scholarly journals Digital contact tracing for COVID-19 epidemic emergency management—A case study based on graph database algorithm (Preprint)

2020 ◽  
Author(s):  
Zijun Mao ◽  
Hong Yao ◽  
Qi Zou ◽  
Weiting Zhang ◽  
Ying Dong
Keyword(s):  
Big Data ◽  
High Risk ◽  
Emergency Management ◽  
Coordination Mechanism ◽  
Contact Tracing ◽  
Hainan Province ◽  
Graph Database ◽  
Public Places ◽  
Data Platform ◽  
Centralized Model

BACKGROUND The Coronavirus Disease 2019 (COVID-19) epidemic is still spreading globally. Contact tracing is a vital strategy in epidemic emergency management, but traditional contact tracing faces many limitations in practice. The application of digital technology provides an opportunity for local government to trace the contacts of COVID-19 cases more comprehensively, efficiently, and precisely. OBJECTIVE Hainan Province, China was selected in this case study for the introduction of a new digital contact tracing method under the centralized model, that is, using graph database algorithm, to analyze multi-source COVID-19 epidemic data to achieve contact tracing on the government’s big data platform. Our research hoped to provide new solutions to break through the limitations of traditional contact tracing by introducing the organizational process, technical process, and main achievements of the digital contact tracing in Hainan Province. METHODS Graph database algorithm, which can efficiently process complex relational networks, was applied in Hainan Province, which relies on the government’s big data platform, to analyze multi-source COVID-19 epidemic data and build networks of the relationship 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 multi-source epidemic data were realized based on the government’s big data platform using a centralized model. The graph database algorithm is compatible and can analyze multi-source and heterogeneous epidemic big data. These practices quickly and accurately identified and traced 10,871 contacts among hundreds of thousands of epidemic data records and identified 378 most-close contacts and a batch of high-risk infected public places. A confirmed patient was found after quarantine measures were implemented on all contacts. CONCLUSIONS 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 multi-source epidemic data were realized based on the government’s big data platform using a centralized model. The graph database algorithm is compatible and can analyze multi-source and heterogeneous epidemic big data. These practices quickly and accurately identified and traced 10,871 contacts among hundreds of thousands of epidemic data records and identified 378 most-close contacts and a batch of high-risk infected public places. A confirmed patient was found after quarantine measures were implemented on all contacts.

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

10.2196/26836 ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. e26836
Author(s):  
Zijun Mao ◽  
Hong Yao ◽  
Qi Zou ◽  
Weiting Zhang ◽  
Ying Dong
Keyword(s):  
Big Data ◽  
High Risk ◽  
Emergency Management ◽  
Contact Tracing ◽  
Hainan Province ◽  
Graph Database ◽  
Technical Process ◽  
Public Places ◽  
Data Platform ◽  
Centralized Model

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.

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

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zijun Mao ◽  
Qi Zou ◽  
Hong Yao ◽  
Jingyi Wu
Keyword(s):  
Big Data ◽  
Emergency Management ◽  
Local Governments ◽  
Management Strategies ◽  
Application Framework ◽  
Hainan Province ◽  
Technology Application ◽  
Management Measures ◽  
Big Data Technology

Abstract 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.

Construction of a multi-source heterogeneous hybrid platform for big data

2021 ◽  
pp. 1-10
Author(s):  
Ying Wang ◽  
Yiding Liu ◽  
Minna Xia
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Forest Fire ◽  
Original Data ◽  
Big Data Analysis ◽  
Multiple Sources ◽  
Data Types ◽  
Fire Monitoring ◽  
Data Platform

Big data is featured by multiple sources and heterogeneity. Based on the big data platform of Hadoop and spark, a hybrid analysis on forest fire is built in this study. This platform combines the big data analysis and processing technology, and learns from the research results of different technical fields, such as forest fire monitoring. In this system, HDFS of Hadoop is used to store all kinds of data, spark module is used to provide various big data analysis methods, and visualization tools are used to realize the visualization of analysis results, such as Echarts, ArcGIS and unity3d. Finally, an experiment for forest fire point detection is designed so as to corroborate the feasibility and effectiveness, and provide some meaningful guidance for the follow-up research and the establishment of forest fire monitoring and visualized early warning big data platform. However, there are two shortcomings in this experiment: more data types should be selected. At the same time, if the original data can be converted to XML format, the compatibility is better. It is expected that the above problems can be solved in the follow-up research.

A monitoring framework for transparency and fairness in big data platform

2021 ◽  
Author(s):  
Karima Aslaoui Mokhtari ◽  
Salima Benbernou ◽  
Mourad Ouziri ◽  
Hakim Lahmar ◽  
Muhammad Younas
Keyword(s):  
Big Data ◽  
Data Platform ◽  
Monitoring Framework

scholarly journals Investigation of Four Clusters of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Rwanda, 2020

2021 ◽  
Vol 18 (13) ◽  
pp. 7018
Author(s):  
Olivier Nsekuye ◽  
Edson Rwagasore ◽  
Marie Aime Muhimpundu ◽  
Ziad El-Khatib ◽  
Daniel Ntabanganyimana ◽  
...  
Keyword(s):  
High Risk ◽  
Severe Acute Respiratory Syndrome ◽  
Symptom Onset ◽  
Attack Rate ◽  
Positive Sample ◽  
Contact Tracing ◽  
Face To Face ◽  
Laboratory Confirmation ◽  
Closed Environment ◽  
Index Cases

We reported the findings of the first Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) four clusters identified in Rwanda. Case-investigations included contact elicitation, testing, and isolation/quarantine of confirmed cases. Socio-demographic and clinical data on cases and contacts were collected. A confirmed case was a person with laboratory confirmation of SARS-CoV-2 infection (PCR) while a contact was any person who had contact with a SARS-CoV-2 confirmed case within 72 h prior, to 14 days after symptom onset; or 14 days before collection of the laboratory-positive sample for asymptomatic cases. High risk contacts were those who had come into unprotected face-to-face contact or had been in a closed environment with a SARS-CoV-2 case for >15 min. Forty cases were reported from four clusters by 22 April 2020, accounting for 61% of locally transmitted cases within six weeks. Clusters A, B, C and D were associated with two nightclubs, one house party, and different families or households living in the same compound (multi-family dwelling). Thirty-six of the 1035 contacts tested were positive (secondary attack rate: 3.5%). Positivity rates were highest among the high-risk contacts compared to low-risk contacts (10% vs. 2.2%). Index cases in three of the clusters were imported through international travelling. Fifteen of the 40 cases (38%) were asymptomatic while 13/25 (52%) and 8/25 (32%) of symptomatic cases had a cough and fever respectively. Gatherings in closed spaces were the main early drivers of transmission. Systematic case-investigations contact tracing and testing likely contributed to the early containment of SARS-CoV-2 in Rwanda.

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