Utilizing Multiple Linear Regression Models to Predict Intentions to Vaccinate for COVID-19
Keywords:COVID-19, intention to vaccinate, multiple linear regression model, Mathematical model, health-related issues, effectiveness of vaccine
The purpose of this research is to develop a multiple linear regression model to predict the intention to vaccinate for COVID-19. An anonymous cross-sectional online survey utilizing Qualtrics software was conducted. Descriptive statistics and inter-correlations between predictors and criterion variables were presented. Multiple linear regression was used to analyze associations among predictors and criterion variables. Overall, results indicated that while 79% of participants reported their intentions to get vaccinated against COVID-19 when a vaccine becomes available, 21% reported not being likely to. Further, results showed that six socio-demographic, health-related, and belief factors had positive effects on intentions to vaccinate for COVID-19. Specifically, the predictive variables of the belief in the effectiveness of the COVID-19 vaccine, political party affiliation, previous influenza vaccinations, employment status, perceived knowledge of COVID-19 and the COVID-19 vaccine, as well as education level have crucial roles in predicting the dependent variable of the intention to vaccinate for COVID-19.
This research contributes to our understanding of the various factors that influence the decision to vaccinate for COVID-19. Overall, the proposed regression model with the variables present in this study represents a strong effect and explains the proportion of the variability in the intention to vaccinate with over 70% accuracy. These results have important practical as well as theoretical implications for public health policymakers. With significant percentages of the population that are still hesitant to vaccinate, future studies should focus on finding this missing link and implementing any social/public health policies to level up individual intentions.
Bruin, W. B., Saw, H., & Goldman, D. P. (2020). Political polarization in US residents’ COVID-19 risk perceptions, policy preferences, and protective behaviors, Journal of Risk and Uncertainty, 61, 177-194.
Callaghan, T., Moghtaderi, A., Lueck, J. A., Hotez, P., Strych, U., Dor, A., . . . Motta, M. (2021). Correlates and disparities of intention to vaccinate against COVID-19. Social Science & Medicine, 113638. doi:10.1016/j.socscimed.2020.113638
CDC COVID Data Tracker. (2021). Retrieved February 18, 2021, from https://covid.cdc.gov/covid-data-tracker/#cases_casesper100klast7days
Cucinotta, D., & Vanelli, M. (2020). WHO Declares COVID-19 a Pandemic. Acta Biomed. doi:10.23750/abm.v91i1.9397
Edwards, E.J., Zhang, X., Chu, K. L., Cosgrove, L. K. (2022). Explaining individual differences in cognitive performance: The role of anxiety, social support and living arrangements during COVID-19, Personality and Individual Differences, 198, 1-6.
Gabanelli, P., Monzani, D., Fiabane, E., Quaquarini, E., Frascaroli, M., Balletti, E., ... & Gorini, A. (2022). Perceived risk, illness perception and dispositional optimism related to COVID-19 among oncologic outpatients undergoing in-hospital treatments and healthy controls. Psychology & Health, 1-17.
Grassly, N. C., & Fraser, C. (2008). Mathematical models of infectious disease transmission. Nature eviews Microbiology, 6(6), 477-487. doi:10.1038/nrmicro1845
Gostin, L. O., Salmon, D. A., & Larson, H. J. (2021). Mandating COVID-19 Vaccines, Journal of American of Medical Association, 325 (6), 532-533.
Hair, J.F.; Anderson, R.E.; Tatham, R.L.; and Black, W.C. (1998). Multivariate Data Analysis with Readings, 5th ed. Prentice-Hall, Englewood Cliffs, NJ.
Interim clinical considerations for use of MRNA COVID-19 Vaccines. (2021, February 10). Retrieved February 18, 2021, from https://www.cdc.gov/vaccines/covid-19/info-by-product/clinical-considerations.html
Kwon, K. O., Li, K., Wei, W. I., Tang, A., Wong, S. Y. S., & Lee, S. S. (2021). Influenza vaccine uptake, COVID-19 Vaccination intention and vaccine hesitancy among nurses: A survey, International Journal of Nursing Studies, 114, 1-9.
Li, H. (2022). Getting though a COVID-19 winter: Physical coldness increases the perceived risk of coronavirus disease, Personality and Individual Differences, in press.
