A Bayesian mixed-effects model to evaluate the determinants of COVID-19 vaccine uptake in the US
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916625976918016 |
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| author | Dey, Asim K. |
| author_facet | Dey, Asim K. |
| contents | The COVID-19 pandemic has adversely affected US public health, resulting in over a hundred million cases and more than one million deaths. Vaccination is the key intervention against the COVID-19 pandemic. Multiple COVID-19 vaccines are now available for human use. However, a number of factors, including socio-demographic variables, impact the uptake of COVID-19 vaccines. In this study, we apply a Bayesian mixed-effects model to assess different socio-demographic and spatial factors that influence the acceptance of COVID-19 vaccines in the US. The fitted mixed-effects model provides the probabilistic inference about the vaccine acceptance determinants with uncertainty quantification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_16130 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Bayesian mixed-effects model to evaluate the determinants of COVID-19 vaccine uptake in the US Dey, Asim K. Applications Quantitative Methods The COVID-19 pandemic has adversely affected US public health, resulting in over a hundred million cases and more than one million deaths. Vaccination is the key intervention against the COVID-19 pandemic. Multiple COVID-19 vaccines are now available for human use. However, a number of factors, including socio-demographic variables, impact the uptake of COVID-19 vaccines. In this study, we apply a Bayesian mixed-effects model to assess different socio-demographic and spatial factors that influence the acceptance of COVID-19 vaccines in the US. The fitted mixed-effects model provides the probabilistic inference about the vaccine acceptance determinants with uncertainty quantification. |
| title | A Bayesian mixed-effects model to evaluate the determinants of COVID-19 vaccine uptake in the US |
| topic | Applications Quantitative Methods |
| url | https://arxiv.org/abs/2502.16130 |