{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T16:49:09Z","timestamp":1772210949422,"version":"3.50.1"},"reference-count":48,"publisher":"Public Library of Science (PLoS)","issue":"3","license":[{"start":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T00:00:00Z","timestamp":1646870400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>\n                    Lyme disease is the most widely reported vector-borne disease in the United States. 95% of confirmed human cases are reported in the Northeast and upper Midwest (25,778 total confirmed cases from Northeast and upper Midwest \/ 27,203 total US confirmed cases). Human cases typically occur in the spring and summer months when an infected nymph\n                    <jats:italic>Ixodid<\/jats:italic>\n                    tick takes a blood meal. Current federal surveillance strategies report data on an annual basis, leading to nearly a year lag in national data reporting. These lags in reporting make it difficult for public health agencies to assess and plan for the current burden of Lyme disease. Implementation of a nowcasting model, using historical data to predict current trends, provides a means for public health agencies to evaluate current Lyme disease burden and make timely priority-based budgeting decisions. The objective of the study was to develop and compare the performance of nowcasting models using free data from Google Trends and Centers of Disease Control and Prevention surveillance reports. We developed two sets of elastic net models for five regions of the United States: 1. Using only monthly proportional hit data from the 21 disease symptoms and tick related terms, and 2. Using monthly proportional hit data from terms identified via Google correlate and the disease symptom and vector terms. Elastic net models using the full-term list were highly accurate (Root Mean Square Error: 0.74, Mean Absolute Error: 0.52, R\n                    <jats:sup>2<\/jats:sup>\n                    : 0.97) for four of the five regions of the United States and improved accuracy 1.33-fold while reducing error 0.5-fold compared to predictions from models using disease symptom and vector terms alone. Many of the terms included and found to be important for model performance were environmentally related. These models can be implemented to help local and state public health agencies accurately monitor Lyme disease burden during times of reporting lag from federal public health reporting agencies.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0251165","type":"journal-article","created":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T13:38:12Z","timestamp":1646919492000},"page":"e0251165","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Inclusion of environmentally themed search terms improves Elastic net regression nowcasts of regional Lyme disease rates"],"prefix":"10.1371","volume":"17","author":[{"given":"Eric","family":"Kontowicz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Grant","family":"Brown","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James","family":"Torner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Margaret","family":"Carrel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kelly K.","family":"Baker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7285-4254","authenticated-orcid":true,"given":"Christine A.","family":"Petersen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2022,3,10]]},"reference":[{"issue":"9","key":"pone.0251165.ref001","doi-asserted-by":"crossref","first-page":"1089","DOI":"10.1086\/508667","article-title":"The clinical assessment, treatment, and prevention of lyme disease, human granulocytic anaplasmosis, and babesiosis: clinical practice guidelines by the Infectious Diseases Society of America","volume":"43","author":"GP Wormser","year":"2006","journal-title":"Clin Infect Dis"},{"issue":"2","key":"pone.0251165.ref002","first-page":"187","article-title":"Epidemiology of Lyme disease.","volume":"29","author":"PS Mead","year":"2015","journal-title":"Infectious Disease Clinics."