ArticleSustainable cities and society2020
Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices.
Article in Sustainable cities and society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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52 citing papers in PubMed, 174 citations in OpenAlex.
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- Mapping the pandemic: a review of Geographical Information Systems-based spatial modeling of Covid-19.Journal of public health in Africa · 2023Article
- COVID-19 and Sustainable Development Goals (SDGs): Scenario analysis through fuzzy cognitive map modeling.Gondwana research : international geoscience journal · 2023Article
- Multi-modal fusion of deep transfer learning based COVID-19 diagnosis and classification using chest x-ray images.Multimedia tools and applications · 2023Article
- A data-driven framework to assess population dynamics during novel coronavirus outbreaks: A case study on Xiamen Island, China.PloS one · 2023Article
- Time series forecasting of COVID-19 transmission in Asia Pacific countries using deep neural networks.Personal and ubiquitous computing · 2023Article
- Do governmental policy interventions help urban economic recovery? Experimental evidence from China's provinces governance amid the COVID-19 pandemic.Growth and change · 2022Article
- Analysis of the socioeconomic impact due to COVID-19 using a deep clustering approach.Applied soft computing · 2022Article
- An Intelligent Algorithm to Predict GDP Rate and Find a Relationship Between COVID-19 Outbreak and Economic Downturn.Computational economics · 2022Article
- IoT-Based Humanoid Software for Identification and Diagnosis of Covid-19 Suspects.IEEE sensors journal · 2022Article
- Alteration of dietary habits and lifestyle pattern during COVID-19 pandemic associated lockdown: An online survey study.Clinical nutrition ESPEN · 2022Article
- Pandemic coronavirus disease (Covid-19): World effects analysis and prediction using machine-learning techniques.Expert systems · 2022Article
- Local binary pattern and deep learning feature extraction fusion for COVID-19 detection on computed tomography images.Expert systems · 2022Article
- Social practices and energy use at home during the first Italian lockdown due to Covid-19.Sustainable cities and society · 2022Article
- Discovering spatial-temporal patterns via complex networks in investigating COVID-19 pandemic in the United States.Sustainable cities and society · 2022Article
- Outbreak COVID-19 in Medical Image Processing Using Deep Learning: A State-of-the-Art Review.Archives of computational methods in engineering : state of the art reviews · 2022Review
- Knowledge into the Practice against COVID-19: A Cross-Sectional Study from Ghana.International journal of environmental research and public health · 2021Article
- Discovering optimal strategies for mitigating COVID-19 spread using machine learning: Experience from Asia.Sustainable cities and society · 2021Article
- Removal of SARS-CoV-2 using UV+Filter in built environment.Sustainable cities and society · 2021Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 disease has once again reiterated the impact of pandemics beyond a biomedical event with potential rapid, dramatic, sweeping disruptions to the management, and conduct of everyday life. Not only the rate and pattern of contagion that threaten our sense of healthy living but also the safety measures put in place for containing the spread of the virus may require social distancing. Three different measures to counteract this pandemic situation have emerged, namely: (i) vaccination, (ii) herd immunity development, and (iii) lockdown. As the first measure is not ready at this stage and the second measure is largely considered unreasonable on the account of the gigantic number of fatalities, a vast majority of countries have practiced the third option despite having a potentially immense adverse economic impact. To mitigate such an impact, this paper proposes a data-driven dynamic clustering framework for moderating the adverse economic impact of COVID-19 flare-up. Through an intelligent fusion of healthcare and simulated mobility data, we model lockdown as a clustering problem and design a dynamic clustering algorithm for localized lockdown by taking into account the pandemic, economic and mobility aspects. We then validate the proposed algorithms by conducting extensive simulations using the Malaysian context as a case study. The findings signify the promises of dynamic clustering for lockdown coverage reduction, reduced economic loss, and military unit deployment reduction, as well as assess potential impact of uncooperative civilians on the contamination rate. The outcome of this work is anticipated to pave a way for significantly reducing the severe economic impact of the COVID-19 spreading. Moreover, the idea can be exploited for potentially the next waves of corona virus-related diseases and other upcoming viral life-threatening calamities.
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