Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices

• Main Authors: Rahman, Md. Arafatur, Nafees, Zaman, A. Taufiq, Asyhari, Al-Turjman, Fadi, Alam Bhuiyan, Md. Zakirul, Mohamad Fadli, Zolkipli
• Format: Article
• Language: English; English
• Published: Elsevier Ltd 2020
• Subjects: QA76 Computer software
• Online Access: http://umpir.ump.edu.my/id/eprint/29601/1/Data-driven%20dynamic%20clustering%20framework%20for%20mitigating%20the%20adverse%20.pdf; http://umpir.ump.edu.my/id/eprint/29601/2/Data-driven%20dynamic%20clustering%20framework%20for%20mitigating%20the%20adverse_FULL.pdf; http://umpir.ump.edu.my/id/eprint/29601/; https://doi.org/10.1016/j.scs.2020.102372; https://doi.org/10.1016/j.scs.2020.102372

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 saf...