},{"issue":"1","key":"pone.0251165.ref003","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/BF01200454","article-title":"Transmission of Lyme disease spirochetes (Borrelia burgdorferi)","volume":"7","author":"J. Piesman","year":"1989","journal-title":"Experimental & applied acarology"},{"issue":"4","key":"pone.0251165.ref004","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.tree.2012.10.011","article-title":"Biodiversity and disease: a synthesis of ecological perspectives on Lyme disease transmission","volume":"28","author":"CL Wood","year":"2013","journal-title":"Trends in ecology & evolution"},{"issue":"10","key":"pone.0251165.ref005","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1016\/S1286-4579(01)01435-6","article-title":"Analysis of Borrelia burgdorferi gene expression during life cycle phases of the tick vector Ixodes scapularis","volume":"3","author":"RD Gilmore","year":"2001","journal-title":"Microbes and infection"},{"issue":"11","key":"pone.0251165.ref006","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1093\/oxfordjournals.aje.a008879","article-title":"Entomologic index for human risk of Lyme disease","volume":"144","author":"TN Mather","year":"1996","journal-title":"American Journal of Epidemiology"},{"issue":"2","key":"pone.0251165.ref007","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1093\/jmedent\/31.2.310","article-title":"Survival of immature Ixodes scapularis (Acari: Ixodidae) at different relative humidities.","volume":"31","author":"KC Stafford","year":"1994","journal-title":"Journal of medical entomology"},{"issue":"6","key":"pone.0251165.ref008","doi-asserted-by":"crossref","first-page":"1260","DOI":"10.1093\/jme\/tjv132","article-title":"Seasonal activity, density, and collection efficiency of the blacklegged tick (Ixodes scapularis)(Acari: Ixodidae) in mid-western Pennsylvania.","volume":"52","author":"T Simmons","year":"2015","journal-title":"Journal of medical entomology"},{"issue":"1","key":"pone.0251165.ref009","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1186\/1756-3305-7-181","article-title":"Adverse moisture events predict seasonal abundance of Lyme disease vector ticks (Ixodes scapularis).","volume":"7","author":"KA Berger","year":"2014","journal-title":"Parasites & vectors"},{"issue":"3","key":"pone.0251165.ref010","doi-asserted-by":"crossref","first-page":"289","DOI":"10.3201\/eid0803.010166","article-title":"Predicting the risk of Lyme disease: habitat suitability for Ixodes scapularis in the north central United States","volume":"8","author":"M Guerra","year":"2002","journal-title":"Emerging infectious diseases"},{"issue":"8","key":"pone.0251165.ref011","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1172\/JCI21681","article-title":"The emergence of Lyme disease","volume":"113","author":"AC Steere","year":"2004","journal-title":"The Journal of clinical investigation"},{"issue":"2","key":"pone.0251165.ref012","doi-asserted-by":"crossref","first-page":"e0116767","DOI":"10.1371\/journal.pone.0116767","article-title":"Health care costs, utilization and patterns of care following Lyme disease.","volume":"10","author":"ER Adrion","year":"2015","journal-title":"PloS one"},{"issue":"5","key":"pone.0251165.ref013","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1016\/S0149-2918(98)80081-7","article-title":"A cost-of-illness study of Lyme disease in the United States.","volume":"20","author":"E Maes","year":"1998","journal-title":"Clinical Therapeutics."},{"issue":"22","key":"pone.0251165.ref014","doi-asserted-by":"crossref","first-page":"1","DOI":"10.15585\/mmwr.ss6622a1","article-title":"Surveillance for Lyme disease\u2014United States, 2008\u20132015.","volume":"66","author":"AM Schwartz","year":"2017","journal-title":"MMWR Surveillance Summaries."