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Megha Mummalaneni, Speaker at Immunology Conferences
University of Nevada, Reno, School of Medicine , United States
Title : Using Google Trends to Gain Insight Into COVID-19 Hesitancy Distribution in the United States


Google Trends, launched on May 11, 2006, serves as a valuable research tool across various healthcare topics. It analyzes daily Google Searches in the United States, offering geographic and temporal pattern data for searched terms (1). Instead of surveying user-stated preferences, it provides insight into actual user behaviors (2). Widely regarded as a reliable, fast, and cost-effective data collection method, Google Trends is utilized in social sciences research (2). However, concerns about data reproducibility and inconsistencies have been raised over time (3). In the context of the ongoing debate on vaccines in the United States, vaccine hesitancy, ranging from acceptance to refusal, has gained prominence (4). Research indicates a significant rise in vaccine hesitancy, particularly in the Americas compared to Europe and the Western Pacific Region (4). This study aimed to determine whether Google Trends data can predict vaccine hesitancy in the United States by comparing it with CDC reports. It was hypothesized that specific Google Trends search terms would accurately reflect the COVID-19 vaccine hesitancy distribution reported by the CDC.

CDC county vaccination hesitancy rates were averaged for each state as a control (5). After comparing multiple search terms, the two with the highest search rates during the same period as the CDC study were “COVID vaccine near me” and “COVID vaccine side effects”. For each search term, the relative search rate on a scale from 1 to 100 was obtained from Google Trends. A linear regression was calculated between the values for each state for the search term “COVID vaccine side effects” and the average vaccination hesitancy from CDC. The search term “COVID vaccine near me” represented the inverse of vaccination hesitancy, so the values were subtracted from 100 to represent vaccination hesitancy. Following that subtraction, a linear regression was calculated between the values and the CDC vaccination hesitancy values.

There was no significant correlation between CDC vaccination hesitancy and the Google Trends search term "COVID vaccine near me" (F1,48 = 2.19, p > .05). However, a significant correlation existed between CDC vaccination hesitancy and the search term "COVID vaccine side effects" (F1,47 = 17.9, p < .05), emphasizing the need for refining search terms in Google Trends. Limitations of Google Trends include assumed correlations between chosen search terms and negative connotations of vaccine hesitancy (6). For instance, searches for "COVID vaccine side effects" could indicate preparation for vaccination or hesitancy. Despite these limitations, Google Trends offers nuanced insights into public sentiment on topics like COVID-19 vaccine hesitancy, avoiding the Hawthorne Effect present in traditional surveys (7). Utilizing anonymized Google Search Histories, it captures unfiltered public sentiments on debated topics (6). Future studies could compare Google Trends data with research on political affiliations regarding attitudes toward COVID-19 vaccination, distinguishing between pro and anti-vaccine sentiments.


Megha graduated from the University of Nevada, Reno with a BS in Neuroscience in May of 2023. She is currently a first-year medical student at the University of Nevada, Reno, School of Medicine with an interest in the specialty of Psychiatry. She is working in a research group under Dr. John Westhoff, MPH, MD – Assistant Dean of Student Research and board-certified Emergency Medicine Physician.