A Cloud Based Intelligent Model for Predicting the Pre-Testing Corona Virus Infection Outbreak

Corona virus disease (COVID) is a transmittable disease caused by a newly discovered corona virus. For this a system is require which trace the location and predict the health of the people. In the present study, a cloud based a model is proposed. The proposed model will be connect with a cloud computing system that will predict the corona virus infected patients using naïve bayes classifier and provides geographic based danger areas to prevent the spreading of corona virus. This way will provide the great help to the local administration and health care agencies to control the spreading of covid. © 2022 IEEE.

Design of Intelligent Management Platform for Industry–Education Cooperation of Vocational Education by Data Mining

Data are playing an increasingly important role in the development of industry–education cooperation strategies in vocational education and training. The objective of this study was to promote the comprehensive progress of an industry–education cooperation system and improve the effect of the application of big data technology in this system. First, we designed of a big data technology application in an intelligent management platform system for industry–education cooperation. Second, we analyzed the synthetical design of the system. Finally, we optimized and designed a support vector machine (SVM) data mining (DM) algorithm model based on big data, and evaluated the model. The results revealed that the designed algorithm model provides outstanding advantages compared with similar algorithm models. In general, the highest average computation time of the designed SVM algorithm model is about 95 ms. The overall average calculation time linearly decreases around 200 iterations and tends to be stable, and the lowest overall average computation time is about 20 ms. In the DM process, the highest accuracy rate of the model is about 97%, and the lowest is about 92%. The DM accuracy rate is always stable as the number of iterations of the model continues to increase. The designed model slowly increases the occupancy rate of the system in the process of increasing computing time. At about 60 min, the system occupancy rate of the model tends to be stable, and the highest is maintained at about 23%. This study not only provides technical support for the optimization of DM algorithms with big data technology, but also contributes to the integrated development of industry–education cooperation systems.

Prediction model of global COVID-19 based on big data technology

In order to overcome the trend influence of novel Coronavirus epidemic in the future, this paper proposes the panel data modeling method based on big data crawler technology, which is based on Python crawler technology to obtain a more effective estimation model from the dynamic perspective of time and cross section. The results showed that the fixed effect error rate established by the development of COVID-19 in China, Japan, South Korea, Germany and Italy was about 3%, and there is a positive correlation between cured cases and confirmed cases of COVID-19. The predicted confirmed cases of COVID-19 in week 63 will be 69, 11,908, 3156, 112293 and 147,545, respectively. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

Application and Prospect of Big Data in the Prevention and Control of Major Epidemics

In recent years, various epidemic viruses have seriously threatened the safety of human life. Big data network technology follows the law of network evolution and is an inevitable choice for effective prevention and control of major epidemics. This article takes the epidemic data at the time of the COVID-19 outbreak as the research object, and analyzes the application of big data technology to people's travel, communication and life in the epidemic from the perspective of big data. This article analyzes the correlation between crowd activities and the spread of the epidemic, the crowd mobile network model, the spatial clustering of cases, and the development trend of the epidemic. The results of the study showed that the number of online communities increased from 17 before the outbreak to 21 after the outbreak, and the average community space was reduced to 80.95% before the outbreak. The ratio of the amount of activity between communities to the amount of activities within the community was changed from before the outbreak. The reduction of 0.31 from 0.31 to 0.20 after the outbreak indicates that the scope of crowd activities has shrunk after the outbreak, and population activities among communities have weakened. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The application framework of big data technology in the COVID-19 epidemic emergency management in local government-a case study of Hainan Province, China.

BACKGROUND: As COVID-19 continues to spread globally, traditional emergency management measures are facing many practical limitations. The application of big data analysis technology provides an opportunity for local governments to conduct the COVID-19 epidemic emergency management more scientifically. The present study, based on emergency management lifecycle theory, includes a comprehensive analysis of the application framework of China's SARS epidemic emergency management lacked the support of big data technology in 2003. In contrast, this study first proposes a more agile and efficient application framework, supported by big data technology, for the COVID-19 epidemic emergency management and then analyses the differences between the two frameworks. METHODS: This study takes Hainan Province, China as its case study by using a file content analysis and semistructured interviews to systematically comprehend the strategy and mechanism of Hainan's application of big data technology in its COVID-19 epidemic emergency management. RESULTS: Hainan Province adopted big data technology during the four stages, i.e., migration, preparedness, response, and recovery, of its COVID-19 epidemic emergency management. Hainan Province developed advanced big data management mechanisms and technologies for practical epidemic emergency management, thereby verifying the feasibility and value of the big data technology application framework we propose. CONCLUSIONS: This study provides empirical evidence for certain aspects of the theory, mechanism, and technology for local governments in different countries and regions to apply, in a precise, agile, and evidence-based manner, big data technology in their formulations of comprehensive COVID-19 epidemic emergency management strategies.

Big Data Technology Applications and the Right to Health in China during the COVID-19 Pandemic.

Individuals have the right to health according to the Constitution and other laws in China. Significant barriers have prevented the full realisation of the right to health in the COVID-19 era. Big data technology, which is a vital tool for COVID-19 containment, has been a central topic of discussion, as it has been used to protect the right to health through public health surveillance, contact tracing, real-time epidemic outbreak monitoring, trend forecasting, online consultations, and the allocation of medical and health resources in China. Big data technology has enabled precise and efficient epidemic prevention and control and has improved the efficiency and accuracy of the diagnosis and treatment of this new form of coronavirus pneumonia due to Chinese institutional factors. Although big data technology has successfully supported the containment of the virus and protected the right to health in the COVID-19 era, it also risks infringing on individual privacy rights. Chinese policymakers should understand the positive and negative impacts of big data technology and should prioritise the Personal Information Protection Law and other laws that are meant to protect and strengthen the right to privacy.