{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T07:17:13Z","timestamp":1790666233686,"version":"4.1.0"},"reference-count":45,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2018,11,16]],"date-time":"2018-11-16T00:00:00Z","timestamp":1542326400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Zoonoses and Public Health"],"published-print":{"date-parts":[[2019,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Online activity\u2010based epidemiological surveillance and forecasting is getting more and more attention. To date, Google search volumes have not been assessed for forecasting of tick\u2010borne diseases. Thus, we performed an analysis of forecasting of the Lyme disease incidence based on the traditional data extended with Google Trends.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Data on the weekly incidence of Lyme disease in Germany from 16 June 2013 to 27 May 2018 were obtained from the database of the Robert Koch Institute. Data of Internet searches were obtained from Google Trends searching \u201cBorreliose\u201d in Germany for the \u201clast 5\u00a0years\u201d as a timespan category. Data were split into the training (from 16 June 2013 to 11 June 2017) and validation (from 12 June 2017, to 27 May 2018) data sets. A seasonal autoregressive moving average model, SARIMA (0,1,1) (0,1,1) [52] model was selected to describe the time series of the weekly Lyme incidence. After this, we added the Google Trends data as an external regressor and identified the SARIMA (0,1,1) (0,1,1) [52] model as optimal. We made predictions for the validation interval using these two models and compared predictions with the values of the validation data set.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Forecasting for the validation timespan resulted in similar values for the models. Comparing the forecasted values with the reported ones resulted in an residual mean squared error (RMSE) of 0.3763; the mean absolute percentage error (MAPE) was 8.233 for the model without Google searches with an RMSE of 0.3732; and the MAPE was 8.17495 for the Google Trends values\u2010expanded model. The difference between the predictive performances was insignificant (Diebold\u2010Mariano Test,\n                      <jats:italic>p<\/jats:italic>\n                      \u2010value\u00a0=\u00a00.4152).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Google Trends data are a good correlate of the reported incidence of Lyme disease in Germany, but it failed to significantly improve the forecasting accuracy in models based on traditional data.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/zph.12539","type":"journal-article","created":{"date-parts":[[2018,11,16]],"date-time":"2018-11-16T21:30:05Z","timestamp":1542403805000},"page":"101-107","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Can Google Trends data improve forecasting of Lyme disease incidence?"],"prefix":"10.1111","volume":"66","author":[{"given":"M\u00e1t\u00e9","family":"Kapit\u00e1ny\u2010F\u00f6v\u00e9ny","sequence":"first","affiliation":[{"name":"Faculty of Health Sciences Semmelweis University Budapest Hungary"},{"name":"Ny\u00edr\u0151 Gyula National Institute of Psychiatry and Addictions Budapest Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tam\u00e1s","family":"Ferenci","sequence":"additional","affiliation":[{"name":"John von Neumann Faculty of Informatics, Physiological Controls Group \u00d3buda University Budapest Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zita","family":"Sulyok","sequence":"additional","affiliation":[{"name":"Institute of Tropical Medicine Eberhard Karls University T\u00fcbingen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josua","family":"Kegele","sequence":"additional","affiliation":[{"name":"Department of Neurology and Epileptology Neurology Clinics, Eberhard Karls University T\u00fcbingen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hardy","family":"Richter","sequence":"additional","affiliation":[{"name":"Department of Neurology and Stroke Neurology Clinics, Eberhard Karls University T\u00fcbingen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Istv\u00e1n","family":"V\u00e1lyi\u2010Nagy","sequence":"additional","affiliation":[{"name":"South\u2010Pest Central Hospital National Institute of Hematology and Infectious Diseases Budapest Hungary"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6960-5126","authenticated-orcid":false,"given":"Mih\u00e1ly","family":"Sulyok","sequence":"additional","affiliation":[{"name":"Department of Neurology and Stroke Neurology Clinics, Eberhard Karls University T\u00fcbingen Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,11,16]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.2196\/publichealth.9136"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40249-015-0090-9"},{"issue":"4","key":"e_1_2_8_4_1","first-page":"327","article-title":"Use of time\u2010series analysis in infectious disease surveillance","volume":"76","author":"Allard R.","year":"1998","journal-title":"Bulletin of the World Health Organization"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/5584_2016_132"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pntd.0001258"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajem.2014.05.052"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1111\/nyas.13950"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/zph.12448"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0023610"},{"key":"e_1_2_8_11_1","unstructured":"Copeland P. 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