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Since the onset of the COVID-19 outbreak in Wuhan, China, numerous forecasting models have been proposed to project the trajectory of coronavirus infection cases. Most of these forecasts are based on epidemiology models that utilize deterministic differential equations and have resulted in widely varying predictions. We propose a new discrete-time Markov chain model that directly incorporates stochastic behavior and for which parameter estimation is straightforward from available data. Using such data from China’s Hubei province (for which Wuhan is the provincial capital city and which accounted for approximately 82% of the total reported COVID-19 cases in the entire country), the model is shown to be flexible, robust, and accurate. As a result, it has been adopted by the first Shanghai assistance medical team in Wuhan’s Jinyintan Hospital, which was the first designated hospital to take COVID-19 patients in the world. The forecast has been used for preparing medical staff, intensive care unit (ICU) beds, ventilators, and other critical care medical resources and for supporting real-time medical management decisions.

The development of mobile Internet, Internet of Things, and cloud computing has contributed to the unprecedented growth of information data. Big data plays a very important role in education. Currently, the literature review and in-depth research on big educational data are not very extensive, mainly involved in two fields: education mining and learning analysis. For a perfect research about education big data, this paper comprehensively reviewed three major aspects (Predictive Analytics, Learning Analytics, and Recommendation Systems) of educational data analytics for an intensive investigation and analysis: (1) Predictive Analytics: It predicts students’ learning performance by tracking students’ learning information and then analyzes students’ learning competence to build an academic early warning system; teachers can be allowed to intervene in students in time and adopts different teaching ways for different students. Therefore, both students’ learning and ability can be individualized and improved; (2) Learning Analytics: This part can identify the learners’ behavior patterns and obtain more implicit learner characteristics by studying the hidden meaning behind learning behaviors and strategies; (3) Recommendation Systems: It can match the needs of learners and recommend appropriate learning resources through different methods. All the above proved that the application of big data technology in education provides powerful data support for the development of education.

The following topics are under this section:

• The future of predicting lifestyle diseases is here in Asia
• Breaking Barriers for Artificial Intelligence (AI) in Healthcare: bridging vision and reality with the language of trust
• Overcoming Challenges of Managing Information in Life Sciences, Towards the Digital Future
• Under the Weather: Cybersecurity Woes in the healthcare Industry

The extensive study was conducted to enhance the prediction of customer turnover in an online retail and distribution organization. The study combines data from surveys, consumer comments, and financial records to uncover themes from textual assessments using state-of-the-art methodologies. Methods such as Dirichlet Multilayer Perceptron Mixing, Latent Dirichlet Allocation and Random Sampling fall within this category. In addition to its usage for assessing geographic data for location-based consumer segmentation, DBSCAN is a crucial tool for this investigation. Model development for churn prediction and root cause analysis makes use of logistic regression and extreme gradient boosting. The statistical and practical benefits of the proposed paradigm are shown via comparison to existing options. A model’s predictive efficacy may be evaluated using the area under the curve or the lift metric. The research also introduces the concept of “Consumer-driven energy-efficient WSNs architecture for Personalization and contextualization in E-commerce Systems,” which suggests using wireless sensor networks (WSNs) to collect data efficiently, provide customized service and provide context for online purchases. Overall, the research demonstrates the effectiveness of machine learning in harnessing consumer input for strategic decision-making, illuminating the potential of creative sensor network integration in enhancing e-commerce personalization and contextualization.

The availability of massive data about sports activities offers nowadays the opportunity to quantify the relation between performance and success. In this study, we analyze more than 6000 games and 10 million events in six European leagues and investigate this relation in soccer competitions. We discover that a team’s position in a competition’s final ranking is significantly related to its typical performance, as described by a set of technical features extracted from the soccer data. Moreover, we find that, while victory and defeats can be explained by the team’s performance during a game, it is difficult to detect draws by using a machine learning approach. We then simulate the outcomes of an entire season of each league only relying on technical data and exploiting a machine learning model trained on data from past seasons. The simulation produces a team ranking which is similar to the actual ranking, suggesting that a complex systems’ view on soccer has the potential of revealing hidden patterns regarding the relation between performance and success.

Nowadays, an increasing number of information technology tools are implemented in order to support decision making about marketing strategies and improve customer relationship management (CRM). Consequently, an improvement in CRM can be obtained by enhancing the databases on which these information technology tools are based. This study shows that data augmentation with situational variables of the purchase occasion can significantly improve purchasing behavior predictions for a home vending company. Three dimensions of situational variables are examined: physical surroundings, temporal perspective and social surroundings respectively represented by weather, time, and salesperson variables. The smallest, but still significant, increase in predictive performance was measured by enhancing the model with time variables. Besides the moment of the day, this study shows that the incorporation of weather variables, and more specifically sunshine, can also improve the accuracy of a CRM model. Finally, the best improvement in purchasing behavior predictions was obtained by taking the salesperson effect into account using a multilevel model.

Knowing how value creation is understood and managed by Big Data-based companies can be a key strategy to boost business. The typical process of identifying value creation in organizations is quite complex, since it involves internal and external factors to them. In Big Data scenarios, we should also consider the uncertainty about future processes of value creation, regarding the trends inferred by predictive analytics processes. Big Data environments operate in a scale of large volumes of parallel-processed data; and aim to generate relevant information that otherwise would be impossible for traditional systems, especially if we expect good performance of transaction speed and of coping with the extensive variety of data types, inherent to such environments. In order to properly identify and work with the idea of value in their businesses, Big Data-based companies have thus far more challenging hurdles to overcome. This paper proposes a survey of the creation of value in such environments. For this purpose, we undertook a theoretical study, which relied in a qualitative method approach of bibliographical, exploratory and descriptive nature. Finally, since this work is still on progress and it is not yet a conclusive proposal, we aim to compare our preliminary results to the typical perception of value creation, found in the literature.

Implementing digital technologies has made organizations more collaborative. Knowledge-based collaborative activities management has become the main organizational model of work. Identifying the factors that influence organizational collaboration is crucial to organizational design and ensuring effective digital transformation. Currently, collaboration has not received enough attention. Enterprises lack an understanding of its importance. This chapter quantitatively studies collaborative activities and their influencing factors. The regression analysis results show that the invisible independent variable such as knowledge-based innovation has a greater effect on organizational collaboration. We further conduct predictive analysis for organizational collaboration, and the predictive analysis results help the two case study companies to clearly understand their current status of organizational collaboration and the level of their respective influencing factors.