  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.
