Use Internet search data to accurately track state level influenza epidemics

Abstract

For epidemics control and prevention, timely insights of potential hot spots are invaluable. Alternative to traditional epidemic surveillance, which often lags behind real time by weeks, big data from the Internet provide important information of the current epidemic trends. Here we present a methodology, ARGOX (Augmented Regression with GOogle data CROSS space), for accurate real-time tracking of state-level influenza epidemics in the United States. ARGOX combines Internet search data at the national, regional and state levels with traditional influenza surveillance data from the Centers for Disease Control and Prevention, and accounts for both the spatial correlation structure of state-level influenza activities and the evolution of people’s Internet search pattern. ARGOX achieves on average 28% error reduction over the best alternative for real-time state-level influenza estimation for 2014 to 2020. ARGOX is robust and reliable and can be potentially applied to track county- and city-level influenza activity and other infectious diseases.

Joint COVID-19 and influenza-like illness forecasts in the United States using internet search information

“…We use 23 highly correlated COVID-19 related Google search queries discovered in prior study 44 (in daily frequency) for COVID-19 cases and deaths forecasts, while using ILI related queries (weekly frequency) from previous study 22,24 for %ILI forecasts. We obtain the search queries for national, regional (summation from states) and state level.…”

“…For COVID-19, our raw estimates for state m week τ cases/deaths y τ,m are ŷGT τ;m , ŷreg τ;r m , ŷnat τ , and y τ−1,m , where r m is the region number for state m. Here, we denote GT and reg to be state/regional estimates with internet search information only, and nat to be national estimates (same as prior study 44 ). Similarly, we obtain the raw estimates for state m weekly %ILI p τ,m : pGT τ;m 24 , preg τ;r m 23 , pnat τ 22 , p τ−1,m . In the second step, we fit two models separately using the raw estimates from step 1 as inputs.…”

“…For COVID-19 cases/deaths predictions, bi-disease ARGOX-Local incorporates neighboring state's %ILI information; similarly for %ILI predictions, bi-disease ARGOX-Local includes neighboring state's COVID-19 cases. Besides bi-disease ARGOX-Local, we also directly employ the previously proposed single-disease forecasting models for COVID-19 44 and %ILI 24 in the second step, since they have already demonstrated robust results prior to the newly emerged bi-disease dynamics.…”

“…Numerous ILI tracking approaches have therefore been proposed, utilizing statistical models 6,7 , mechanistic models such as compartmental models [8][9][10][11] , ensemble approaches 12 , and deep learning models 13,14 . Several approaches rely on external signals such as environmental conditions and weather reports 15,16 ; social media, such as Twitter posts 17,18 and Wikipedia article views 19,20 ; search engine data, such as: Google [21][22][23][24][25] , Yahoo 26 , and Baidu internet searches 27 .…”

COVID-19 hospitalizations forecasts using internet search data

“…The detailed data collection procedure and subsequent data pre-processing (introduced in sections below) are illustrated in the flowchart (Figure S1). In step 1 (green highlighted boxes in Figure S1), to curate the pool of potentially predictive queries, we first started with 129 influenza (flu) related queries based on prior studies 9 , 24 , 25 . Then, we changed “Influnza” and “Flu” keywords to “Coronavirus” and “COVID-19”, respectively.…”