们的研究结果表明，来自一般人类运动模型的传输模式，可以提高预测的时空传输模式的地方，当地的流动性数据是不可用的。
Human mobility is an important driver of geographic spread of infectious pathogens. Detailed information about human movements during outbreaks are, however, difficult to obtain and may not be available during future epidemics. The Ebola virus disease (EVD) outbreak in West Africa between 2014-16 demonstrated how quickly pathogens can spread to large urban centers following one cross-species transmission event. Here we describe a flexible transmission model to test the utility of generalised human movement models in estimating EVD cases and spatial spread over the course of the outbreak. A transmission model that includes a general model of human mobility significantly improves prediction of EVD's incidence compared to models without this component. Human movement plays an important role not only to ignite the epidemic in locations previously disease free, but over the course of the entire epidemic. We also demonstrate important differences between countries in population mixing and the improved prediction attributable to movement metrics. Given their relative rareness, locally derived mobility data are unlikely to exist in advance of future epidemics or pandemics. Our findings show that transmission patterns derived from general human movement models can improve forecasts of spatio-temporal transmission patterns in places where local mobility data is unavailable.