Currently accepted at: Journal of Medical Internet Research
Date Submitted: Jul 22, 2025
Date Accepted: Jan 28, 2026
This paper has been accepted and is currently in production.
It will appear shortly on 10.2196/81099
The final accepted version (not copyedited yet) is in this tab.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
AI Meets Attitudes: Decoding COVID-19 Vaccine Hesitancy in Alaska’s Diverse Communities
ABSTRACT
Background:
The global COVID-19 vaccine rollout faces challenges from persistent hesitancy, especially in rural and underserved regions. Alaska’s unique geographic, cultural, and infrastructural barriers create complex vaccine uptake dynamics.
Objective:
This study uses advanced machine learning on survey data to identify key socio-demographic and attitudinal predictors of hesitancy, informing targeted public health strategies.
Methods:
This study surveyed 720 Alaska adults, selected via targeted sampling to capture diverse COVID-19 vaccine attitudes across demographics and regions. A structured questionnaire assessed hesitancy through 17 indicators. We applied XGBoost, Random Forest, and KNN models for both regression and classification, and interpreted classification results via SHAP values.
Results:
Analysis of 720 respondents showed that in Alaska, 1.8% of surveyed individuals completed the full primary vaccination series (doses 1–3) and received all three booster doses. 63.47% vaccination rate (at least one dose), with Pfizer preferred over Moderna. A total of 34% of participants reported receiving the first dose of the COVID-19 vaccine, 43% received the second dose, 18% received a third dose, 22% received the first booster, 13% received the second booster, and only 4% received a third booster. Geographic data revealed higher uptake in urban centers and variability in rural areas. Young adult males exhibited the highest hesitancy, while LGBT individuals showed the lowest. Trust in the healthcare system was the strongest predictor, confirmed by machine learning analyses.
Conclusions:
Focusing on a geographically and demographically distinct U.S. population, this study advances the scientific understanding of vaccine hesitancy while informing context-sensitive public health strategies. The findings offer actionable evidence to guide targeted communication, equitable outreach, and data-driven policy in Alaska and similarly underserved regions across the Americas, underscoring the importance of culturally tailored, trust-centered interventions to promote vaccine uptake and health equity. Clinical Trial: NA
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.