Infodemiology of public sentiment toward the measles vaccine in Canada: A Google Trends and health belief model–based analysis, 2025

Opinion:

The term “infodemiology” was new to me, but it is a very useful way to describe how information, opinions, and trust about vaccines spread and change over time, almost like an epidemic of ideas or ever-changing perceptions.  This study combines online search data with ideas from psychology to better understand why some people hesitate about vaccines. The methods are quite solid, but there are some important limits. Using Google Trends (GT) means the authors are looking only at what people search for, which does not always show what they truly believe or plan to do, and focusing on English‑language searches in Canada leaves out many people who search in other languages. Also, the way they label searches as positive or negative is based only on the words typed; for example, “Is the MMR vaccine safe?” might come from someone who is worried or from someone just checking before vaccinating, and the method cannot really tell the difference. The paper also shows differences between regions, but it does not go very far in explaining how factors like income, education, or culture might drive those patterns. A next step would be to combine this kind of online monitoring with surveys or interviews, so that the numbers can be checked against what people actually think and feel. The proposed solutions—such as better clinic‑finder tools and clear “myth‑busting” material—sound practical, but they still need to be tested to see if they truly change behavior in real life.

Study summary

This study by Jokar et al. (2026) investigates public sentiment toward the measles vaccine in Canada using Google Trends (GT) data and the Extended Health Belief Model (EHBM). Conducted between July and September 2025, the research analyzed 92 vaccine-related queries from January to August 2025, categorizing them by sentiment (positive, negative, neutral) and EHBM constructs (perceived susceptibility, severity, benefits, barriers, cues to action, and self-efficacy). The primary aim was to identify regional and thematic patterns in vaccine-related information seeking to inform risk communication strategies.

Measles remains a significant public health concern despite vaccine availability, largely due to vaccine hesitancy fueled by misinformation. In Canada, infants under 12 months are particularly vulnerable, with 69% lacking immunity before their first dose. The authors argue that infodemiology—studying health information dissemination—offers a valuable lens for monitoring public attitudes, especially given Google’s dominance in Canada’s search engine market (91%). Previous studies have shown GT’s utility in tracking vaccine interest during COVID-19, making it a suitable tool for measles-related analysis.

The study employed a retrospective observational design, extracting normalized RSV (Relative Search Volume) data from GT. Queries were systematically identified using anchor terms (“Measles vaccine,” “MMR vaccine”), related queries, and autocomplete suggestions. Sentiment classification was performed independently by two reviewers, with discrepancies resolved by a third. To address GT’s normalization bias, RSV values were standardized using Z-scores, enabling cross-query comparability. Sentiment Difference (SD) indices were calculated to measure the balance between positive and negative sentiment. Additionally, measles incidence data from the Public Health Agency of Canada were integrated to contextualize search trends.

Search interest peaked nationally on March 19, 2025, coinciding with rising measles cases, suggesting a link between disease burden and vaccine curiosity. Alberta exhibited consistently high engagement across all sentiment categories, while Manitoba and Saskatchewan showed stronger negative sentiment. Positive queries often reflected proactive behaviors (e.g., “book measles vaccine appointment”), whereas negative queries focused on safety concerns and misinformation (e.g., “MMR vaccine autism”). EHBM analysis revealed that most queries related to cues to action (43), followed by perceived barriers (16), indicating that while many Canadians sought vaccination access, safety concerns persisted. Alberta scored high across all EHBM constructs, suggesting an engaged population, whereas Nova Scotia displayed high perceived barriers but low cues to action, and Ontario showed low engagement despite elevated measles incidence.

The authors interpret the March peak as a response to heightened media coverage and rising case counts, consistent with patterns observed during COVID-19. They emphasize the role of misinformation in shaping negative sentiment, particularly in provinces with higher frequencies of queries like “MMR vaccine autism.” The study advocates for tailored communication strategies, including emotionally engaging content and interactive tools (e.g., clinic locators) to address logistical barriers. Limitations include GT’s exclusion of non-Google searches, reliance on English-language queries, and inability to capture offline populations.

GT proves to be a cost-effective tool for monitoring vaccine interest and sentiment, offering granular insights for targeted public health messaging. The authors recommend leveraging these findings to design region-specific interventions that counter misinformation and facilitate vaccine access.

Critical opinion:

This study makes a valuable contribution by integrating infodemiology with behavioral theory to address vaccine hesitancy—a pressing global health challenge. Its methodological rigor, particularly the use of Z-score normalization and EHBM constructs, strengthens the validity of findings. However, several limitations warrant attention. First, reliance on GT data introduces inherent biases: search behavior may not accurately reflect attitudes or intentions, and the exclusion of non-English queries risks underrepresenting linguistic minorities in Canada. Second, sentiment classification based solely on query text lacks contextual nuance; for instance, a query like “MMR vaccine safe” could stem from either skepticism or reassurance-seeking. Incorporating qualitative analysis or triangulating with social media data could enhance interpretive depth.

Moreover, while the study identifies regional variations, it stops short of exploring underlying socio-demographic factors (e.g., education, income, cultural beliefs) that may drive these patterns. Future research should integrate GT with survey-based approaches to validate assumptions about intent and sentiment. Finally, the authors’ recommendations—such as interactive clinic locators and myth-busting content—are practical but require evaluation for effectiveness in real-world settings.

In sum, Jokar et al. provide a timely and innovative framework for monitoring vaccine sentiment, but its utility hinges on complementing digital surveillance with context-rich data. As misinformation continues to evolve, multi-modal strategies combining infodemiology, behavioral insights, and community engagement will be essential for sustaining vaccine confidence.

By Health Literacy Asia