Temporal trends in online searches related to COVID-19 vaccine safety: A digital infodemiology study
Bibliographic record
Abstract
Background: The rapid development of COVID-19 vaccines may have raised public concerns about their safety and side effects in the United States (US). This study aimed to assess trends in online searches related to the safety and side effects of COVID-19 vaccines in the US from 2021-2022. Methods: Google COVID-19 Vaccination Search Insights was used to analyze searches about COVID-19 vaccine safety and side effects in the US from January 4, 2021, to November 21, 2022 (98 weeks). Data were scaled from 0 (low interest) to 100 (high interest) as a fixed scaling factor called scaled normalized interest (SNI) to indicate relative search interest over time and by location. A joinpoint regression analysis was used to determine the search trends during the study period. Results: Analysis included 709 counties across 38 US states. Searches of COVID-19 vaccine safety and side effects peaked in April 2021 in the District of Columbia (SNI: 35.8), Massachusetts (29.7), New Hampshire (27.4), Connecticut (27.3), and Maine (26.7), then decreased significantly by an average monthly percentage change (AMPC) of -16.6% (95% CI -19.9 to -13.3) until July 2022. Overall AMPC from January 2021 to November 2022 was -8.9% (95% CI -16.2 to -0.9; P<0.001). Conclusion: Online searches related to COVID-19 vaccine safety and side effects decreased dramatically over time, supporting the utility of digital surveillance to track real-time vaccine safety concerns. This study provides insights into public interest in COVID-19 vaccine risks and can help monitor potential safety issues.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".