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Exploring the Impact of Government Interventions on COVID-19 Pandemic Spread in Kuwait

Exploring the Impact of Government Interventions on COVID-19 Pandemic Spread in Kuwait

Sana S. BuHamra, Jehad Al Dallal
Copyright: © 2021 |Volume: 16 |Issue: 4 |Pages: 19
ISSN: 1555-3396|EISSN: 1555-340X|EISBN13: 9781799859819|DOI: 10.4018/IJHISI.288893
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MLA

Sana S. BuHamra, and Jehad Al Dallal. "Exploring the Impact of Government Interventions on COVID-19 Pandemic Spread in Kuwait." IJHISI vol.16, no.4 2021: pp.1-19. http://doi.org/10.4018/IJHISI.288893

APA

Sana S. BuHamra & Al Dallal, J. (2021). Exploring the Impact of Government Interventions on COVID-19 Pandemic Spread in Kuwait. International Journal of Healthcare Information Systems and Informatics (IJHISI), 16(4), 1-19. http://doi.org/10.4018/IJHISI.288893

Chicago

Sana S. BuHamra, and Jehad Al Dallal. "Exploring the Impact of Government Interventions on COVID-19 Pandemic Spread in Kuwait," International Journal of Healthcare Information Systems and Informatics (IJHISI) 16, no.4: 1-19. http://doi.org/10.4018/IJHISI.288893

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Abstract

To model the trajectory of the pandemic in Kuwait from February 24, 2020 to February 28, 2021, we used two modeling procedures: Auto Regressive Integrated Moving Average (ARIMA) with structural breaks and Multivariate Adaptive Regression Splines (MARS), and then mapped the key breakpoints of the models to the set of government-enforced interventions. The MARS model, as opposed to the ARIMA model, provides a more precise interpretation of the intervention's effects. It demonstrates that partial and total lockdown interventions were highly effective in reducing the number of confirmed cases. When some interventions, such as enforcing regional curfews, closing workplaces, and imposing travel restrictions, were combined, their impact became significant. MARS method is recommended to be applied when exploring the impact of interventions on the spread of a disease. It does not require any prior assumptions about the statistical distribution of data, does not affect data collinearity, has simple and transparent functions, and allows for a more accurate analysis of intervention results.