An analysis of COVID-19 vaccine hesitancy in the U.S.

References

• Abdi, H. and Williams, L.J. (2010) Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2, 433–459.
   Google Scholar
• Akiba, T., Sano, S., Yanase, T., Ohta, T. and Koyama, M. (2019) Optuna: A next-generation hyperparameter optimization framework, in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, Anchorage, AK, USA, pp. 2623–2631.
   Google Scholar
• ASPE. (2021) Vaccine hesitancy for COVID-19: State, county, and local estimates. Technical report, U.S. Department of Health and Human Services. https://aspe.hhs.gov/reports/vaccine-hesitancy-covid-19-state-county-local-estimates (accessed on June 06, 2020).
   Google Scholar
• Bavadekar, S., Boulanger, A., Davis, J., Desfontaines, D., Gabrilovich, E., Gadepalli, K., Ghazi, B., Griffith, T., Gupta, J., Kamath, C., Kraft, D., Kumar, R., Kumok, A., Mayer, Y., Manurangsi, P., Patankar, A., Perera, I.M., Scott, C., Shekel, T., Miller, B., Smith, K., Stanton, C., Sun, M., Young, M. and Wellenius, G. (2021) Google COVID-19 vaccination search insights: Anonymization process description. CoRR, arXiv preprint arXiv:2107.01179.
   Google Scholar
• Bell, A., Rich, A., Teng, M., Orešković, T., Bras, N.B., Mestrinho, L., Golubovic, S., Pristas, I. and Zejnilovic, L. (2019) Proactive advising: A machine learning driven approach to vaccine hesitancy. 2019 IEEE International Conference on Healthcare Informatics, IEEE Press, Piscataway, NJ, pp. 1–6.
   Google Scholar
• Berning, P., Huang, L., Razavi, A.C., Boakye, E., Osuji, N., Stokes, A.C., Martin, S.S., Ayers, J.W., Blaha, M.J. and Dzaye, O. (2022) Association of online search trends with vaccination in the United States: June 2020 through May 2021. Frontiers in Immunology, 13, 884211–884220.
   PubMed Web of Science ®Google Scholar
• Betsch, C., Schmid, P., Heinemeier, D., Korn, L., Holtmann, C. and Böhm, R. (2018) Beyond confidence: Development of a measure assessing the 5C psychological antecedents of vaccination. PLOS ONE, 13, e0208601.
   PubMed Web of Science ®Google Scholar
• Blankenship, E.B., Goff, M.E., Yin, J., Tse, Z.T.H., Fu, K.-W., Liang, H., Saroha, N. and Fung, I.C.-H. (2018) Sentiment, contents, and retweets: A study of two vaccine-related twitter datasets. The Permanente Journal, 22, 17–138.
   PubMedGoogle Scholar
• Bollyky, T.J. (2021) US COVID-19 vaccination challenges go beyond supply. Annals of Internal Medicine, 174(4), 558–559.
   PubMed Web of Science ®Google Scholar
• Callaghan, T., Moghtaderi, A., Lueck, J.A., Hotez, P.J., Strych, U., Dor, A., Fowler, E.F. and Motta, M. (2020) Correlates and disparities of COVID-19 vaccine hesitancy. SSRN Electronic Journal, 272, 113638–113642.
   Google Scholar
• CDC. (2021a) Influenza vaccination coverage for all ages. https://data.cdc.gov/Flu-Vaccinations/Influenza-Vaccination-Coverage-for-All-Ages-6-Mont/vh55-3he6 (accessed on November 13, 2021).
   Google Scholar
• CDC. (2021b) COVID-19 vaccinations in the United States, county. https://data.cdc.gov/Vaccinations/COVID-19-Vaccinations-in-the-United-States-County/8xkx-amqh (accessed on December 25, 2021).
   Google Scholar
• CDC. (2021c) Vaccine hesitancy for COVID-19: County and local estimates. https://data.cdc.gov/Vaccinations/Vaccine-Hesitancy-for-COVID-19-County-and-local-es/q9mh-h2tw (accessed on November 20, 2021).
   Google Scholar
• CDC. (2022a) Archive of COVID-19 vaccination data updates. https://www.cdc.gov/coronavirus/2019-ncov/vaccines/distributing/vaccination-data-archived-updates.html (accessed on June 12, 2022).
