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How Should the Structure of Smart Cities Change to Predict and Overcome a Pandemic?

Author

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  • Jung-Hoon Kim

    (Smart City Research Center, Korea Institute of Civil Engineering and Building Technology, Goyang-si 10223, Korea)

  • Joo-Young Kim

    (Department of Architecture, Sejong University, Seoul 05006, Korea)

Abstract

A proposed countermeasure to COVID-19 is a robust healthcare system that can respond and identify transmission paths using information technology. This involves the use of smart city services for tracking an infected person. However, during the COVID-19 pandemic, the healthcare system could only provide data on the number of infected people. Additionally, smart city services could respond neither timely nor sequentially. This study proposed a method for timely and sequential responses, through a flexible combination of the healthcare system and smart city services by envisioning a scenario that sequentially grafts the current status of COVID-19 in Korea. The results are the following. First, the COVID-19 outbreak was summarized in the context of the healthcare system and current smart city services. A method by which the latter could respond to the various needs of the former was suggested. Second, recommendations on combining or dismissing certain smart city services, as per the needs of coping with COVID-19, were summarized. Third, smart city services must be utilized only for addressing pandemics, as data from the healthcare system consists of personal information. Therefore, smart city services for responding to COVID-19 must be flexible.

Suggested Citation

  • Jung-Hoon Kim & Joo-Young Kim, 2022. "How Should the Structure of Smart Cities Change to Predict and Overcome a Pandemic?," Sustainability, MDPI, vol. 14(5), pages 1-17, March.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2981-:d:763726
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    References listed on IDEAS

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    3. Jung Won Sonn & Myounggu Kang & Yeol Choi, 2020. "Smart city technologies for pandemic control without lockdown," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 24(2), pages 149-151, April.
    4. Zhu, Cheng-Cheng & Zhu, Jiang, 2021. "Dynamic analysis of a delayed COVID-19 epidemic with home quarantine in temporal-spatial heterogeneous via global exponential attractor method," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
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    1. Mohamed Abdel-Basset & Ibrahim Alrashdi & Hossam Hawash & Karam Sallam & Ibrahim A. Hameed, 2023. "Towards Efficient and Trustworthy Pandemic Diagnosis in Smart Cities: A Blockchain-Based Federated Learning Approach," Mathematics, MDPI, vol. 11(14), pages 1-17, July.
    2. Jonathan Gumz & Diego Castro Fettermann & Enzo Morosini Frazzon & Mirko Kück, 2022. "Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions," Sustainability, MDPI, vol. 14(20), pages 1-34, October.
    3. Pedro Ivo Silva-da-Nóbrega & Adriana Fumi Chim-Miki & Marysol Castillo-Palacio, 2022. "A Smart Campus Framework: Challenges and Opportunities for Education Based on the Sustainable Development Goals," Sustainability, MDPI, vol. 14(15), pages 1-17, August.

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