Using web search queries to monitor influenza-like illness: an exploratory retrospective analysis, Netherlands, 2017/18 influenza season

Abstract

Background Despite the early development of Google Flu Trends in 2009, standards for digital epidemiology methods have not been established and research from European countries is scarce. Aim In this article, we study the use of web search queries to monitor influenza-like illness (ILI) rates in the Netherlands in real time. Methods In this retrospective analysis, we simulated the weekly use of a prediction model for estimating the then-current ILI incidence across the 2017/18 influenza season solely based on Google search query data. We used weekly ILI data as reported to The European Surveillance System (TESSY) each week, and we removed the then-last 4 weeks from our dataset. We then fitted a prediction model based on the then-most-recent search query data from Google Trends to fill the 4-week gap (‘Nowcasting’). Lasso regression, in combination with cross-validation, was applied to select predictors and to fit the 52 models, one for each week of the season. Results The models provided accurate predictions with a mean and maximum absolute error of 1.40 (95% confidence interval: 1.09–1.75) and 6.36 per 10,000 population. The onset, peak and end of the epidemic were predicted with an error of 1, 3 and 2 weeks, respectively. The number of search terms retained as predictors ranged from three to five, with one keyword, ‘griep’ (‘flu’), having the most weight in all models. Discussion This study demonstrates the feasibility of accurate, real-time ILI incidence predictions in the Netherlands using Google search query data.

Analyzing Citizens’ and Health Care Professionals’ Searches for Smell/Taste Disorders and Coronavirus in Finland During the COVID-19 Pandemic: Infodemiological Approach Using Database Logs

“…However, predicting the course of the pandemic may be difficult due to a variety of factors that have been found to contribute to an infectious disease outbreak [ 5 , 8 ]. Previously, data from search engines have generated high hopes for contributing to outbreak surveillance [ 7 , 9 - 11 ]. For example, for influenza outbreaks, Google Trends and Google Flu Trends have been used as models for predicting incidences of the disease [ 10 , 11 ].…”

“…Previously, data from search engines have generated high hopes for contributing to outbreak surveillance [ 7 , 9 - 11 ]. For example, for influenza outbreaks, Google Trends and Google Flu Trends have been used as models for predicting incidences of the disease [ 10 , 11 ]. In China, an increase in internet searches on coronavirus was observed 5-10 days before the disease outbreak and was found to predict an increase in suspected and laboratory-confirmed COVID-19 cases [ 8 ].…”

Digital Surveillance Through an Online Decision Support Tool for COVID-19 Over One Year of the Pandemic in Italy: Observational Study

“…Digital data have already been used in prediction models for influenza. Through the analysis of the volume of online searches for flu-related terms, Google Flutrends offered a forecast of the trend in influenza-like illness cases in the United States [35], and a similar approach has recently been used in the Netherlands [36]. Data for participatory surveillance have also been integrated with classic surveillance data [37].…”

Trends of Online Search of COVID-19 Related Terms in Cyprus

“…Moreover, the sample characteristics (i.e., age, gender) are unknown. However, Google Trends have been useful in predicting the onset of seasonal influenza and other infectious disease epidemics [30][31][32]. In addition, the search query used in this study was general, covering all COVID-19-related terms (e.g., vaccines, therapies), and universal, permitting comparisons with similar data in different countries, while the search volume used was sufficient to provide evidence [33] in regions with high internet access.…”