{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T14:46:37Z","timestamp":1787237197821,"version":"build-2736575974"},"reference-count":24,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T00:00:00Z","timestamp":1699488000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Lincoln Memorial University","award":["N\/A (Intramural funds)"],"award-info":[{"award-number":["N\/A (Intramural funds)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pathogens"],"abstract":"<jats:p>Google Trends data can be informative for zoonotic disease incidences, including Lyme disease. However, the use of Google Trends for predictive purposes is underutilized. In this study, we demonstrate the potential to use Google Trends for zoonotic disease prediction by predicting monthly state-level Lyme disease case counts in the United States. We requested Lyme disease data for the years 2010\u20132021. We downloaded Google Trends search data on terms for Lyme disease, symptoms of Lyme disease, and diseases with similar symptoms to Lyme disease. For each search term, we built an expanding window negative binomial model that adjusted for seasonal differences using a lag term. Performance was measured by Root Mean Squared Errors (RMSEs) and the visual associations between observed and predicted case counts. The highest performing model had excellent predictive ability in some states, but performance varied across states. The highest performing models were for Lyme disease search terms, which indicates the high specificity of search terms. We outline challenges of using Google Trends data, including data availability and a mismatch between geographic units. We discuss opportunities for Google Trends data for One Health research, including prediction of additional zoonotic diseases and incorporating environmental and companion animal data. Lastly, we recommend that Google Trends be explored as an option for predicting other zoonotic diseases and incorporate other data streams that may improve predictive performance.<\/jats:p>","DOI":"10.3390\/pathogens12111332","type":"journal-article","created":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T03:42:08Z","timestamp":1699501328000},"page":"1332","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Challenges and Opportunities in One Health: Google Trends Search Data"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2915-2529","authenticated-orcid":false,"given":"Lauren","family":"Wisnieski","sequence":"first","affiliation":[{"name":"Richard A. Gillespie College of Veterinary Medicine, Lincoln Memorial University, Harrogate, TN 37752, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karen","family":"Gruszynski","sequence":"additional","affiliation":[{"name":"Richard A. Gillespie College of Veterinary Medicine, Lincoln Memorial University, Harrogate, TN 37752, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vina","family":"Faulkner","sequence":"additional","affiliation":[{"name":"Richard A. Gillespie College of Veterinary Medicine, Lincoln Memorial University, Harrogate, TN 37752, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barbara","family":"Shock","sequence":"additional","affiliation":[{"name":"School of Mathematics and Science, Lincoln Memorial University, Harrogate, TN 37752, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1126\/science.1248506","article-title":"The parable of Google Flu: Traps in big data analysis","volume":"343","author":"Lazer","year":"2014","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1016\/j.ijforecast.2020.12.006","article-title":"Transparent modeling of influenza incidence: Big data or a single data point from psychological theory?","volume":"38","author":"Katsikopoulas","year":"2022","journal-title":"Int. J. Forecast."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1557","DOI":"10.1086\/630200","article-title":"Google Trends: A web-based tool for real-time surveillance of disease outbreaks","volume":"49","author":"Carneiro","year":"2009","journal-title":"Clin. Infect. Dis."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.envint.2018.05.016","article-title":"Using Google Trends and ambient temperature to predict seasonal influenza outbreaks","volume":"117","author":"Zhang","year":"2018","journal-title":"Environ. Int."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.1017\/S0950268818002078","article-title":"Prediction of Zika-confirmed cases in Brazil and Colombia using Google Trends","volume":"146","author":"Morsy","year":"2018","journal-title":"Epidemiol. Infect."