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

Mohammed Al Zobbi, Belal Alsinglawi, Omar Mubin and Fady Alnajjar
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Mohammed Al Zobbi: School of Computer, Data and Mathematical Sciences, Western Sydney University, Parramatta South Campus, Sydney 2116 NSW, Australia
Belal Alsinglawi: School of Computer, Data and Mathematical Sciences, Western Sydney University, Parramatta South Campus, Sydney 2116 NSW, Australia
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

IJERPH, 2020, vol. 17, issue 15, 1-9

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.

Keywords: COVID-19; infectious disease modeling; basic reproduction number; machine learning; government regulations; spread control (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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