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边缘地区的麻疹:沿海地区的异质性与感染动态

Measles on the edge: coastal heterogeneities and infection dynamics.

作者信息

Bharti Nita, Xia Yingcun, Bjornstad Ottar N, Grenfell Bryan T

机构信息

Department of Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America.

出版信息

PLoS One. 2008 Apr 9;3(4):e1941. doi: 10.1371/journal.pone.0001941.

Abstract

Mathematical models can help elucidate the spatio-temporal dynamics of epidemics as well as the impact of control measures. The gravity model for directly transmitted diseases is currently one of the most parsimonious models for spatial epidemic spread. This model uses distance-weighted, population size-dependent coupling to estimate host movement and disease incidence in metapopulations. The model captures overall measles dynamics in terms of underlying human movement in pre-vaccination England and Wales (previously established). In spatial models, edges often present a special challenge. Therefore, to test the model's robustness, we analyzed gravity model incidence predictions for coastal cities in England and Wales. Results show that, although predictions are accurate for inland towns, they significantly underestimate coastal persistence. We examine incidence, outbreak seasonality, and public transportation records, to show that the model's inaccuracies stem from an underestimation of total contacts per individual along the coast. We rescue this predicted 'edge effect' by increasing coastal contacts to approximate the number of per capita inland contacts. These results illustrate the impact of 'edge effects' on epidemic metapopulations in general and illustrate directions for the refinement of spatiotemporal epidemic models.

摘要

数学模型有助于阐明流行病的时空动态以及控制措施的影响。直接传播疾病的引力模型是目前空间流行病传播最简洁的模型之一。该模型使用距离加权、依赖人口规模的耦合来估计集合种群中的宿主移动和疾病发病率。该模型根据疫苗接种前英格兰和威尔士潜在的人类移动情况(先前已确定)捕捉了麻疹的总体动态。在空间模型中,边界往往带来特殊挑战。因此,为了测试该模型的稳健性,我们分析了英格兰和威尔士沿海城市的引力模型发病率预测。结果表明,虽然对内陆城镇的预测准确,但它们显著低估了沿海地区的疫情持续情况。我们研究了发病率、疫情季节性和公共交通记录,以表明该模型的不准确源于对沿海地区每个人的总接触量估计不足。我们通过增加沿海接触量以近似内陆人均接触量的方式来挽救这种预测的“边界效应”。这些结果总体上说明了“边界效应”对流行病集合种群的影响,并说明了时空流行病模型改进的方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40bd/2275791/8cf8221a66bc/pone.0001941.g001.jpg

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