Díaz-Lozano Miguel, Guijo-Rubio David, Gutiérrez Pedro Antonio, Hervás-Martínez César
Maimonides Institute for Biomedical Research of Córdoba (IMIBIC), 14004 Córdoba, Spain.
Department of Computer Science and Numerical Analysis, University of Cordoba, 14071 Cordoba, Spain.
Expert Syst Appl. 2023 Sep 1;225:120103. doi: 10.1016/j.eswa.2023.120103. Epub 2023 Apr 17.
The sanitary emergency caused by COVID-19 has compromised countries and generated a worldwide health and economic crisis. To provide support to the countries' responses, numerous lines of research have been developed. The spotlight was put on effectively and rapidly diagnosing and predicting the evolution of the pandemic, one of the most challenging problems of the past months. This work contributes to the existing literature by developing a two-step methodology to analyze the transmission rate, designing models applied to territories with similar pandemic behavior characteristics. Virus transmission is considered as bacterial growth curves to understand the spread of the virus and to make predictions about its future evolution. Hence, an analytical clustering procedure is first applied to create groups of locations where the virus transmission rate behaved similarly in the different outbreaks. A curve decomposition process based on an iterative polynomial process is then applied, obtaining meaningful forecasting features. Information of the territories belonging to the same cluster is merged to build models capable of simultaneously predicting the 14-day incidence in several locations using Evolutionary Artificial Neural Networks. The methodology is applied to Andalusia (Spain), although it is applicable to any region across the world. Individual models trained for a specific territory are carried out for comparison purposes. The results demonstrate that this methodology achieves statistically similar, or even better, performance for most of the locations. In addition to being extremely competitive, the main advantage of the proposal lies in its complexity cost reduction. The total number of parameters to be estimated is reduced up to 93.51% for the short term and 93.31% for the mid-term forecasting, respectively. Moreover, the number of required models is reduced by 73.53% and 58.82% for the short- and mid-term forecasting horizons.
由新冠病毒（COVID-19）引发的卫生紧急状况已危及各国，并造成了一场全球范围的健康和经济危机。为支持各国的应对措施，人们开展了众多研究方向。其中的焦点是有效且快速地诊断和预测疫情的发展，这是过去几个月里最具挑战性的问题之一。本研究通过开发一种两步法来分析传播率，为现有文献做出了贡献，该方法设计了适用于具有相似疫情行为特征地区的模型。病毒传播被视为细菌生长曲线，以了解病毒的传播情况并预测其未来发展。因此，首先应用一种分析聚类程序来创建不同疫情爆发中病毒传播率表现相似的地点组。然后应用基于迭代多项式过程的曲线分解过程，获得有意义的预测特征。将属于同一聚类的地区信息合并，以构建能够使用进化人工神经网络同时预测多个地点14天发病率的模型。该方法应用于安达卢西亚（西班牙），不过它适用于世界上任何地区。为进行比较，还针对特定地区训练了单独的模型。结果表明，该方法在大多数地点都取得了统计学上相似甚至更好的性能。除了极具竞争力外，该提议的主要优势在于其降低了复杂度成本。在短期预测中，待估计参数的总数减少了93.51%，在中期预测中减少了93.31%。此外，在短期和中期预测范围内，所需模型的数量分别减少了73.53%和58.82%。