   Google Scholar
• CDC. (2022b) COVID-19 risks and vaccine information for older adults. https://www.cdc.gov/aging/covid19/covid19-older-adults.html (accessed on April 21, 2022).
   Google Scholar
• Chandir, S., Siddiqi, D.A., Hussain, O.A., Niazi, T., Shah, M.T., Dharma, V.K., Habib, A. and Khan, A.J. (2018) Using predictive analytics to identify children at high risk of defaulting from a routine immunization program: Feasibility study. JMIR Public Health Surveillance, 4, e9681.
   Google Scholar
• Chandrasekaran, R., Mehta, V., Valkunde, T. and Moustakas, E. (2020) Topics, trends, and sentiments of Tweets about the COVID-19 pandemic: Temporal infoveillance study. Journal of Medical Internet Research, 22(10), e22624.
   PubMed Web of Science ®Google Scholar
• Childs, J.W. (2021) Winter storms delayed millions of COVID-19 vaccine doses. https://weather.com/health/coronavirus/news/2021-02-17-covid-19-vaccines-delayed-weather (accessed February 2024).
   Google Scholar
• Dubé, E., Laberge, C., Guay, M., Bramadat, P., Roy, R. and Bettinger, J.A. (2013) Vaccine hesitancy. Human Vaccines & Immunotherapeutics, 9, 1763–1773.
   PubMed Web of Science ®Google Scholar
• FCC. (2021) Area and census block. https://geo.fcc.gov/api/census/ (accessed February 2024).
   Google Scholar
• Fisher, W.D. (1958) On grouping for maximum homogeneity. Journal of the American Statistical Association, 53, 789–798.
   Web of Science ®Google Scholar
• Franch-Pardo, I., Napoletano, B.M., Rosete-Verges, F. and Billa, L. (2020) Spatial analysis and GIS in the study of COVID-19. A review. Science of the Total Environment, 739, 140033.
   PubMed Web of Science ®Google Scholar
• Garett, R. and Young, S.D. (2021) Online misinformation and vaccine hesitancy. Translational Behavioral Medicine, 11(12), 2194–2199.
   PubMed Web of Science ®Google Scholar
• GDELT. (2021) GDELT online news coverage of COVID-19 with locations. https://console.cloud.google.com/bigquery?p=gdelt-bq&d=covid19&t=onlinenewsgeo&page=table (accessed on November 25, 2021).
   Google Scholar
• Grammakov, D., Jurkov, R., Hsiao, Y.-C. and Prescott, R. (2020) Full list of US states and cities. GitHub.
   Google Scholar
• Hale, T., Angrist, N., Goldszmidt, R., Kira, B., Petherick, A., Phillips, T., Webster, S., Cameron-Blake, E., Hallas, L., Majumdar, S. and Tatlow, H. (2021) A global panel database of pandemic policies (Oxford COVID-19 government response tracker). Nature Human Behaviour, 5, 529–538.
   PubMed Web of Science ®Google Scholar
• Hamel, L., Lopes, L., Sparks, G., Kirzinger, A., Kearney, A., Stokes, M. and Brodie, M. (2022) KFF COVID-19 vaccine monitor: January 2022. https://www.kff.org/coronavirus-covid-19/poll-finding/kff-covid-19-vaccine-monitor-january-2022/ (accessed on April 10, 2023).
   Google Scholar
• Ivory, D., Leatherby, L. and Gebeloff, R. (2021) Least vaccinated U.S. counties have something in common: Trump voters. https://www.nytimes.com/interactive/2021/04/17/us/vaccine-hesitancy-politics.html (accessed on June 14, 2021).
   Google Scholar
• Jenks, G.F. (1977) Optimal data classification for choropleth maps. Department of Geography, University of Kansas Occasional Paper.
   Google Scholar
• Jenks, G.F. and Caspall, F.C. (1971) Error on choroplethic maps: Definition, measurement, reduction. Annals of the Association of American Geographers, 61(2), 217–244.
   Web of Science ®Google Scholar
• KFF. (2021) Poll: Most Americans worry political pressure will lead to premature approval of a COVID-19 vaccine; half say they would not get a free vaccine approved before election day. https://www.kff.org/coronavirus-covid-19/press-release/poll-most-americans-worry-political-pressure-will-lead-to-premature-approval-of-a-covid-19-vaccine-half-say-they-would-not-get-a-free-vaccine-approved-before-election-day/ (accessed on July 7, 2021).
