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摘要翻译：
本文提出了一个新的模型来预测传染性疾病在不确定或低质量信息下的演变，就像新冠肺炎在中国和欧洲传播期间发生的最初情况一样。该模型已用于预测西班牙的死亡率，但可用于预测不同限制政策下ICU或机械呼吸机的需求。该模型的主要创新之处在于它跟踪单个个体的感染日期，并使用随机分布来聚集共享相同感染日期的个体。此外，它使用两种类型的感染，轻度和严重，恢复时间不同。这些特征在一组微分方程中实现，这些微分方程决定了携带者、感染、康复、住院和死亡的数量。与实际数据进行比较，结果吻合较好。
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英文标题：
《Robust predictive model for Carriers, Infections and Recoveries (CIR):
predicting death rates for CoVid-19 in Spain》
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作者：
Efren M. Benavides
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最新提交年份：
2020
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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：
This article presents a new model to predict the evolution of infective diseases under uncertainty or low-quality information, just as it has happened in the initial scenario during the CoVid-19 spread in China and Europe. The model has been used to predict the death rate in Spain but can be used to predict the demand of ICUs or mechanical ventilators under different restraint policies. The main novelty of the model is that it keeps track of the date of infection of a single individual and uses stochastic distributions to aggregate individuals who share the same date of infection. In addition, it uses two types of infections, mild and serious, with a different recovery time. These features are implemented in a set of differential equations which determine the number of Carriers, Infections, Recoveries, Hospitalized and Deaths. Comparison with real data shows good agreement.
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PDF链接：
https://arxiv.org/pdf/2003.13890
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