Laboratory of Engineering Sciences for Energy, National School of Applied Sciences ENSAJ, UCD, El Jadida 24000, Morocco.
Laboratory of Mechanics of Normandy, National Institute of Applied Sciences INSA of Rouen-Normandy, 76800 Saint Etienne du Rouvray, France.
Int J Environ Res Public Health. 2023 Feb 24;20(5):4102. doi: 10.3390/ijerph20054102.
In this paper, we propose a new method for epidemic risk modelling and prediction, based on uncertainty quantification (UQ) approaches. In UQ, we consider the state variables as members of a convenient separable Hilbert space, and we look for their representation in finite dimensional subspaces generated by truncations of a suitable Hilbert basis. The coefficients of the finite expansion can be determined by approaches established in the literature, adapted to the determination of the probability distribution of epidemic risk variables. Here, we consider two approaches: collocation (COL) and moment matching (MM). Both are applied to the case of SARS-CoV-2 in Morocco, as an epidemic risk example. For all the epidemic risk indicators computed in this study (number of detections, number of deaths, number of new cases, predictions and human impact probabilities), the proposed models were able to estimate the values of the state variables with precision, i.e., with very low root mean square errors (RMSE) between predicted values and observed ones. Finally, the proposed approaches are used to generate a decision-making tool for future epidemic risk management, or, more generally, a quantitative disaster management approach in the humanitarian supply chain.
在本文中，我们提出了一种新的基于不确定性量化 (UQ) 方法的流行病风险建模和预测方法。在 UQ 中，我们将状态变量视为方便可分 Hilbert 空间的成员，并寻找它们在由合适 Hilbert 基截断生成的有限维子空间中的表示。有限展开的系数可以通过文献中建立的方法来确定，这些方法适用于流行病风险变量的概率分布的确定。在这里，我们考虑两种方法：配置（COL）和矩匹配（MM）。这两种方法都应用于摩洛哥的 SARS-CoV-2 作为流行病风险示例。对于本研究中计算的所有流行病风险指标（检测数量、死亡数量、新发病例数量、预测和人类影响概率），所提出的模型都能够以高精度估计状态变量的值，即，预测值与观测值之间的均方根误差 (RMSE) 非常低。最后，所提出的方法被用于生成未来流行病风险管理的决策工具，或者更一般地，人道主义供应链中的定量灾害管理方法。
