Hirschprung Ron S, Hajaj Chen
Department of Industrial Engineering and Management, Ariel University, Israel.
Heliyon. 2021 Jul;7(7):e07416. doi: 10.1016/j.heliyon.2021.e07416. Epub 2021 Jun 29.
COVID-19 has long become a worldwide pandemic. It is responsible for the death of over two million people and posed an economic recession. This paper studies the spread pattern of COVID-19, aiming to establish a prediction model for this event. We harness Data Mining and Machine Learning methodologies to train regression models to predict the number of confirmed cases in a spatial-temporal space. We introduce an innovative concept ‒ the Center of Infection Mass (CoIM) ‒ adapted from the field of physics. We empirically evaluated our model on western European countries, based on the CoIM index and other features, and showed that a relatively high accurate prediction of the spread can be obtained. Our contribution is twofold: first, we introduced a prediction methodology and proved empirically that a prediction can be made even to the range of over a month; second, we showed promise in adopting the CoIM index to prediction models, when models that adopt the CoIM yield significantly better results than those that discard it. By applying our model, and better controlling the inherent tradeoff between life-saving and economy, we believe that decision-makers can take close to optimal measures. Thus, this methodology may contribute to public welfare.
新冠疫情早已成为一场全球大流行。它已导致超过两百万人死亡，并引发了经济衰退。本文研究新冠疫情的传播模式，旨在建立针对该事件的预测模型。我们利用数据挖掘和机器学习方法来训练回归模型，以预测时空空间中的确诊病例数。我们引入了一个源自物理学领域的创新概念——感染质量中心（CoIM）。我们基于CoIM指数和其他特征，在西欧国家对我们的模型进行了实证评估，结果表明能够获得对疫情传播相对准确的预测。我们的贡献有两方面：第一，我们引入了一种预测方法，并通过实证证明甚至可以对一个多月后的情况进行预测；第二，我们展示了将CoIM指数应用于预测模型的前景，采用CoIM的模型比不采用它的模型产生的结果显著更好。通过应用我们的模型，并更好地控制在拯救生命和经济之间的内在权衡，我们相信决策者可以采取近乎最优的措施。因此，这种方法可能有助于公共福利。
