摘要翻译：
背景：严重急性呼吸综合征(SARS)疫情的全球蔓延清楚地表明，在理解新出现的疾病爆发时，考虑远程运输网络的重要性。因此，引入广泛的运输数据集是开发具有真实感的流行病模型的重要一步。方法：我们建立了一个通用的随机元人口模型，该模型结合了220个国家3100个城市地区的实际旅行和人口普查数据。该模型允许对国家爆发的可能性及其规模进行概率预测。该模型提供的可预测性水平可以定量分析，并与代表疾病传播最可能途径的稳健流行病路径的出现有关。结果：为了评估模型的预测能力，考虑了SARS全球传播的案例研究。模型中使用的疾病参数值和初始条件是根据香港的经验数据评估的。对特定国家爆发的可能性以及新出现的流行病途径进行了评估。模拟结果与SARS全球流行的经验数据一致。结论：所提出的计算方法表明，长期流动和人口统计数据的结合为流行病模型提供了一种预测能力，这种能力可以得到一致的检验和理论上的激励。因此，这种计算策略可被视为分析和预测新出现疾病的全球传播以及确定旨在减少潜在灾难性疫情影响的遏制政策的通用工具。
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英文标题：
《Predictability and epidemic pathways in global outbreaks of infectious
  diseases: the SARS case study》
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作者：
Vittoria Colizza, Alain Barrat, Marc Barthelemy, Alessandro Vespignani
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最新提交年份：
2008
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分类信息：

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：
  Background: The global spread of the severe acute respiratory syndrome (SARS) epidemic has clearly shown the importance of considering the long-range transportation networks in the understanding of emerging diseases outbreaks. The introduction of extensive transportation data sets is therefore an important step in order to develop epidemic models endowed with realism. Methods: We develop a general stochastic meta-population model that incorporates actual travel and census data among 3 100 urban areas in 220 countries. The model allows probabilistic predictions on the likelihood of country outbreaks and their magnitude. The level of predictability offered by the model can be quantitatively analyzed and related to the appearance of robust epidemic pathways that represent the most probable routes for the spread of the disease. Results: In order to assess the predictive power of the model, the case study of the global spread of SARS is considered. The disease parameter values and initial conditions used in the model are evaluated from empirical data for Hong Kong. The outbreak likelihood for specific countries is evaluated along with the emerging epidemic pathways. Simulation results are in agreement with the empirical data of the SARS worldwide epidemic. Conclusions: The presented computational approach shows that the integration of long-range mobility and demographic data provides epidemic models with a predictive power that can be consistently tested and theoretically motivated. This computational strategy can be therefore considered as a general tool in the analysis and forecast of the global spreading of emerging diseases and in the definition of containment policies aimed at reducing the effects of potentially catastrophic outbreaks.
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PDF链接：
https://arxiv.org/pdf/0801.2261