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摘要翻译：
在这篇文章中，我们使用SARS疫情的全球和区域数据，结合易感、暴露、感染、诊断和康复人群的模型（“SEIJR”）来提取这些人群的平均性质和速率常数。该模型拟合了加拿大安大略省（多伦多）、中国香港和新加坡疫情的数据，并根据各种假设和观察做出了预测，包括隔离诊断患有非典的个人的当前影响。香港和新加坡的疫情动态似乎不同于安大略省多伦多的动态。多伦多在3月31日至4月6日期间病例数迅速增加，随后新增病例数显著放缓。我们将此解释为3月26日后诊断率和患者隔离有效性增加的结果。我们的最佳估计与非典最终在多伦多得到控制是一致的，尽管控制的时间对我们模型中的参数很敏感。结果表明，尽管经验模拟了传播的异质性，但SARS的平均繁殖数为1.2，这一值与某些流感病毒株的计算值非常相似。虽然在世界一些地区，非典感染率高于10%（如果不加以控制的话）并不令人惊讶，但由于缺乏数据以及观察到的参数的异质性和敏感性，我们无法预测非典的长期影响。
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英文标题：
《SARS oubreaks in Ontario, Hong Kong and Singapore: the role of diagnosis
and isolation as a control mechanism》
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作者：
Gerardo Chowell, Paul W. Fenimore, Melissa A. Castillo-Garsow, Carlos
Castillo-Chavez
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最新提交年份：
2005
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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：
In this article we use global and regional data from the SARS epidemic in conjunction with a model of susceptible, exposed, infective, diagnosed, and recovered classes of people (``SEIJR'') to extract average properties and rate constants for those populations. The model is fitted to data from the Ontario (Toronto) in Canada, Hong Kong in China and Singapore outbreaks and predictions are made based on various assumptions and observations, including the current effect of isolating individuals diagnosed with SARS. The epidemic dynamics for Hong Kong and Singapore appear to be different from the dynamics in Toronto, Ontario. Toronto shows a very rapid increase in the number of cases between March 31st and April 6th, followed by a {\it significant} slowing in the number of new cases. We explain this as the result of an increase in the diagnostic rate and in the effectiveness of patient isolation after March 26th. Our best estimates are consistent with SARS eventually being contained in Toronto, although the time of containment is sensitive to the parameters in our model. It is shown that despite the empirically modeled heterogeneity in transmission, SARS' average reproductive number is 1.2, a value quite similar to that computed for some strains of influenza \cite{CC2}. Although it would not be surprising to see levels of SARS infection higher than ten per cent in some regions of the world (if unchecked), lack of data and the observed heterogeneity and sensitivity of parameters prevent us from predicting the long-term impact of SARS.
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PDF链接：
https://arxiv.org/pdf/q-bio/0503002
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