论文标题
一种经验算法，以预测社交距离干预措施后，有症状患者的数量的演变
An empirical algorithm to forecast the evolution of the number of COVID-19 symptomatic patients after social distancing interventions
论文作者
论文摘要
我们提出了一种经验算法，以预测在大流行蔓延的早期和严格的社会疏远干预措施之后，在199个有症状的患者数量的演变。该算法基于一个低维模型，用于在实施严格的社会距离措施后降低指数增长率的变化。从测试阳性数量给出的可观察到的数据中，我们的模型估计了在模型配方中引入的受感染后标的数量。我们还使用该模型跟踪感染患者的数量，这些患者后来使用注册的死亡人数以及从感染到死亡的分布时间。研究了所提出的模型与SIR模型的关系。模型参数拟合是通过最小化数据和模型预测之间的二次误差来完成的。还提出了一个扩展模型，该模型允许长期预测。该模型的在线实施是可以在www.ctim.es/covid19上撤销的
We present an empirical algorithm to forecast the evolution of the number of COVID-19 symptomatic patients in the early stages of the pandemic spread and after strict social distancing interventions. The algorithm is based on a low dimensional model for the variation of the exponential growth rate that decreases after the implementation of strict social distancing measures. From the observable data given by the number of tested positive, our model estimates the number of infected hindcast introducing in the model formulation the incubation time. We also use the model to follow the number of infected patients who later die using the registered number of deaths and the distribution time from infection to death. The relationship of the proposed model with the SIR models is studied. Model parameters fitting is done by minimizing a quadratic error between the data and the model forecast. An extended model is also proposed that allows a longer term forecast. An online implementation of the model is avalaible at www.ctim.es/covid19