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.1186\/s12889-021-11615-w","article-title":"The correlation between Google Trends and salmonellosis","volume":"21","author":"Wang","year":"2021","journal-title":"BMI Public Health"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e101306","DOI":"10.1016\/j.ttbdis.2019.101306","article-title":"Predicting tick-borne encephalitis using Google Trends","volume":"11","author":"Sulyok","year":"2020","journal-title":"Ticks Tick-Borne Dis."},{"key":"ref_8","first-page":"101","article-title":"Can Google Trends data improve forecasting of Lyme disease incidence?","volume":"66","author":"Ferenci","year":"2018","journal-title":"Zoonoses Public Health"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/S0140-6736(11)60103-7","article-title":"Lyme borreliosis","volume":"379","author":"Stanek","year":"2012","journal-title":"Lancet"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1093\/ilar\/ilx005","article-title":"Tick-borne zoonoses in the United States: Persistent and emerging threats to human health","volume":"58","author":"Eisen","year":"2017","journal-title":"ILAR J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"10942","DOI":"10.1073\/pnas.1204536109","article-title":"Deer, predators, and the emergence of Lyme disease","volume":"109","author":"Levi","year":"2012","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kilpatrick, A.M., Dobson, A.D.M., Levi, T., Salkeld, D.J., Swei, A., Ginsberg, H.S., Kjemtrup, A., Padgett, K.A., Jensen, P.M., and Fish, D. (2017). Lyme disease ecology in a changing world: Consensus, uncertainty and critical gaps for improving control. Philos. Trans. R. Soc. B Biol. Sci., 372.","DOI":"10.1098\/rstb.2016.0117"},{"key":"ref_13","first-page":"473","article-title":"Lyme disease pathogenesis","volume":"42","author":"Coburn","year":"2021","journal-title":"Curr. Issues Mol. Biol."},{"key":"ref_14","first-page":"m0141","article-title":"Lyme borreliosis: Diagnosis and management","volume":"369","author":"Kullberg","year":"2020","journal-title":"BMJ"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.3201\/eid2806.211335","article-title":"Economic burden of reported Lyme disease in high-incidence areas, United States, 2014\u20132016","volume":"28","author":"Hook","year":"2022","journal-title":"Emerg. Infect. Dis."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"616","DOI":"10.3201\/eid2702.202731","article-title":"Estimating the frequency of Lyme disease diagnosis, United States, 2010\u20132018","volume":"27","author":"Kugeler","year":"2021","journal-title":"Emerg. Infect. Dis."},{"key":"ref_17","first-page":"e22","article-title":"Surveillance for Lyme disease- United States, 2008\u20132015","volume":"66","author":"Schwartz","year":"2017","journal-title":"MMWR"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"395","DOI":"10.3389\/fpubh.2020.00395","article-title":"Spatial and temporal comparison of perceived risks and confirmed cases of Lyme Disease: An exploratory study of google trends","volume":"8","author":"Kim","year":"2020","journal-title":"Front. Public Health"},{"key":"ref_19","unstructured":"(2023, August 03). Surveillance Data, Available online: https:\/\/www.cdc.gov\/lyme\/datasurveillance\/surveillance-data.html?CDC_AA_refVal=https%3A%2F%2Fwww.cdc.gov%2Flyme%2Fstats%2Fgraphs.html."},{"key":"ref_20","unstructured":"R Core Team (2021). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing."},{"key":"ref_21","unstructured":"Massicotte, P., and Eddelbuettel, D. (2023, August 03). Available online: https:\/\/CRAN.R-project.org\/package=gtrendsR."},{"key":"ref_22","unstructured":"StataCorp (2021). Stata Statistical Software: Release 17, StataCorp LLC."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kuhn, M., and Johnson, K. (2013). Applied Predictive Modeling, Springer Nature.","DOI":"10.1007\/978-1-4614-6849-3"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Stricker, R.B., and Johnson, L. (2014). Lyme disease: Call for a \u201cManhattan Project\u201d to combat the epidemic. PLOS Pathog., 10.","DOI":"10.1371\/journal.ppat.1003796"}],"container-title":["Pathogens"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-0817\/12\/11\/1332\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:19:42Z","timestamp":1760131182000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-0817\/12\/11\/1332"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,9]]},"references-count":24,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["pathogens12111332"],"URL":"https:\/\/doi.org\/10.3390\/pathogens12111332","relation":{},"ISSN":["2076-0817"],"issn-type":[{"value":"2076-0817","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,9]]}}}