论文标题
携带者，感染和恢复的强大预测模型（CIR）：西班牙Covid-19的首次更新
Robust predictive model for Carriers, Infections and Recoveries (CIR): first update for CoVid-19 in Spain
论文作者
论文摘要
本文报告了先前在Arxiv：2003.13890V1中介绍的模型的评估的首次更新。新的可用数据已用于喂养模型，与实际数据的比较仍然显示出良好的一致性。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。
This article reports a first update on the assesment of the model previously presented in arXiv:2003.13890v1. New available data have been used to feed the model and the comparison with real data still shows good agreement. 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.
