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摘要翻译：
大流行病会对社区和社会造成巨大的干扰和损害。到目前为止，流行病的建模集中在大规模差分方程模型，如SIR和SEIR模型，或者详细的微观模拟，这些都很难在全球范围内应用。本文介绍了一个同时考虑全球和局部感染传播的流行病混合模型。我们假设传染病在区域间的传播受全球交通模式的显著影响，而在区域内的传播受当地条件的影响。因此，我们在SEIR模型的基础上，考虑了全球感染传播的区域间联系和人口密度，对流行病的传播进行了建模。我们通过对2002-2003年SARS疫情传播的模拟研究，利用不同地区之间的人口、人口密度和交通网络的现有数据，验证了我们的混合模型。虽然众所周知，国际关系和全球交通模式显著影响流行病的传播，但我们的结果表明，将这些因素集成到相对简单的模型中可以大大改善疾病传播模型的结果。
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英文标题：
《A Hybrid Model for Disease Spread and an Application to the SARS
Pandemic》
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作者：
Teruhiko Yoneyama, Sanmay Das and Mukkai Krishnamoorthy
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最新提交年份：
2010
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Multiagent Systems 多智能体系统
分类描述：Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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一级分类：Quantitative Biology 数量生物学
二级分类：Other Quantitative Biology 其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
Pandemics can cause immense disruption and damage to communities and societies. Thus far, modeling of pandemics has focused on either large-scale difference equation models like the SIR and the SEIR models, or detailed micro-level simulations, which are harder to apply at a global scale. This paper introduces a hybrid model for pandemics considering both global and local spread of infections. We hypothesize that the spread of an infectious disease between regions is significantly influenced by global traffic patterns and the spread within a region is influenced by local conditions. Thus we model the spread of pandemics considering the connections between regions for the global spread of infection and population density based on the SEIR model for the local spread of infection. We validate our hybrid model by carrying out a simulation study for the spread of SARS pandemic of 2002-2003 using available data on population, population density, and traffic networks between different regions. While it is well-known that international relationships and global traffic patterns significantly influence the spread of pandemics, our results show that integrating these factors into relatively simple models can greatly improve the results of modeling disease spread.
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PDF链接：
https://arxiv.org/pdf/1007.4523
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