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Article

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

1
School of Computer, Data and Mathematical Sciences, Western Sydney University, Parramatta South Campus, Sydney 2116 NSW, Australia
2
College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, UAE
*
Author to whom correspondence should be addressed.
Mohammed Al Zobbi and Belal Alsinglawi have contributed equally.
Int. J. Environ. Res. Public Health 2020, 17(15), 5574; https://doi.org/10.3390/ijerph17155574
Received: 20 June 2020 / Revised: 15 July 2020 / Accepted: 28 July 2020 / Published: 2 August 2020
(This article belongs to the Special Issue Transmission Dynamics of Novel Coronavirus Disease 2019 (COVID-19))
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. View Full-Text
Keywords: COVID-19; infectious disease modeling; basic reproduction number; machine learning; government regulations; spread control COVID-19; infectious disease modeling; basic reproduction number; machine learning; government regulations; spread control
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MDPI and ACS Style

Al Zobbi, M.; Alsinglawi, B.; Mubin, O.; Alnajjar, F. Measurement Method for Evaluating the Lockdown Policies during the COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2020, 17, 5574. https://doi.org/10.3390/ijerph17155574

AMA Style

Al Zobbi M, Alsinglawi B, Mubin O, Alnajjar F. Measurement Method for Evaluating the Lockdown Policies during the COVID-19 Pandemic. International Journal of Environmental Research and Public Health. 2020; 17(15):5574. https://doi.org/10.3390/ijerph17155574

Chicago/Turabian Style

Al Zobbi, Mohammed, Belal Alsinglawi, Omar Mubin, and Fady Alnajjar. 2020. "Measurement Method for Evaluating the Lockdown Policies during the COVID-19 Pandemic" International Journal of Environmental Research and Public Health 17, no. 15: 5574. https://doi.org/10.3390/ijerph17155574

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