Visualizing the Surge: Google Trends Analysis of Post-COVID Consumer Interest in Immunity-Boosting Herbal Beverages

Abstract

Abstract

The COVID-19 pandemic precipitated rapid shifts in consumer health behaviour, including heightened attention to immunity-enhancing foods and beverages. This short communication uses publicly available Google Trends data to quantify and visualize changes in public interest toward a set of herbal beverage terms before and after the onset of the pandemic. Monthly Google Trends data (Jan 2018 – Sept 2025) were retrieved for a targeted set of keywords covering broad terms, product-specific queries, and India-specific traditional terms. To enable cross-query comparability we included Vitamin C as an anchor term in every batch and applied anchor-based rescaling to all series.

Pre-COVID (Jan 2018–Dec 2019) and post-COVID (Jan 2021–Dec 2021) average monthly interests were computed and compared; 2020 was treated as a transition year and excluded from pre/post averages. Results are shown as a normalized time series and percent change in mean interest. Several terms including Vitamin-C, Ashwagandha, Kadha, and Immune booster spiked around March–June 2020, coincident with the COVID-19 pandemic declaration and early national lockdowns.

Quantitatively, many product and culture-specific terms demonstrated substantial percent increases from pre- to post-COVID. Ashwagandha and Vitamin-C show particularly large increases; some lifestyle/product terms (Turmeric latte / Elderberry syrup) show smaller or transient changes. Google Trends shows a rapid and sustained increase in public interest for herbal, immunity-related beverages after the pandemic onset.

These signals are useful as a rapid, reproducible proxy of consumer intent and market interest, with implications for public health messaging, consumer safety surveillance, and prioritizing targeted clinical research.

References

1. Adzhubei, I. A., Schmidt, S., Peshkin, L., Ramensky, V. E., Gerasimova, A., Bork, P., Kondrashov, A. S., & Sunyaev, S. R. (2010). A method and server for predicting damaging missense mutations. Nature Methods, 7(4), 248–249. https://doi.org/10.1038/nmeth0410-248


2. Amini-Rarani, M., Azami-Aghdash, S., Mousavi Isfahani, H., & Mohseni, M. (2024). Estimation of the prevalence of anxiety during the COVID-19 pandemic: A meta-analysis of meta-analyses. BMC Public Health, 24(1), 2831. https://doi.org/10.1186/s12889-024-19729-7


3. Cebrián, E., & Domenech, J. (2022). Is Google Trends a quality data source? Applied Economics Letters, 30(6), 811–815. https://doi.org/10.1080/13504851.2021.2023088


4. Dong, C., Wei, P., Jian, X., Gibbs, R., Boerwinkle, E., Wang, K., & Liu, X. (2015). Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies. Human Molecular Genetics, 24(8), 2125–2137. https://doi.org/10.1093/hmg/ddu733


5. Eysenbach, G. (2009). Infodemiology and infoveillance: Framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. Journal of Medical Internet Research, 11(1), e11. https://doi.org/10.2196/jmir.1157


6. Google LLC. Google Trends. Available from: https://trends.google.com. Accessed 15 Sept 2025.


7. Government of India. (2020). Ministry of AYUSH advisory on immunity-boosting measures for self-care during COVID-19 crisis. Retrieved from https://www.ayush.gov.in


8. Hawkins, J., Baker, C., Cherry, L., & Dunne, E. (2019). Black elderberry (Sambucus nigra) supplementation effectively treats upper respiratory symptoms: A meta-analysis of randomized, controlled clinical trials. Complementary Therapies in Medicine, 42, 361–365. https://doi.org/10.1016/j.ctim.2018.12.004


9. Hewlings, S. J., & Kalman, D. S. (2017). Curcumin: A review of its effects on human health. Foods, 6(10), 92. https://doi.org/10.3390/foods6100092


10. Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: Traps in big data analysis. Science, 343(6176), 1203–1205. https://doi.org/10.1126/science.1248506


11. Lin, L. Y., Husnayain, A., Chen, Y. T., & Kuo, C. Y. (2024). The association between interest of nutritional supplements and COVID-19 pandemic – evidence from Google Trends. BMC Public Health, 24(1), 109. https://doi.org/10.1186/s12889-023-17607-2


12. Mavragani, A., & Ochoa, G. (2019). Google Trends in infodemiology and infoveillance: Methodology framework. JMIR Public Health and Surveillance, 5(2), e13439. https://doi.org/10.2196/13439


13. Ng, P. C., & Henikoff, S. (2003). SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Research, 31(13), 3812–3814. https://doi.org/10.1093/nar/gkg509


14. Nuti, S. V., Wayda, B., Ranasinghe, I., Wang, S., Dreyer, R. P., Chen, S. I., & Murugiah, K. (2014). The use of Google Trends in health care research: A systematic review. PLOS One, 9(10), e109583. https://doi.org/10.1371/journal.pone.0109583


15. Office of Dietary Supplements (NIH). Ashwagandha—Health Professional Fact Sheet. Available from: https://ods.od.nih.gov/factsheets/Ashwagandha-HealthProfessional/. Accessed 15 Sept 2025.


16. Office of Dietary Supplements, National Institutes of Health (NIH). (2025). Dietary supplements: Consumer information. https://ods.od.nih.gov/


17. Rovetta, A. (2021). Reliability of Google Trends: Analysis of the limits and potential of web infoveillance during COVID-19 pandemic and for future research. Frontiers in Research Metrics and Analytics, 6, 670226. https://doi.org/10.3389/frma.2021.670226


18. Schwarz, J. M., Rödelsperger, C., Schuelke, M., & Seelow, D. (2014). MutationTaster2: Mutation prediction for the deep-sequencing age. Nature Methods, 11(4), 361–362. https://doi.org/10.1038/nmeth.2890


19. Thusberg, J., Olatubosun, A., & Vihinen, M. (2011). Performance of mutation pathogenicity prediction methods on missense variants. Human Mutation, 32(4), 358–368. https://doi.org/10.1002/humu.21445


20. Tiralongo, E., Wee, S. S., & Lea, R. A. (2016). Elderberry supplementation reduces cold duration and symptoms in air-travellers: A randomized, double-blind placebo-controlled clinical trial. Nutrients, 8(4), 182. https://doi.org/10.3390/nu8040182


21. Wieland, L. S., Piechotta, V., Feinberg, T., Ludeman, E., Hutton, B., Kanji, S., Seely, D., & Garritty, C. (2021). Elderberry for prevention and treatment of viral respiratory illnesses: A systematic review. BMC Complementary Medicine and Therapies, 21(1), 112. https://doi.org/10.1186/s12906-021-03283-5


22. Wieland, L. S., Piechotta, V., Feinberg, T., Ludeman, E., Hutton, B., Kanji, S., & Seely, D. (2021). Elderberry for prevention and treatment of viral respiratory illnesses: A systematic review and meta-analysis. BMC Complementary Medicine and Therapies, 21(1), 112. https://doi.org/10.1186/s12906-021-03283-5


23. World Health Organization (WHO). (2020). WHO Director-General’s opening remarks at the media briefing on COVID-19 – 11 March 2020. https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020