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.

Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study.

BACKGROUND: The COVID-19 epidemic is still spreading globally. Contact tracing is a vital strategy in epidemic emergency management; however, traditional contact tracing faces many limitations in practice. The application of digital technology provides an opportunity for local governments to trace the contacts of individuals with COVID-19 more comprehensively, efficiently, and precisely. OBJECTIVE: Our research aimed to provide new solutions to overcome the limitations of traditional contact tracing by introducing the organizational process, technical process, and main achievements of digital contact tracing in Hainan Province. METHODS: A graph database algorithm, which can efficiently process complex relational networks, was applied in Hainan Province; this algorithm relies on a governmental big data platform to analyze multisource COVID-19 epidemic data and build networks of relationships among high-risk infected individuals, the general population, vehicles, and public places to identify and trace contacts. We summarized the organizational and technical process of digital contact tracing in Hainan Province based on interviews and data analyses. RESULTS: An integrated emergency management command system and a multi-agency coordination mechanism were formed during the emergency management of the COVID-19 epidemic in Hainan Province. The collection, storage, analysis, and application of multisource epidemic data were realized based on the government's big data platform using a centralized model. The graph database algorithm is compatible with this platform and can analyze multisource and heterogeneous big data related to the epidemic. These practices were used to quickly and accurately identify and trace 10,871 contacts among hundreds of thousands of epidemic data records; 378 closest contacts and a number of public places with high risk of infection were identified. A confirmed patient was found after quarantine measures were implemented by all contacts. CONCLUSIONS: During the emergency management of the COVID-19 epidemic, Hainan Province used a graph database algorithm to trace contacts in a centralized model, which can identify infected individuals and high-risk public places more quickly and accurately. This practice can provide support to government agencies to implement precise, agile, and evidence-based emergency management measures and improve the responsiveness of the public health emergency response system. Strengthening data security, improving tracing accuracy, enabling intelligent data collection, and improving data-sharing mechanisms and technologies are directions for optimizing digital contact tracing.

Promoting Public Engagement during the COVID-19 Crisis: How Effective Is the Wuhan Local Government's Information Release?

During times of public crises (such as COVID-19), governments must act swiftly to release crisis information effectively and efficiently to the public. This paper provides a general overview of the way that the Wuhan local government use Weibo as a channel to engage with their citizens during the COVID-19 pandemic. Based on the media richness, dialogic loop, and a series of theoretically relevant factors, such as content type, text length, and information source, we try to examine how citizen engage with their local government. By analyzing the data mining samples from Wuhan Release, the official Sina Weibo account of Wuhan's local government, results show that, despite the unstable situation COVID-19 over the crisis, there exist three stages of a crisis on the whole. Combining the behavior of the government and the public, duration from 31 December 2019 to 19 January 2020 could be seen as the development period, then the outbreak period (30 January 2020 to 28 February 2020), and a grace period (29 February 2020 to19 April 2020). Public attention to different types of information changes over time, but curbing rumors has always been a priority. Media richness features partially influent citizen engagement. Text length is significantly positively associated with citizen engagement through government social media. However, posts containing information sources have a negative impact on citizen engagement.

Promoting Public Engagement during the COVID-19 Crisis: How Effective Is the Wuhan Local Government’s Information Release?

During times of public crises (such as COVID-19), governments must act swiftly to release crisis information effectively and efficiently to the public. This paper provides a general overview of the way that the Wuhan local government use Weibo as a channel to engage with their citizens during the COVID-19 pandemic. Based on the media richness, dialogic loop, and a series of theoretically relevant factors, such as content type, text length, and information source, we try to examine how citizen engage with their local government. By analyzing the data mining samples from Wuhan Release, the official Sina Weibo account of Wuhan’s local government, results show that, despite the unstable situation COVID-19 over the crisis, there exist three stages of a crisis on the whole. Combining the behavior of the government and the public, duration from 31 December 2019 to 19 January 2020 could be seen as the development period, then the outbreak period (30 January 2020 to 28 February 2020), and a grace period (29 February 2020 to19 April 2020). Public attention to different types of information changes over time, but curbing rumors has always been a priority. Media richness features partially influent citizen engagement. Text length is significantly positively associated with citizen engagement through government social media. However, posts containing information sources have a negative impact on citizen engagement.