Measurement method for evaluating the lockdown policies during the COVID-19 pandemic

Mohammed Al Zobbi, Belal Alsinglawi, Omar Mubin, Fady Alnajjar

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

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.

Original languageEnglish
Article number5574
Pages (from-to)1-9
Number of pages9
JournalInternational Journal of Environmental Research and Public Health
Volume17
Issue number15
DOIs
Publication statusPublished - Aug 1 2020

Keywords

  • Basic reproduction number
  • COVID-19
  • Government regulations
  • Infectious disease modeling
  • Machine learning
  • Spread control

ASJC Scopus subject areas

  • Pollution
  • Public Health, Environmental and Occupational Health
  • Health, Toxicology and Mutagenesis

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