tetano
Editor, Senior Moderator
JMIR Mhealth Uhealth
. 2021 Jan 14.
doi: 10.2196/26836. Online ahead of print.
Digital contact tracing for COVID-19 epidemic emergency management-A case study based on graph database algorithm
Zijun Mao[SUP] 1 [/SUP], Hong Yao[SUP] 2 [/SUP], Qi Zou[SUP] 2 [/SUP], Weiting Zhang[SUP] 2 [/SUP], Ying Dong[SUP] 3 [/SUP]
Affiliations
Abstract
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: Our research aimed 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: During the emergency management of the COVID-19 epidemic, Hainan Province used graph database algorithm to trace contacts in a centralized model, which can identify high-risk infected individuals and public places more quickly and accurately. The 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, making data collection intelligent, and improving data sharing mechanisms and technologies are the directions for optimizing digital contact tracing.
. 2021 Jan 14.
doi: 10.2196/26836. Online ahead of print.
Digital contact tracing for COVID-19 epidemic emergency management-A case study based on graph database algorithm
Zijun Mao[SUP] 1 [/SUP], Hong Yao[SUP] 2 [/SUP], Qi Zou[SUP] 2 [/SUP], Weiting Zhang[SUP] 2 [/SUP], Ying Dong[SUP] 3 [/SUP]
Affiliations
- PMID: 33460389
- DOI: 10.2196/26836
Abstract
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: Our research aimed 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: During the emergency management of the COVID-19 epidemic, Hainan Province used graph database algorithm to trace contacts in a centralized model, which can identify high-risk infected individuals and public places more quickly and accurately. The 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, making data collection intelligent, and improving data sharing mechanisms and technologies are the directions for optimizing digital contact tracing.