Aggarwal Lakshita, Goswami Puneet, Sachdeva Shelly
Department of Computer Science & Engineering, SRM University Delhi-NCR Sonepat, Haryana, India.
Department of Computer Science & Engineering, NIT Delhi, India.
Appl Soft Comput. 2021 Mar;101:107056. doi: 10.1016/j.asoc.2020.107056. Epub 2020 Dec 29.
COVID-19 is a buzz word nowadays. The deadly virus that started in China has spread worldwide. The fundamental principle is "if the disease can travel faster information has to travel even faster". The sequence of events reveals the upheaval need to strengthen the ability of the early warning system, risk reduction, and management of national and global risks. Digital contact tracing apps like Aarogya setu (India) and Pan-European privacy preserving proximity tracing (German) has somehow helped but they are more effective in the initial stage and less relevant in the community spread phase. Thus, there is a need to devise a Decision Support System (DSS) based on machine learning algorithms. In this paper, we have attempted to propose an Additive Utility Assumption Approach for Criterion Comparison in Multi-criterion Intelligent Decision Support system for COVID-19. The dataset of Covid-19 has been taken from government link for validating the results. In this paper, an additive utility assumption-based approach for multi-criterion decision support system (MCDSS) with an accurate prediction of identified risk factors on certain well-defined input parameters is proposed and validated empirically using the standard SEIR model approach (Susceptible, Exposed, Infected and Recovered). The results includes comparative analysis in tabular form with already existing approaches to illustrate the potential of the proposed approach including the parameters such as Precision, Recall and F-Score. Other advanced parameters such as, MCC (Matthews Correlation Coefficient), ROC (Receiver Operating Characteristics) and PRC (Precision Recall) have also been considered for validation and the graphs are illustrated using Jupyter notebook. The statistical analysis of the most affected top eight states of India is undertaken effectively using the Weka software tool and IBM Cognos software to correctly predict the outbreak of pandemic situation due to Covid-19. Finally, the article has immense potential to contribute to the COVID-19 situation and may prove to be instrumental in propelling the research interest of researchers and providing some useful insights for the current pandemic situation.
新冠病毒病（COVID-19）如今是个热门词汇。这种始于中国的致命病毒已在全球传播。基本原则是“如果疾病传播速度更快，那么信息传播速度必须更快”。一系列事件揭示了加强早期预警系统能力、降低风险以及管理国家和全球风险的迫切需求。像印度的“阿罗gya Setu”和德国的“泛欧隐私保护近距离追踪”这样的数字接触者追踪应用程序虽起到了一定帮助，但它们在初始阶段更有效，在社区传播阶段相关性较低。因此，有必要设计一种基于机器学习算法的决策支持系统（DSS）。在本文中，我们尝试为COVID-19多准则智能决策支持系统中的准则比较提出一种加法效用假设方法。COVID-19的数据集取自政府链接以验证结果。本文提出了一种基于加法效用假设的多准则决策支持系统（MCDSS），该系统能根据某些明确的输入参数准确预测已识别的风险因素，并使用标准的SEIR模型方法（易感、暴露、感染和康复）进行实证验证。结果包括以表格形式与现有方法进行比较分析，以说明所提方法的潜力，包括精度、召回率和F值等参数。还考虑了其他高级参数，如马修斯相关系数（MCC）、受试者工作特征（ROC）和精确召回率（PRC）进行验证，并使用Jupyter notebook绘制图表。利用Weka软件工具和IBM Cognos软件对印度受影响最严重的八个邦进行了有效统计分析，以正确预测因COVID-19导致的大流行情况爆发。最后，本文对COVID-19情况具有巨大的贡献潜力，可能有助于推动研究人员的研究兴趣，并为当前的大流行情况提供一些有用的见解。
