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Measurement Method for Evaluating the Lockdown Policies during the COVID-19 Pandemic

Author

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  • Mohammed Al Zobbi

    (School of Computer, Data and Mathematical Sciences, Western Sydney University, Parramatta South Campus, Sydney 2116 NSW, Australia
    Mohammed Al Zobbi and Belal Alsinglawi have contributed equally.)

  • Belal Alsinglawi

    (School of Computer, Data and Mathematical Sciences, Western Sydney University, Parramatta South Campus, Sydney 2116 NSW, Australia
    Mohammed Al Zobbi and Belal Alsinglawi have contributed equally.)

  • Omar Mubin

    (School of Computer, Data and Mathematical Sciences, Western Sydney University, Parramatta South Campus, Sydney 2116 NSW, Australia)

  • Fady Alnajjar

    (College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, UAE)

Abstract

Coronavirus Disease 2019 (COVID-19) has affected day to day life and slowed down the global economy. Most countries are enforcing strict quarantine to control the havoc of this highly contagious disease. Since the outbreak of COVID-19, many data analyses have been done to provide close support to decision-makers. We propose a method comprising data analytics and machine learning classification for evaluating the effectiveness of lockdown regulations. Lockdown regulations should be reviewed on a regular basis by governments, to enable reasonable control over the outbreak. The model aims to measure the efficiency of lockdown procedures for various countries. The model shows a direct correlation between lockdown procedures and the infection rate. Lockdown efficiency is measured by finding a correlation coefficient between lockdown attributes and the infection rate. The lockdown attributes include retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, residential, and schools. Our results show that combining all the independent attributes in our study resulted in a higher correlation (0.68) to the dependent value Interquartile 3 (Q3). Mean Absolute Error (MAE) was found to be the least value when combining all attributes.

Suggested Citation

  • Mohammed Al Zobbi & Belal Alsinglawi & Omar Mubin & Fady Alnajjar, 2020. "Measurement Method for Evaluating the Lockdown Policies during the COVID-19 Pandemic," IJERPH, MDPI, vol. 17(15), pages 1-9, August.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:15:p:5574-:d:393467
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    References listed on IDEAS

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    1. Peng Zhou & Xing-Lou Yang & Xian-Guang Wang & Ben Hu & Lei Zhang & Wei Zhang & Hao-Rui Si & Yan Zhu & Bei Li & Chao-Lin Huang & Hui-Dong Chen & Jing Chen & Yun Luo & Hua Guo & Ren-Di Jiang & Mei-Qin L, 2020. "Addendum: A pneumonia outbreak associated with a new coronavirus of probable bat origin," Nature, Nature, vol. 588(7836), pages 6-6, December.
    2. Peng Zhou & Xing-Lou Yang & Xian-Guang Wang & Ben Hu & Lei Zhang & Wei Zhang & Hao-Rui Si & Yan Zhu & Bei Li & Chao-Lin Huang & Hui-Dong Chen & Jing Chen & Yun Luo & Hua Guo & Ren-Di Jiang & Mei-Qin L, 2020. "A pneumonia outbreak associated with a new coronavirus of probable bat origin," Nature, Nature, vol. 579(7798), pages 270-273, March.
    3. Andrew Atkeson, 2020. "What Will be the Economic Impact of COVID-19 in the US? Rough Estimates of Disease Scenarios," Staff Report 595, Federal Reserve Bank of Minneapolis.
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    Cited by:

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    2. Yulin Hswen & Elad Yom-Tov & Vaidhy Murti & Nicholas Narsing & Siona Prasad & George W. Rutherford & Kirsten Bibbins-Domingo, 2022. "Covidseeker: A Geospatial Temporal Surveillance Tool," IJERPH, MDPI, vol. 19(3), pages 1-10, January.
    3. Dimitris Zavras, 2021. "Feeling Uncertainty during the Lockdown That Commenced in March 2020 in Greece," IJERPH, MDPI, vol. 18(10), pages 1-10, May.
    4. Khalid A. Kheirallah & Belal Alsinglawi & Abdallah Alzoubi & Motasem N. Saidan & Omar Mubin & Mohammed S. Alorjani & Fawaz Mzayek, 2020. "The Effect of Strict State Measures on the Epidemiologic Curve of COVID-19 Infection in the Context of a Developing Country: A Simulation from Jordan," IJERPH, MDPI, vol. 17(18), pages 1-11, September.
    5. Galetsi, Panagiota & Katsaliaki, Korina & Kumar, Sameer, 2022. "The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19," Social Science & Medicine, Elsevier, vol. 301(C).
    6. Xue Zhang & Mildred E. Warner, 2020. "COVID-19 Policy Differences across US States: Shutdowns, Reopening, and Mask Mandates," IJERPH, MDPI, vol. 17(24), pages 1-17, December.

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