Ketu Shwet, Mishra Pramod Kumar
Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, India.
Appl Intell (Dordr). 2021;51(3):1492-1512. doi: 10.1007/s10489-020-01889-9. Epub 2020 Sep 28.
Virus based epidemic is one of the speedy and widely spread infectious disease which can affect the economy of the country as well as it is life-threatening too. So, there is a need to forecast the epidemic lifespan, which can help us in taking preventive measures and remedial action on time. These preventive measures and corrective action may consist of closing schools, closing malls, closing theaters, sealing of borders, suspension of public services, and suspension of traveling. Resuming such restrictions is depends upon the outbreak momentum and its decay rate. The accurate forecasting of the epidemic lifespan is one of the enormously essential and challenging tasks. It is a challenging task because the lack of knowledge about the novel virus-based diseases and its consequences with complicated societal-governmental factors can influence the widespread of this newly born disease. At this stage, any forecasting can play a vital role, and it will be reliable too. As we know, the novel virus-based diseases are in a growing phase, and we also do not have real-time data samples. Thus, the biggest challenge is to find out the machine learning-based best forecasting model, which could offer better forecasting with the limited training samples. In this paper, the Multi-Task Gaussian Process (MTGP) regression model with enhanced predictions of novel coronavirus (COVID-19) outbreak is proposed. The purpose of the proposed MTGP regression model is to predict the COVID-19 outbreak worldwide. It will help the countries in planning their preventive measures to reduce the overall impact of the speedy and widely spread infectious disease. The result of the proposed model has been compared with the other prediction model to find out its suitability and correctness. In subsequent analysis, the significance of IoT based devices in COVID-19 detection and prevention has been discussed.
基于病毒的疫情是迅速传播且广泛扩散的传染病之一，它会影响国家经济，同时也危及生命。因此，有必要预测疫情的持续时间，这有助于我们及时采取预防措施和补救行动。这些预防措施和纠正行动可能包括关闭学校、关闭商场、关闭剧院、封锁边境、暂停公共服务以及暂停旅行。恢复这些限制取决于疫情的爆发势头及其衰减率。准确预测疫情持续时间是极其重要且具有挑战性的任务之一。这是一项具有挑战性的任务，因为对新型病毒性疾病及其与复杂社会 - 政府因素的后果缺乏了解，可能会影响这种新出现疾病的传播。在现阶段，任何预测都能发挥重要作用，并且也会是可靠的。正如我们所知，新型病毒性疾病正处于发展阶段，而且我们也没有实时数据样本。因此，最大的挑战是找到基于机器学习的最佳预测模型，该模型能够在有限的训练样本下提供更好的预测。本文提出了具有增强的新型冠状病毒（COVID - 19）爆发预测功能的多任务高斯过程（MTGP）回归模型。所提出的MTGP回归模型的目的是预测全球范围内的COVID - 19疫情爆发。它将帮助各国规划预防措施，以减少这种迅速传播且广泛扩散的传染病的总体影响。已将所提出模型的结果与其他预测模型进行比较，以确定其适用性和正确性。在后续分析中，还讨论了基于物联网的设备在COVID - 19检测和预防中的重要性。
