论文标题
通过减少数据不确定性的流行病的订单建模，预测数据同化
Predictive data assimilation through Reduced Order Modeling for epidemics with data uncertainty
论文作者
论文摘要
在本文中，我们开发了一种数据同化程序，以通过数据不确定性（应用于Covid-19-19的大流行）来预测流行病的演变。我们通过解决邻近估计的真实（或官方）的数据的SIR流行模型来构建解决方案的vademecum。通过适当的正交分解（POD）从该vademecum构建了降低的基础。然后，将减少的POD碱基用于在最小二乘过程中已知数据的期间，以吸收大流行数据（感染，恢复，已故）。然后，拟合曲线用于预测接下来几天的大流行的演变。对安达卢西亚地区（西班牙），意大利和西班牙的验证测试显示了7天的准确预测，随着同化数据的数量的增加而有所改善。
In this article, we develop a data assimilation procedure to predict the evolution of epidemics with data uncertainty, with application to the Covid-19 pandemic. We construct a vademecum of solutions by solving the SIR epidemic model for a set of data neighboring the estimated real (or official) ones. A reduced basis is constructed from this vademecum through Proper Orthogonal Decomposition (POD). The reduced POD base is then applied to assimilate the pandemic data (infected, recovered, deceased) during the period in which data are known, by a least squares procedure. The fitted curves are then used to predict the evolution of the pandemic in the next days. Validation tests for Andalusia region (Spain), Italy and Spain show accurate predictions for 7 days that improve as the number of assimilated data increases.