   Google Scholar
• Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. and Liu, T.-Y. (2017) Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154.
   Google Scholar
• Khubchandani, J., Sharma, S., Price, J.H., Wiblishauser, M.J., Sharma, M. and Webb, F.J. (2021) COVID-19 vaccination hesitancy in the United States: A rapid national assessment. Journal of Community Health, 46, 270–277.
   PubMed Web of Science ®Google Scholar
• Lange, J. and Lange, C. (2022) Applying machine learning and AI explanations to analyze vaccine hesitancy. medRxiv, page 2022.01.06.22268845.
   PubMedGoogle Scholar
• Larson, H.J., Jarrett, C., Schulz, W.S., Chaudhuri, M., Zhou, Y., Dube, E., Schuster, M., MacDonald, N.E. and Wilson, R. (2015) Measuring vaccine hesitancy: The development of a survey tool. Vaccine, 33(34), 4165–4175.
   PubMed Web of Science ®Google Scholar
• Lee, J. and Huang, Y. (2022) COVID-19 vaccine hesitancy: The role of socioeconomic factors and spatial effects. Vaccines, 10, 352.
   Google Scholar
• Lundberg, S.M., Erion, G., Chen, H., DeGrave, A., Prutkin, J.M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N. and Lee, S.-I. (2020) From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2(1), 2522–5839.
   Google Scholar
• MacDonald, N.E., the SAGE Working Group on Vaccine Hesitancy. (2015) Vaccine hesitancy: Definition, scope and determinants. Vaccine, 33(34), 4161–4164.
   PubMed Web of Science ®Google Scholar
• Merrick, E., Weissman, J.P. and Patel, S.J. (2022) Utilizing Google trends to monitor coronavirus vaccine interest and hesitancies. Vaccine, 40, 4057–4063.
   PubMed Web of Science ®Google Scholar
• MIT Election Data and Science Lab. (2021) County presidential election returns 2000-2020. https://dataverse.harvard.edu/dataset.xhtml?persistentId= doi:10.7910/DVN/VOQCHQ (accessed on May 10, 2021).
   Google Scholar
• Mollalo, A. and Tatar, M. (2021) Spatial modeling of COVID-19 vaccine hesitancy in the United States. International Journal of Environmental Research and Public Health, 18, 9488.
   PubMed Web of Science ®Google Scholar
• Murthy, B.P., Sterrett, N., Weller, D., Zell, E., Reynolds, L., Toblin, R.L., Murthy, N., Kriss, J., Rose, C., Cadwell, B., et al. (2021) Disparities in COVID-19 vaccination coverage between urban and rural counties—United States, December 14, 2020–April 10, 2021. MMWR. Morbidity and Mortality Weekly Report, 70, 759–764.
   PubMed Web of Science ®Google Scholar
• Paraskevis, D., Kostaki, E.G., Alygizakis, N., Thomaidis, N.S., Cartalis, C., Tsiodras, S., and Dimopoulos, M.A. (2021) A review of the impact of weather and climate variables to COVID-19: In the absence of public health measures high temperatures cannot probably mitigate outbreaks. Science of the Total Environment, 768, 144578.
   PubMed Web of Science ®Google Scholar
• Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011) Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.
   Web of Science ®Google Scholar
• Poirier, C., Luo, W., Majumder, M.S., Liu, D., Mandl, K.D., Mooring, T.A. and Santillana, M. (2020) The role of environmental factors on transmission rates of the COVID-19 outbreak: An initial assessment in two spatial scales. Scientific Reports, 10(1), 17002.
   PubMed Web of Science ®Google Scholar
• Razai, M.S., Oakeshott, P., Esmail, A., Wiysonge, C.S., Viswanath, K. and Mills, M.C. (2021) COVID-19 vaccine hesitancy: The five Cs to tackle behavioural and sociodemographic factors. Journal of the Royal Society of Medicine, 114, 295–298.
   PubMed Web of Science ®Google Scholar
• Reinhart A., Brooks, L., Jahja, M., Rumack, A., Tang, J., Agrawal, S., Al Saeed, W., Arnold, T., Basu, A., Bien, J., et al. (2021) An open repository of real-time COVID-19 indicators. Proceedings of the National Academy of Sciences, 118(51), e2111452118.