},{"key":"pone.0251165.ref015","article-title":"Lyme disease Surveillance and available data 2020 [updated November 22, 2019.","author":"CDC","journal-title":"Available from: Lyme disease surveillance and available data"},{"issue":"1","key":"pone.0251165.ref016","first-page":"25","article-title":"Local public health department funding: trends over time and relationship to health outcomes","volume":"107","author":"MC Boeke","year":"2008","journal-title":"Wisconsin Medical Journal (WMJ)"},{"issue":"1","key":"pone.0251165.ref017","first-page":"e1157","article-title":"infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the","volume":"11","author":"Eysenbach G. Infodemiology","year":"2009","journal-title":"Internet. Journal of medical Internet research"},{"issue":"9","key":"pone.0251165.ref018","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1016\/S0002-9343(02)01473-0","article-title":"Infodemiology: The epidemiology of (mis) information","volume":"113","author":"G. Eysenbach","year":"2002","journal-title":"The American journal of medicine"},{"key":"pone.0251165.ref019","article-title":"International Telecommunication Union","author":"R. Bureau","year":"2017","journal-title":"ICT Facts and Figures"},{"issue":"2","key":"pone.0251165.ref020","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1111\/hir.12216","article-title":"Trends of infodemiology studies: a scoping review.","volume":"35","author":"K Zeraatkar","year":"2018","journal-title":"Health Information & Libraries Journal"},{"issue":"2","key":"pone.0251165.ref021","doi-asserted-by":"crossref","first-page":"608","DOI":"10.21533\/pen.v7i2.442","article-title":"Review on nowcasting using least absolute shrinkage selector operator (LASSO) to predict dengue occurrence in San Juan and Iquitos as part of disease surveillance system.","volume":"7","author":"SL Tang","year":"2019","journal-title":"Periodicals of Engineering and Natural Sciences"},{"key":"pone.0251165.ref022","article-title":"Trending now: using social media to predict and track disease outbreaks.","author":"CW Schmidt","year":"2012","journal-title":"National Institute of Environmental Health Sciences"},{"issue":"11","key":"pone.0251165.ref023","doi-asserted-by":"crossref","first-page":"e1003892","DOI":"10.1371\/journal.pcbi.1003892","article-title":"Global disease monitoring and forecasting with Wikipedia","volume":"10","author":"N Generous","year":"2014","journal-title":"PLoS computational biology"},{"issue":"10","key":"pone.0251165.ref024","doi-asserted-by":"crossref","first-page":"206","DOI":"10.14745\/ccdr.v43i10a03","article-title":"Emerging Infections: Emerging infectious diseases: prediction and detection.","volume":"43","author":"N Ogden","year":"2017","journal-title":"Canada Communicable Disease Report"},{"issue":"5","key":"pone.0251165.ref025","doi-asserted-by":"crossref","first-page":"e0127084","DOI":"10.1371\/journal.pone.0127084","article-title":"Nowcasting unemployment rates with google searches: Evidence from the visegrad group countries.","volume":"10","author":"J Pavlicek","year":"2015","journal-title":"PloS one"},{"key":"pone.0251165.ref026","article-title":"Nowcasting obesity in the US using Google search volume data.","author":"S Sarigul","year":"2014"},{"issue":"1","key":"pone.0251165.ref027","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1093\/ijpor\/edq048","article-title":"Measuring the public agenda using search engine queries","volume":"23","author":"M Scharkow","year":"2011","journal-title":"International Journal of Public Opinion Research"},{"issue":"4","key":"pone.0251165.ref028","doi-asserted-by":"crossref","first-page":"295","DOI":"10.4161\/viru.24041","article-title":"Mathematical modeling of infectious disease dynamics.","volume":"4","author":"CI Siettos","year":"2013","journal-title":"Virulence"},{"issue":"2","key":"pone.0251165.ref029","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net.","volume":"67","author":"H Zou","year":"2005","journal-title":"Journal of the royal statistical society: series B (statistical methodology)."},{"issue":"7232","key":"pone.0251165.ref030","first-page":"1012","article-title":"Detecting influenza epidemics using search engine query data.","volume":"457","author":"J Ginsberg","year":"2009"},{"issue":"11","key":"pone.0251165.ref031","first-page":"1443","volume":"47","author":"PM Polgreen","year":"2008","journal-title":"Using internet searches for influenza surveillance"},{"issue":"2","key":"pone.0251165.ref032","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1111\/zph.12291","article-title":"Underreporting of Lyme and Other Tick\u2010Borne Diseases in Residents of a High\u2010Incidence County, Minnesota, 2009.","volume":"65","author":"E Schiffman","year":"2018","journal-title":"Zoonoses and public health"},{"key":"pone.0251165.ref033","unstructured":"United States Map. US Census Bureau Geography Division [Available from: https:\/\/www2.census.gov\/geo\/tiger\/TIGER2019\/ZCTA5\/]; 2019."