Vaccination Uptake, Happiness and Emotions: Using a Supervised Machine Learning Approach

Simple item page
aut.relation.softwareversion1482
dc.contributor.authorRossouw, Stephanie
dc.contributor.authorGreyling, Talita
dc.date.accessioned2024-09-27T02:21:18Z
dc.date.available2024-09-27T02:21:18Z
dc.date.issued2024-09-06
dc.description.abstractThe COVID-19 pandemic is an example of an immense global failure to curb the spread of a pathogen and save lives. To indirectly protect people against a deadly virus, a population needs to achieve herd immunity, which is attained either through vaccination or prior infection. However, achieving herd immunity by vaccination is preferable as it limits the health risks of disease. As the coronavirus mutated, vaccination estimates for achieving herd immunity went from 70% to 90%. In this study, we investigate the order of the importance of the variables to identify those factors that contribute most to achieving high vaccination rates. Secondly, we consider if subjective measures, including the level of happiness and different collective emotions of populations, contribute to higher vaccine uptake. We employ an XGBoost machine learning model (and, as robustness tests, Random Forest and Decision Tree models) to train our data. Our target output variable is the number of people vaccinated as a percentage of the population. We consider two thresholds of our output variable, the first at 70% of a country's population, corresponding to the initial suggestions to achieve herd immunity, and the second with a threshold of 90%, suggested later due to the highly infectious virus. We use a dataset that includes ten countries in the Northern and Southern Hemisphere and variables related to COVID-19, vaccines, country characteristics and the level of happiness and collective emotions within countries. The most important variables listed in reaching the 70% and 90% thresholds are similar. These include the implemented vaccination policy, international travel controls, the percentage of the population in rural areas, the average temperature, and the happiness levels within countries. It is remarkable how the importance of subjective measures of people's emotions and moods play a role in attaining higher vaccination levels. As the vaccine threshold increases, the importance of subjective well-being variables rises. Therefore, not only the implemented policies and country characteristics but also the happiness levels and emotions play a role in compliance and achieving higher vaccination thresholds. Our results provide actionable policy insights to increase vaccination rates. Additionally, we highlight the importance of subjective measures such as happiness and collective emotions to increase vaccination rates and assist governments to be better prepared for the next global pandemic.
dc.identifier.citationGreyling, T., & Rossouw, S. (2024). Vaccination uptake, happiness and emotions: Using a supervised machine learning approach. Global Labor Organization (GLO) Discussion Paper Series. https://www.econstor.eu/handle/10419/301837
dc.identifier.urihttp://hdl.handle.net/10292/18070
dc.publisherGlobal Labor Organization (GLO)
dc.relation.urihttps://www.econstor.eu/handle/10419/301837
dc.rightsEconStor supports Green Open Access. All papers on EconStor are made freely available without restrictions or embargo periods. Publication on EconStor is based on usage agreements with authors or the editors/publishers of a series or journal. Authors' copyrights are safeguarded. Publication on EconStor does not inhibit further publication of the documents in journals or on other document servers. Disseminating publications with EconStor is free for publishers and authors.
dc.rights.accessrightsOpenAccess
dc.titleVaccination Uptake, Happiness and Emotions: Using a Supervised Machine Learning Approach
dc.typec-40
pubs.elements-id568544
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
GLO-DP-1482.pdf
Size:
858.32 KB
Format:
Adobe Portable Document Format
Description:
Article
Download
Collections
School of Social Sciences and Public Policy