{"id":{"repo_id":"utc","oai_identifier":"oai:scholar.utc.edu:theses-1973"},"canonical_url":"https://search.dev.ndltd.org/etd/utc/oai:scholar.utc.edu:theses-1973","repository":{"repo_id":"utc","name":"University of Tennessee - Chattanooga","base_url":"https://scholar.utc.edu/do/oai/"},"display":{"title":"Utilizing Google Trends data for effective modeling of COVID-19 outcomes: a vector auto regression (VAR) approach","abstract":"Over the past decade, especially during the three-year COVID-19 epidemic, infodemiology, which uses web-based data for public health issues, has helped assess and anticipate human behavior. Google's real-time population data can indicate popular interest during a pandemic. This study analyzes Google Trends (GT) data of COVID-19-related search phrases and CDC information to forecast US daily new cases, cumulative cases, and deaths. Seasonality and trends are removed from trend data using an Augmented Dickey-Fuller (ADF) test. Using an eight-week forecast horizon, Granger Causality tests and Vector Auto Regression (VAR) models predict COVID-19 cases and deaths. GT's relative search volume of COVID-19 keywords and CDC's daily confirmed cases and cumulative deaths determine VAR model input search terms. RMSE (root mean square error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and MASE (Mean Absolute Scaled Error) were used to compare forecast accuracies. Also analyzed are Long-Covid search trends.","abstract_html":"Over the past decade, especially during the three-year COVID-19 epidemic, infodemiology, which uses web-based data for public health issues, has helped assess and anticipate human behavior. Google's real-time population data can indicate popular interest during a pandemic. This study analyzes Google Trends (GT) data of COVID-19-related search phrases and CDC information to forecast US daily new cases, cumulative cases, and deaths. Seasonality and trends are removed from trend data using an Augmented Dickey-Fuller (ADF) test. Using an eight-week forecast horizon, Granger Causality tests and Vector Auto Regression (VAR) models predict COVID-19 cases and deaths. GT's relative search volume of COVID-19 keywords and CDC's daily confirmed cases and cumulative deaths determine VAR model input search terms. RMSE (root mean square error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and MASE (Mean Absolute Scaled Error) were used to compare forecast accuracies. Also analyzed are Long-Covid search trends.","abstract_has_math":false,"creators":["Osei, Gertrude"],"institution":"University of Tennessee at Chattanooga","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Gao, Lani","Ma, Ziwei; Barioli, Francesco","College of Arts and Sciences"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T05:47:06Z","subjects":["Internet in medicine--United States","COVID-19 Pandemic, 2020---Social aspects--United States","Data mining"],"languages":["English","eng"],"rights":[],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.utc.edu/theses/809","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Gao, Lani","Ma, Ziwei; Barioli, Francesco","College of Arts and Sciences"]},{"key":"dc:creator","label":"Author","values":["Osei, Gertrude"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05-01T07:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"]},{"key":"dc:relation","label":"Dc Relation","values":["Masters Theses and Doctoral Dissertations"]},{"key":"dc:type","label":"Dc Type","values":["Masters theses","Text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Internet in medicine--United States","COVID-19 Pandemic, 2020---Social aspects--United States","Data mining"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholar.utc.edu/theses/809"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Dept. of Mathematics","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."]},{"key":"dc:description.abstract","label":"Abstract","values":["Over the past decade, especially during the three-year COVID-19 epidemic, infodemiology, which uses web-based data for public health issues, has helped assess and anticipate human behavior. Google's real-time population data can indicate popular interest during a pandemic. This study analyzes Google Trends (GT) data of COVID-19-related search phrases and CDC information to forecast US daily new cases, cumulative cases, and deaths. Seasonality and trends are removed from trend data using an Augmented Dickey-Fuller (ADF) test. Using an eight-week forecast horizon, Granger Causality tests and Vector Auto Regression (VAR) models predict COVID-19 cases and deaths. GT's relative search volume of COVID-19 keywords and CDC's daily confirmed cases and cumulative deaths determine VAR model input search terms. RMSE (root mean square error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and MASE (Mean Absolute Scaled Error) were used to compare forecast accuracies. Also analyzed are Long-Covid search trends."]},{"key":"dc:title","label":"Title","values":["Utilizing Google Trends data for effective modeling of COVID-19 outcomes: a vector auto regression (VAR) approach"]}]}],"canonical_facts":{"dc:contributor":["Gao, Lani","Ma, Ziwei; Barioli, Francesco","College of Arts and Sciences"],"dc:creator":["Osei, Gertrude"],"dc:date":["2023-05-01T07:00:00Z"],"dc:description":["Dept. of Mathematics","M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science."],"dc:description.abstract":["Over the past decade, especially during the three-year COVID-19 epidemic, infodemiology, which uses web-based data for public health issues, has helped assess and anticipate human behavior. Google's real-time population data can indicate popular interest during a pandemic. This study analyzes Google Trends (GT) data of COVID-19-related search phrases and CDC information to forecast US daily new cases, cumulative cases, and deaths. Seasonality and trends are removed from trend data using an Augmented Dickey-Fuller (ADF) test. Using an eight-week forecast horizon, Granger Causality tests and Vector Auto Regression (VAR) models predict COVID-19 cases and deaths. GT's relative search volume of COVID-19 keywords and CDC's daily confirmed cases and cumulative deaths determine VAR model input search terms. RMSE (root mean square error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and MASE (Mean Absolute Scaled Error) were used to compare forecast accuracies. Also analyzed are Long-Covid search trends."],"dc:identifier":["https://scholar.utc.edu/theses/809"],"dc:language":["English","eng"],"dc:publisher":["University of Tennessee at Chattanooga","Chattanooga (Tenn.)"],"dc:relation":["Masters Theses and Doctoral Dissertations"],"dc:rights":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Internet in medicine--United States","COVID-19 Pandemic, 2020---Social aspects--United States","Data mining"],"dc:title":["Utilizing Google Trends data for effective modeling of COVID-19 outcomes: a vector auto regression (VAR) approach"],"dc:type":["Masters theses","Text"]},"updated_at":"2026-07-24T05:47:06Z"}