},{"key":"pone.0251165.ref034","article-title":"Google correlate whitepaper.","author":"M Mohebbi","year":"2011"},{"key":"pone.0251165.ref035","author":"P Massicotte","year":"2016","journal-title":"Package \u2018gtrendsR\u2019"},{"key":"pone.0251165.ref036","unstructured":"Google Trends [Available from: https:\/\/trends.google.com\/trends\/?geo=US."},{"issue":"2","key":"pone.0251165.ref037","doi-asserted-by":"crossref","first-page":"e13439","DOI":"10.2196\/13439","article-title":"Google Trends in infodemiology and infoveillance: methodology framework.","volume":"5","author":"A Mavragani","year":"2019","journal-title":"JMIR public health and surveillance"},{"issue":"2","key":"pone.0251165.ref038","first-page":"140095","article-title":"Adaptive nowcasting of influenza outbreaks using Google searches.","volume":"1","author":"T Preis","year":"2014"},{"issue":"4","key":"pone.0251165.ref039","first-page":"1","article-title":"Technology","volume":"3","author":"V Lampos","year":"2012","journal-title":"Nowcasting events from the social web with statistical learning."},{"key":"pone.0251165.ref040","unstructured":"Kuhn M. The caret package 2012 [Available from: https:\/\/cran.r-project.org\/web\/packages\/caret\/caret.pdf."},{"key":"pone.0251165.ref041","author":"T Hastie","year":"2014","journal-title":"Glmnet vignette"},{"issue":"1","key":"pone.0251165.ref042","volume":"8","author":"M Kang","year":"2013","journal-title":"Using google trends for influenza surveillance in South China"},{"issue":"2","key":"pone.0251165.ref043","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/j.ttbdis.2017.11.006","article-title":"Risk factors for tick exposure in suburban settings in the Northeastern United States.","volume":"9","author":"P Mead","year":"2018","journal-title":"Ticks and tick-borne diseases"},{"issue":"5","key":"pone.0251165.ref044","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1016\/j.ttbdis.2019.06.004","article-title":"Time of year and outdoor recreation affect human exposure to ticks in California, United States.","volume":"10","author":"DJ Salkeld","year":"2019","journal-title":"Ticks and tick-borne diseases"},{"issue":"5","key":"pone.0251165.ref045","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1093\/oxfordjournals.aje.a115578","article-title":"Lyme disease in outdoor workers: risk factors, preventive measures, and tick removal methods","volume":"131","author":"BS Schwartz","year":"1990","journal-title":"American journal of epidemiology"},{"issue":"13","key":"pone.0251165.ref046","doi-asserted-by":"crossref","first-page":"1569","DOI":"10.1017\/S003118202000147X","article-title":"A comparative evaluation of northern and southern Ixodes scapularis questing height and hiding behaviour in the USA","volume":"147","author":"M Tietjen","year":"2020","journal-title":"Parasitology"},{"issue":"1","key":"pone.0251165.ref047","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1111\/zph.12539","article-title":"Can Google Trends data improve forecasting of Lyme disease incidence?","volume":"66","author":"M Kapit\u00e1ny\u2010F\u00f6v\u00e9ny","year":"2019","journal-title":"Zoonoses and public health"},{"issue":"1","key":"pone.0251165.ref048","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-020-0222-x","article-title":"Lymelight: forecasting Lyme disease risk using web search data.","volume":"3","author":"A Sadilek","year":"2020","journal-title":"npj Digital Medicine"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0251165","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,10]],"date-time":"2022-03-10T13:38:51Z","timestamp":1646919531000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0251165"}},"subtitle":[],"editor":[{"given":"Sam R.","family":"Telford","sequence":"first","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2022,3,10]]},"references-count":48,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,3,10]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0251165","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.04.22.440883","asserted-by":"object"}]},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,10]]}}}