Theoretical and Natural Science
- The Open Access Proceedings Series for Conferences
Theoretical and Natural Science
Vol. 31, 07 March 2024
Open
Access | Article
Predictive factors of happiness and policy countermeasure in post COVID-19 pandemic era
* Author to whom correspondence should be addressed.
Abstract
The COVID-19 pandemic has severely affected numerous individuals’ lives and inevitably lowered their happiness levels. In the process of recovering from the effects of the pandemic, a series of measures must be taken to enhance people’s happiness as soon as possible. The research used multiple linear regression and random forest to analyze the data from World Happiness Report 2023 in order to identify some effective predictive factors of happiness, and made recommendations to policymakers. The result of this study shows social support and GDP are the main indexes policymakers should put effort into improving. Governments can enhance the level of social support not only from the classical perspective but also from improving health care. Reformations of the health care system are necessary if current contingency measures are not sufficient for such severe situations as COVID-19. Policies on economics are continuously beneficial to a population’s overall well-being and thus are important for policymakers to work on.
Keywords
Happiness, predictive factors, COVID-19 pandemic, policymakers
References
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3. Gupta, Meenu, et al. “Machine Learning-Based Comparative Analysis of COVID-19 Infected Cases with GDP and World Happiness Report.” Micro-Electronics and Telecommunication Engineering: Proceedings of 6th ICMETE 2022. Singapore: Springer Nature Singapore, 2023. 345-355.
4. Khder, Moaiad Ahmad, Mohammad Adnan Sayfi, and Samah Wael Fujo. “Analysis of World Happiness Report Dataset Using Machine Learning Approaches.” International Journal of Advances in Soft Computing & Its Applications 14.1, 2022.
5. Agarwal, Tarushi, et al. “Predicting Happiness Score During Covid-19 Using Machine Learning.” 2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST). IEEE, 2022.
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics
- ISBN (Print)
- 978-1-83558-317-3
- ISBN (Online)
- 978-1-83558-318-0
- Published Date
- 07 March 2024
- Series
- Theoretical and Natural Science
- ISSN (Print)
- 2753-8818
- ISSN (Online)
- 2753-8826
- DOI
- 10.54254/2753-8818/31/20241150
- Copyright
- 07 March 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited