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Information Journal Paper

Title

Data Architecture of Coronavirus Disease 2019 Surveillance System: A Systematic Review

Pages

  47-61

Abstract

 Introduction: Management and control of Reportable diseases are challenging because these diseases include a large spectrum of infectious conditions that need accurate, precise, and timely reporting. To deal with this problem, an integrated Surveillance system using the set of core Data architecture principles is crucial to ensure the effective management of data. Therefore, this study aimed to identify the core Data architecture requirements for effective management of Coronavirus Disease 2019 (COVID-19), followed by designing a Data architecture model. Materials & Methods: This systematic review was conducted in 2020 through searching five databases, including PubMed, Web of Science, Science Direct, and Scopus M, as well as Google scholar search engine to identify metrics for COVID-19 Data architecture designing. Moreover, the search formula definition, implication of inclusion and exclusion criteria, search filtering adjustment, and related study identification were performed in this study. Subsequently, the cases identifying the architecture data of the COVID-19 component were systematically extracted and categorized in suitable classes. Finally, the management system of the architecture model of the patient was visualized in this study. Findings: Out of 398 identified studies, 27 articles met the inclusion criteria. The obtained data were categorized into five classes, including organizations involved in data management (data producer, data users, and decision-makers), data sources, information requirements (11 information classes and 77 data elements), standards (semantic and syntactic), and control quality criteria of the data. Discussions & Conclusions: Implementation of customized Data architecture for COVID-19 can increase the potential of the health care systems to prevent the high prevalence of this disease and improve the quality of care through timely and effective health monitoring, accurate epidemiological investigations, clinical decision supports, and health-care policymaking.

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    APA: Copy

    Shanbehzadeh, M., Kazemi Arpanahi, H., Nopour, R., Haqiri, H., MOBASHERI, F., & Bazvandnezhad, Z.. (2021). Data Architecture of Coronavirus Disease 2019 Surveillance System: A Systematic Review. JOURNAL OF ILAM UNIVERSITY OF MEDICAL SCIENCES, 28(6 ), 47-61. SID. https://sid.ir/paper/415619/en

    Vancouver: Copy

    Shanbehzadeh M., Kazemi Arpanahi H., Nopour R., Haqiri H., MOBASHERI F., Bazvandnezhad Z.. Data Architecture of Coronavirus Disease 2019 Surveillance System: A Systematic Review. JOURNAL OF ILAM UNIVERSITY OF MEDICAL SCIENCES[Internet]. 2021;28(6 ):47-61. Available from: https://sid.ir/paper/415619/en

    IEEE: Copy

    M. Shanbehzadeh, H. Kazemi Arpanahi, R. Nopour, H. Haqiri, F. MOBASHERI, and Z. Bazvandnezhad, “Data Architecture of Coronavirus Disease 2019 Surveillance System: A Systematic Review,” JOURNAL OF ILAM UNIVERSITY OF MEDICAL SCIENCES, vol. 28, no. 6 , pp. 47–61, 2021, [Online]. Available: https://sid.ir/paper/415619/en

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