Ma, Ziwei; Barioli, Francesco
College of Arts and Sciences
University of Tennessee at Chattanooga
Place of Publication
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.
This work was supported by Dr. Lani Gao's grant, Sim Center CEACSE grant (2022-2023). I would like to thank my esteemed supervisor – Dr. Lani Gao (Department of Mathematics, UTC) for her invaluable supervision and support. To Dr. Ziwei Ma (Department of Mathematics, UTC) and Dr. Francesco Barioli (Department of Mathematics, UTC), thank you very much for your support to get this work done.
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.
Internet in medicine--United States; COVID-19 Pandemic, 2020---Social aspects--United States; Data mining
xi, 72 leaves
Osei, Gertrude, "Utilizing Google Trends data for effective modeling of COVID-19 outcomes: a vector auto regression (VAR) approach" (2023). Masters Theses and Doctoral Dissertations.