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Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study.
JMIR Mhealth Uhealth. 2021 01 22; 9(1):e26836.JM

Abstract

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.

Links

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Authors+Show Affiliations

Mao Z
College of Public Administration, Huazhong University of Science and Technology, Wuhan, China. Non-traditional Security Research Center, Huazhong University of Science and Technology, Wuhan, China.
Yao H
College of Public Administration, Huazhong University of Science and Technology, Wuhan, China. Non-traditional Security Research Center, Huazhong University of Science and Technology, Wuhan, China.
Zou Q
College of Public Administration, Huazhong University of Science and Technology, Wuhan, China. Non-traditional Security Research Center, Huazhong University of Science and Technology, Wuhan, China.
Zhang W
College of Public Administration, Huazhong University of Science and Technology, Wuhan, China. Non-traditional Security Research Center, Huazhong University of Science and Technology, Wuhan, China.
Dong Y
College of Public Administration, Huazhong University of Science and Technology, Wuhan, China. Non-traditional Security Research Center, Huazhong University of Science and Technology, Wuhan, China. School of Law and Humanities, China University of Mining and Technology, Beijing, China.

MeSH

AlgorithmsBig DataCOVID-19ChinaComputer GraphicsContact TracingData VisualizationDatabases, FactualDigital TechnologyEpidemicsHumans

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

33460389

Citation

Mao, Zijun, et al. "Digital Contact Tracing Based On a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study." JMIR mHealth and UHealth, vol. 9, no. 1, 2021, pp. e26836.
Mao Z, Yao H, Zou Q, et al. Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study. JMIR Mhealth Uhealth. 2021;9(1):e26836.
Mao, Z., Yao, H., Zou, Q., Zhang, W., & Dong, Y. (2021). Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study. JMIR mHealth and UHealth, 9(1), e26836. https://doi.org/10.2196/26836
Mao Z, et al. Digital Contact Tracing Based On a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study. JMIR Mhealth Uhealth. 2021 01 22;9(1):e26836. PubMed PMID: 33460389.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study. AU - Mao,Zijun, AU - Yao,Hong, AU - Zou,Qi, AU - Zhang,Weiting, AU - Dong,Ying, Y1 - 2021/01/22/ PY - 2020/12/30/received PY - 2021/01/14/accepted PY - 2021/01/14/revised PY - 2021/1/19/pubmed PY - 2021/1/29/medline PY - 2021/1/18/entrez KW - COVID-19 KW - China KW - big data KW - digital contact tracing KW - emergency management KW - graph database KW - visualization SP - e26836 EP - e26836 JF - JMIR mHealth and uHealth JO - JMIR Mhealth Uhealth VL - 9 IS - 1 N2 - 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. SN - 2291-5222 UR - https://www.unboundmedicine.com/medline/citation/33460389/Digital_Contact_Tracing_Based_on_a_Graph_Database_Algorithm_for_Emergency_Management_During_the_COVID_19_Epidemic:_Case_Study_ DB - PRIME DP - Unbound Medicine ER -