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使用喀麦隆的口蹄疫作为案例研究,进行网络分析以量化宿主迁移对多层次疾病传播模型的影响。

Network analyses to quantify effects of host movement in multilevel disease transmission models using foot and mouth disease in Cameroon as a case study.

机构信息

Division of Environmental Health Sciences, College of Public Health, The Ohio State University, Columbus, OH, United States of America.

Department of Geography, The Ohio State University, Columbus, OH, United States of America.

出版信息

PLoS Comput Biol. 2019 Aug 29;15(8):e1007184. doi: 10.1371/journal.pcbi.1007184. eCollection 2019 Aug.

Abstract

The dynamics of infectious diseases are greatly influenced by the movement of both susceptible and infected hosts. To accurately represent disease dynamics among a mobile host population, detailed movement models have been coupled with disease transmission models. However, a number of different host movement models have been proposed, each with their own set of assumptions and results that differ from the other models. Here, we compare two movement models coupled to the same disease transmission model using network analyses. This application of network analysis allows us to evaluate the fit and accuracy of the movement model in a multilevel modeling framework with more detail than established statistical modeling fitting methods. We used data that detailed mobile pastoralists' movements as input for 100 stochastic simulations of a Spatio-Temporal Movement (STM) model and 100 stochastic simulations of an Individual Movement Model (IMM). Both models represent dynamic movement and subsequent contacts. We generated networks in which nodes represent camps and edges represent the distance between camps. We simulated pathogen transmission over these networks and tested five network metrics-strength, betweenness centrality, three-step reach, density, and transitivity-to determine which could predict disease simulation outcomes and thereby be used to correlate model simulation results with disease transmission simulations. We found that strength, network density, and three-step reach of movement model results correlated with the final epidemic size of outbreak simulations. Betweenness centrality only weakly correlated for the IMM model. Transitivity only weakly correlated for the STM model and time-varying IMM model metrics. We conclude that movement models coupled with disease transmission models can affect disease transmission results and should be carefully considered and vetted when modeling pathogen spread in mobile host populations. Strength, network density, and three-step reach can be used to evaluate movement models before disease simulations to predict final outbreak sizes. These findings can contribute to the analysis of multilevel models across systems.

摘要

传染病的动态受到易感宿主和感染宿主移动的极大影响。为了准确地表示移动宿主群体中的疾病动态,详细的运动模型已与疾病传播模型结合使用。然而,已经提出了许多不同的宿主运动模型,每个模型都有自己的假设和结果,与其他模型不同。在这里,我们使用网络分析比较了两个与相同疾病传播模型耦合的运动模型。网络分析的这种应用使我们能够在多层次建模框架中比传统的统计建模拟合方法更详细地评估运动模型的拟合和准确性。我们使用详细描述流动牧民运动的数据作为输入,对时空运动(STM)模型和个体运动模型(IMM)各进行了 100 次随机模拟。这两个模型都代表了动态运动和随后的接触。我们生成了以营地为节点、以营地之间距离为边的网络。我们在这些网络上模拟了病原体的传播,并测试了五个网络指标——强度、介数中心性、三步可达性、密度和传递性,以确定哪些指标可以预测疾病模拟结果,从而将模型模拟结果与疾病传播模拟相关联。我们发现,运动模型的强度、网络密度和三步可达性与爆发模拟的最终流行规模相关。对于 IMM 模型,介数中心性只有弱相关性。对于 STM 模型和时变 IMM 模型指标,传递性只有弱相关性。我们得出结论,与疾病传播模型耦合的运动模型会影响疾病的传播结果,因此在对移动宿主群体中的病原体传播进行建模时应仔细考虑和验证。在进行疾病模拟之前,可以使用强度、网络密度和三步可达性来评估运动模型,以预测最终的爆发规模。这些发现可以为跨系统的多层次模型分析做出贡献。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/176d/6776348/776829153235/pcbi.1007184.g001.jpg

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