Lucia, V. C., Kelekar, A., & Afonso, N. M. (2021). COVID-19 Vaccine hesitancy among medical students, 43(3), 445-449.
MacIntyre, C. R., Costantino, V., & Trent, M. (2020). Modelling of COVID-19 vaccination strategies and herd IMMUNITY, in scenarios of limited and full vaccine supply in NSW, Australia. doi:10.1101/2020.12.15.20248278
Motta, M. (2021). Can a COVID-19 vaccine live up to Americans’ expectations? A conjoint analysis of how vaccine characteristics influence vaccination intentions: Social Science & Medicine. doi:https://doi.org/10.1016/j.socscimed.2020.113642
Neufeld, Z., Khataee, H., & Czirok, A. (2020). Targeted adaptive isolation strategy FOR COVID-19 pandemic. Infectious Disease Modelling, 5, 357-361. doi:10.1016/j.idm.2020.04.003
Okuonghae, D., & Omame, A. (2020). Analysis of a mathematical model FOR Covid-19 population dynamics in Lagos, Nigeria. Chaos, Solitons & Fractals, 139, 110032. doi:10.1016/j.chaos.2020.110032
Paul, E., Steptoe, A., & Fancourt, D. (2021). Attitudes towards vaccines and intention to vaccinate AGAINST COVID-19: Implications for public HEALTH COMMUNICATIONS. The Lancet Regional Health - Europe, 1, 100012. doi:10.1016/j.lanepe.2020.100012
Rhodes, A., Hoq, M., Measey, M., & Danchin, M. (2021). Intention to vaccinate against COVID-19 in Australia, the Lancet, 21(5), e110.
Sherman, S.M., Sim, J., Cutts, M. Dasch, H., Amlot, R., Rubin, G.J., Sevdalis, N., & Smith, L.E. (2022). COVID-19 vaccination acceptability in the UK at the start of the vaccination programme: A nationally representative cross-sectional survey (CoVAccS – wave 2), Public Health, 202, 1-9.
Sherman, S. M., Smith, L. E., Sim, J., Amlôt, R., Cutts, M., Dasch, H., . . . Sevdalis, N. (2020). COVID-19 vaccination intention in the UK: Results from the COVID-19 Vaccination ACCEPTABILITY study (COVACCS), a nationally representative cross-sectional survey.
Human Vaccines & Immunotherapeutics, 1-10. doi:10.1080/21645515.2020.1846397
Shmueli, L. (2020). Predicting intention to receive COVID-19 vaccine among the general population using the health BELIEF model and the theory of Planned behavior model. doi:10.1101/2020.12.20.20248587
Taber, K. S. (2017). The use of cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 1273-1296. doi:10.1007/s11165-016-9602-2
Tiwari, V., Deyal, N., & Bisht, N. S. (2020). Mathematical modelling based study and prediction of COVID-19 epidemic dissemination under the impact of Lockdown in India. doi:10.1101/2020.07.25.20161885
Veera Krishna, M. (2020). Mathematical modelling on diffusion and control of covid–19. Infectious Disease Modelling, 5, 588-597. doi:10.1016/j.idm.2020.08.009
Wang, N., Fu, Y., Zhang, H., & Shi, H. (2020). An evaluation of mathematical models for the outbreak of covid-19. Precision Clinical Medicine, 3(2), 85-93. doi:10.1093/pcmedi/pbaa016
Wong, Martin C.S., Eliza L.Y.Wong, Junjie Huang, Annie W.L.Cheung, KevinLaw, Marc K.C.Chong, Rita W.Y.Ng, Christopher K.C.Lai, Siaw S.Boon, Joseph T.F.Lau, ZiguiChen, Paul K.S.Chan (2021). Acceptance of the COVID-19 vaccine based on the health belief model: A population-based survey in Hong Kong, Vaccine, 39(7), 1148-1156.
Whitehead, A. L., & Perry, S. L. (2020). How culture Wars DELAY HERD Immunity: Christian nationalism and Anti-vaccine Attitudes. Socius: Sociological Research for a Dynamic World, 6, 237802312097772. doi:10.1177/2378023120977727
Worldometer: Coronavirus Cases. (2021). Retrieved February 18, 2021, from https://www.worldometers.info/coronavirus/
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