   Web of Science ®Google Scholar
• Salomon, J.A., Reinhart, A. and Bilinski, E.A. (2021) The US COVID-19 trends and impact survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination. Proceedings of the National Academy of Sciences, 118(51), e2111454118.
   Web of Science ®Google Scholar
• Sharma, M., Davis, R.E. and Wilkerson, A.H. (2021) COVID-19 vaccine acceptance among college students: A theory-based analysis. International Journal of Environmental Research and Public Health, 18, 4617–4629.
   PubMed Web of Science ®Google Scholar
• Siegler, A.J., Luisi, N., Hall, E.W., Bradley, H., Sanchez, T., Lopman, B.A. and Sullivan, P.S. (2021) Trajectory of COVID-19 vaccine hesitancy over time and association of initial vaccine hesitancy with subsequent vaccination. JAMA Network Open, 4(9), e2126882–e2126882.
   Web of Science ®Google Scholar
• To, Q.G., To, K.G., Huynh, V.N., Nguyen, N.T.Q., Ngo, D.T.N., Alley, S.J., Tran, A.N.Q., Tran, A.N.P., Pham, N.T.T., Bui, T.X., et al. (2021) Applying machine learning to identify anti-vaccination tweets during the COVID-19 pandemic. International Journal of Environmental Research and Public Health, 18, 4069.
   PubMed Web of Science ®Google Scholar
• U.S. Bureau of Labor Statistics. (2021) Local area unemployment statistics. https://www.bls.gov/lau//#cntyaa (accessed on December 13, 2021).
   Google Scholar
• U.S. Census Bureau. (2019) Dataset for poverty status. {https://data.census.gov/cedsci/table?t=Income/%20and/%20Poverty&g=0100000US/%240500000&tid=ACSST1Y2019.S1701} (accessed on December 13, 2021).
   Google Scholar
• U.S. Census Bureau. (2021) Dataset for types of computers and internet subcriptions. https://data.census.gov/cedsci/table?q=S2801&g=0100000US/%240500000&tid=ACSST1Y2019.S2801 (accessed on December 13, 2021).
   Google Scholar
• U.S. Department of Health & Human Services. (2022) COVID-19 vaccines. https://www.hhs.gov/coronavirus/covid-19-vaccines/index.html (accessed on May 28,2023).
   Google Scholar
• Wahltinez, O., Murphy, K., Brenner, M., Lee, M., Erlinger, A. and Daswani, M. (2020) COVID-19 open-data: Curating a fine-grained, global-scale data repository for SARS-CoV-2. Github. Work in progress.
   Google Scholar
• Wang, C.-W., de Jong, E.P., Faure, J.A., Ellington, J.L., Chen, C.-H.S. and Chan, C.-C. (2022) A matter of trust: A qualitative comparison of the determinants of COVID-19 vaccine hesitancy in Taiwan, the United States, the Netherlands, and Haiti. Human Vaccines & Immunotherapeutics, 18(5), 2050121.
   Web of Science ®Google Scholar
• WHO. (2019) Ten threats to global health in 2019. https://www.who.int/news-room/spotlight/ten-threats-to-global-health-in-2019 (accessed on June 14, 2021).
   Google Scholar
• Williams, C.J., Kranzler, E.C., Luchman, J.N., Denison, B., Fischer, S., Wonder, T., Ostby, R., Vines, M., Weinberg, J., Petrun Sayers, E.L., et al. (2023) The initial relationship between the United States Department of Health and Human Services’ digital COVID-19 public education campaign and vaccine uptake: Campaign effectiveness evaluation. Journal of Medical Internet Research, 25:e43873.
   PubMed Web of Science ®Google Scholar
• Wilson, S.L. and Wiysonge, C. (2020) Social media and vaccine hesitancy. BMJ Global Health, 5(10), e004206.
   PubMed Web of Science ®Google Scholar
• Yousefinaghani, S., Dara, R., Mubareka, S., Papadopoulos, A. and Sharif, S. (2021) An analysis of COVID-19 vaccine sentiments and opinions on Twitter. International Journal of Infectious Diseases, 108, 256–262.
   PubMed Web of Science ®Google Scholar