Background In the prevention and control of infectious diseases, previous research on the application of big data technology has mainly focused on the early warning and early monitoring of infectious diseases. Although the application of big data technology for COVID-19 warning and monitoring remain important tasks, prevention of the disease’s rapid spread and reduction of its impact on society are currently the most pressing challenges for the application of big data technology during the COVID-19 pandemic. After the outbreak of COVID-19 in Wuhan, the Chinese government and nongovernmental organizations actively used big data technology to prevent, contain, and control the spread of COVID-19. Objective The aim of this study is to discuss the application of big data technology to prevent, contain, and control COVID-19 in China; draw lessons; and make recommendations. Methods We discuss the data collection methods and key data information that existed in China before the outbreak of COVID-19 and how these data contributed to the prevention and control of COVID-19. Next, we discuss China’s new data collection methods and new information assembled after the outbreak of COVID-19. Based on the data and information collected in China, we analyzed the application of big data technology from the perspectives of data sources, data application logic, data application level, and application results. In addition, we analyzed the issues, challenges, and responses encountered by China in the application of big data technology from four perspectives: data access, data use, data sharing, and data protection. Suggestions for improvements are made for data collection, data circulation, data innovation, and data security to help understand China’s response to the epidemic and to provide lessons for other countries’ prevention and control of COVID-19. Results In the process of the prevention and control of COVID-19 in China, big data technology has played an important role in personal tracking, surveillance and early warning, tracking of the virus’s sources, drug screening, medical treatment, resource allocation, and production recovery. The data used included location and travel data, medical and health data, news media data, government data, online consumption data, data collected by intelligent equipment, and epidemic prevention data. We identified a number of big data problems including low efficiency of data collection, difficulty in guaranteeing data quality, low efficiency of data use, lack of timely data sharing, and data privacy protection issues. To address these problems, we suggest unified data collection standards, innovative use of data, accelerated exchange and circulation of data, and a detailed and rigorous data protection system. Conclusions China has used big data technology to prevent and control COVID-19 in a timely manner. To prevent and control infectious diseases, countries must collect, clean, and integrate data from a wide range of sources; use big data technology to analyze a wide range of big data; create platforms for data analyses and sharing; and address privacy issues in the collection and use of big data.

Development of cross border e-commerce (CBEC) is one of the major enablers of SMEs access to overseas market opportunities. The boom in China's cross border e-commerce has been exponential. It has made China the world's largest cross-border market with over 200 million cross-border online consumers. This article explores the opportunities and the challenges for SMEs to leverage the China's CBEC to find and develop new customers in markets outside their domestic ones. It aims to help small to medium enterprises understand the unique Chinese e-commerce ecosystem and develop an appropriate e-commerce strategy that enables them to enter the Chinese e-commerce ecosystem. This research reveals that the significant growth opportunities and challenges for those SMEs poised for business expansion into Chinese market via CBEC and offers a detailed understanding of the China's cross border ecommerce system. SMEs are a major and important part of the world economy in terms of making contribution to Gross Domestic Product (GDP) and employment. It is important to help SMEs to understand the significance of cross border e-commerce ecosystem and develop an appropriate e-commerce strategy that enables them to engage with CBEC and empower them to maximize the opportunities offered by CBEC in China. This article recommends that SMEs take a proactive approach to developing effective digital marketing strategies to engage with Chinese online consumers and keep up with new policies, programs and regulatory changes introduced by Chinese governments in a fast changing digital environment. It also suggests that SMEs consider cooperation with an online retailer who is present on China's cross border B2C platforms to yield better results for brand building and direct sale.

No abstract

No abstract

Based on data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS), this paper calculates the health distribution of the elderly using the Quality of Well-Being Scale (QWB) score, and then estimates health inequality among the elderly in rural China using the Wagstaff index (WI) and Erreygers index (EI). Following this, it compares health inequalities among the elderly in different age groups, and finally, uses the Shapley and recentered influence function-index-ordinary least squares (RIF-I-OLS) model to decompose the effect of four factors on health inequality among the elderly in rural China. The QWB score distribution shows that the health of the elderly in rural China improved with social economic development and medical reform from 2002 to 2014. However, at the same time, we were surprised to find that the health level of the 65–74 years old group has been declining steadily since 2008. This phenomenon implies that the incidence of chronic diseases is moving towards the younger elderly. The WI and EI show that there is indeed pro-rich health inequality among the rural elderly, the health inequality of the younger age groups is more serious than that of the older age groups, and the former incidence of health inequality is higher. Health inequality in the age group of 65–74 years old is higher than that in other groups, and the trend of change fluctuated downward from 2002 to 2014. Health inequality in the age group of 75–84 years old is lower than that in the group of 65–74 years old, but higher than that in the other age groups. The results of Shapley decomposition show that demographic characteristics, socioeconomic status (SES), health care access, and quality of later life contributed 0.0054, 0.0130, 0.0442, and 0.0218 to the health inequality index of the elderly, which accounted for 6.40%, 15.39%, 52.41%, and 25.80% of health inequality index. From the results of RIF-I-OLS decomposition, this paper has analyzed detailed factors’ marginal effects on health inequality from four dimensions, which indicates that the health inequality among the elderly in rural China was mainly caused by the disparity of income, medical expenses, and living arrangement.