COVID-19 Forecasts Using Internet Search Information in the United States

As the COVID-19 ravaging through the globe, accurate forecasts of the disease spread is crucial for situational awareness, resource allocation, and public health decision-making. Alternative to the traditional disease surveillance data collected by the United States (US) Centers for Disease Control and Prevention (CDC), big data from Internet such as online search volumes has been previously shown to contain valuable information for tracking infectious disease dynamics. In this study, we evaluate the feasibility of using Internet search volume of relevant queries to track and predict COVID-19 pandemic. We found strong association between COVID-19 death trend and the search volume of symptom-related queries such as "loss of taste". Then, we further develop an influenza-tracking model to predict future 4-week COVID-19 deaths on the US national level, by combining search volume information with COVID-19 time series information. Encouraged by the 20% error reduction on national level comparing to the baseline time series model, we additionally build state-level COVID-19 deaths models, leveraging the cross-state cross-resolution spatial temporal framework that pools information from search volume and COVID-19 reports across states, regions and the nation. These variants of ARGOX are then aggregated in a winner-takes-all ensemble fashion to produce the final state-level 4-week forecasts. Numerical experiments demonstrate that our method steadily outperforms time series baseline models, and achieves the state-of-the-art performance among the publicly available benchmark models. Overall, we show that disease dynamics and relevant public search behaviors co-evolve during the COVID-19 pandemic, and capturing their dependencies while leveraging historical cases/deaths as well as spatial-temporal cross-region information will enable stable and accurate US national and state-level forecasts.