Forecasting the Trend of COVID-19 Considering the Impacts of Public Health Interventions: An Application of FGM and Buffer Level.

IF 5.9 Q1 Computer Science
Kai Lisa Lo, Minglei Zhang, Yanhui Chen, Jinhong Jackson Mi
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引用次数: 3

Abstract

Purpose: COVID-19 is still showing a tendency of spreading around the world. In order to improve the subsequent control of COVID-19, it is essential to conduct a study on measuring and predicting the scale of the outbreak in the future.

Methods: This paper uses rolling mechanism and grid search to find the best fractional order of Fractional Order Accumulation Grey Model (FGM). Buffer level is proposed based on the general form of weakening buffer operator to measure the effect of government control measurements on the epidemic. And the buffer level is associated with the Government Response Stringency index and the Mobility Index.

Results: Firstly, the model proposed in this paper dominates the ARIMA model which has been widely used in predicting the confirmed COVID-19 cases. Secondly, in the process of using the buffer level to modify the FGM, this paper finds that government measurements require the active cooperation of the public and often have a time lag when they are effective. Only when government increase its stringency and the public observe the order can the spread of COVID-19 be slowed down. If there is only the controlling measure and the public does not react actively, it will not slow down the epidemic. Thirdly, according to the Mobility Index and Government Response Stringency Index in December, this paper predicts the cumulative confirmed cases of the end of January in different scenarios according to different buffer levels. The study suggests that the world should continue to maintain high vigilance and take corresponding control measures for the outbreak of COVID-19.

Conclusions: Government's control measures and public's abidance are both important in this battle with COVID-19. Governments control measures have time-lag effect and the time lag is about 9 days. When the government increases its stringency and the public cooperates with the government, we must consider the weaken buffer operator with proper buffer level in the prediction process. These prediction methods can be considered in the prediction of COVID-19 confirmed cases in the future or the trend of other epidemics.

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考虑公共卫生干预影响的COVID-19趋势预测:女性生殖器切割和缓冲水平的应用
目的:新冠肺炎疫情在全球仍有蔓延趋势。为提高后续疫情防控水平,有必要开展未来疫情规模的测算和预测研究。方法:利用滚动机制和网格搜索方法寻找分数阶累积灰色模型(FGM)的最佳分数阶。在弱化缓冲算子的一般形式的基础上,提出了缓冲水平来衡量政府控制措施对疫情的影响。缓冲水平与政府反应严格度指数和流动性指数相关。结果:首先,本文提出的模型优于ARIMA模型,ARIMA模型已被广泛用于预测新冠肺炎确诊病例。其次,在利用缓冲水平修正女性生殖器切割的过程中,本文发现政府措施需要公众的积极配合,并且往往在有效时存在时滞。只有政府加强严格管理,公众遵守秩序,才能减缓新冠病毒的传播。如果只有控制措施,公众不积极反应,就不会减缓疫情。第三,根据12月份的流动性指数和政府应对严密性指数,根据不同的缓冲水平,预测1月底不同情景下的累计确诊病例。研究建议,世界各国应继续保持高度警惕,并采取相应的控制措施。结论:在抗击新冠肺炎疫情中,政府的防控措施和公众的遵守都很重要。政府控制措施有时滞效应,时滞约为9天。在政府加大紧缩力度、公众与政府合作的情况下,在预测过程中必须考虑适当缓冲级别的弱化缓冲算子。这些预测方法可用于预测未来新冠肺炎确诊病例或其他疫情趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Healthcare Informatics Research
Journal of Healthcare Informatics Research Computer Science-Computer Science Applications
CiteScore
13.60
自引率
1.70%
发文量
12
期刊介绍: Journal of Healthcare Informatics Research serves as a publication venue for the innovative technical contributions highlighting analytics, systems, and human factors research in healthcare informatics.Journal of Healthcare Informatics Research is concerned with the application of computer science principles, information science principles, information technology, and communication technology to address problems in healthcare, and everyday wellness. Journal of Healthcare Informatics Research highlights the most cutting-edge technical contributions in computing-oriented healthcare informatics.  The journal covers three major tracks: (1) analytics—focuses on data analytics, knowledge discovery, predictive modeling; (2) systems—focuses on building healthcare informatics systems (e.g., architecture, framework, design, engineering, and application); (3) human factors—focuses on understanding users or context, interface design, health behavior, and user studies of healthcare informatics applications.   Topics include but are not limited to: ·         healthcare software architecture, framework, design, and engineering;·         electronic health records·         medical data mining·         predictive modeling·         medical information retrieval·         medical natural language processing·         healthcare information systems·         smart health and connected health·         social media analytics·         mobile healthcare·         medical signal processing·         human factors in healthcare·         usability studies in healthcare·         user-interface design for medical devices and healthcare software·         health service delivery·         health games·         security and privacy in healthcare·         medical recommender system·         healthcare workflow management·         disease profiling and personalized treatment·         visualization of medical data·         intelligent medical devices and sensors·         RFID solutions for healthcare·         healthcare decision analytics and support systems·         epidemiological surveillance systems and intervention modeling·         consumer and clinician health information needs, seeking, sharing, and use·         semantic Web, linked data, and ontology·         collaboration technologies for healthcare·         assistive and adaptive ubiquitous computing technologies·         statistics and quality of medical data·         healthcare delivery in developing countries·         health systems modeling and simulation·         computer-aided diagnosis
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