论文标题
携带者，感染和恢复的强大预测模型（CIR）：预测西班牙Covid-19的死亡率
Robust predictive model for Carriers, Infections and Recoveries (CIR): predicting death rates for CoVid-19 in Spain
论文作者
论文摘要
本文提出了一种新模型，以预测不确定性或低质量信息下感染性疾病的演变，就像在中国和欧洲的Covid-19期间的最初情况中发生的那样。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的ICU或机械呼吸机的需求。该模型的主要新颖性是它跟踪一个人的感染日期，并使用随机分布来汇总具有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。这些特征是在确定载体，感染，恢复，住院和死亡人数的一组微分方程中实现的。与真实数据的比较显示出良好的一致性。
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.
