A Forecast Model for COVID-19 Spread Trends Using Blog and GPS Data from Smartphones

This study investigates the feasibility of using GPS data and frequency of COVID-19-related blog words to forecast new infection trends through a linear regression analysis. By employing time series' trend decomposition and Spearman's rank correlation, we identify and select a set of significant variables from the GPS and blog data to construct two models: a fixed-period model and a sequential adaptive model that updates with each new wave of infections. Our findings reveal that the adaptive model more effectively captures long-term trends, achieving approximately 90% accuracy in forecasting infection rates seven days in advance. Despite challenges in forecasting exact values, this research demonstrates that combining GPS and blog data through a dynamic, wave-based learning model offers a promising direction for enhancing the forecasting accuracy of COVID-19 spread. This approach has significant implications for public health preparedness.