Prediction of Asthma Hospitalizations for the Common Cold Using Google Trends: Infodemiology Study

Abstract

Background In contrast to air pollution and pollen exposure, data on the occurrence of the common cold are difficult to incorporate in models predicting asthma hospitalizations. Objective This study aims to assess whether web-based searches on common cold would correlate with and help to predict asthma hospitalizations. Methods We analyzed all hospitalizations with a main diagnosis of asthma occurring in 5 different countries (Portugal, Spain, Finland, Norway, and Brazil) for a period of approximately 5 years (January 1, 2012-December 17, 2016). Data on web-based searches on common cold were retrieved from Google Trends (GT) using the pseudo-influenza syndrome topic and local language search terms for common cold for the same countries and periods. We applied time series analysis methods to estimate the correlation between GT and hospitalization data. In addition, we built autoregressive models to forecast the weekly number of asthma hospitalizations for a period of 1 year (June 2015-June 2016) based on admissions and GT data from the 3 previous years. Results In time series analyses, GT data on common cold displayed strong correlations with asthma hospitalizations occurring in Portugal (correlation coefficients ranging from 0.63 to 0.73), Spain (ρ=0.82-0.84), and Brazil (ρ=0.77-0.83) and moderate correlations with those occurring in Norway (ρ=0.32-0.35) and Finland (ρ=0.44-0.47). Similar patterns were observed in the correlation between forecasted and observed asthma hospitalizations from June 2015 to June 2016, with the number of forecasted hospitalizations differing on average between 12% (Spain) and 33% (Norway) from observed hospitalizations. Conclusions Common cold–related web-based searches display moderate-to-strong correlations with asthma hospitalizations and may be useful in forecasting them.

Comparison of epidemiologic surveillance and Google Trends data on asthma and allergic rhinitis in England

“…The incidence of 'asthma' was strongly and positively correlated with that of 'URTI', but negatively correlated with that of 'AR' and with GTs data on 'pollen'/'AR'. These results are mostly explained by the different seasonality of AR and asthma, and suggest that, even though AR and pollen exposure may be linked to worsening asthma, viral infections may be a more relevant trigger for asthma exacerbations (as previously shown using GTs for 'common cold' and asthma hospitalizations in five countries 6 ).…”

Heightened religiosity proactively and reactively responds to the COVID-19 pandemic across the globe: Novel insights from the parasite-stress theory of sociality and the behavioral immune system theory

“…Thus, this research used Google Trends, a big data tool tracking real-time online searches on specific topics ( Arora et al, 2019 ), to capture people’s real-time intrinsic religiosity ( Alper, 2019 , Pelham et al, 2018 ). As detailed in recent studies, Google Trends data collection approaches ( Ma, 2022 , Ma and Ye, 2021a , Sousa-Pinto et al, 2020 , Sousa-Pinto et al, 2021 ) were followed.…”

“…As discussed in recent studies ( Ma, 2022 , Ma and Ye, 2021a , Ma and Ye, 2022 , Sousa-Pinto et al, 2020 , Sousa-Pinto et al, 2021 ), topic search terms were appropriate for cross-national comparative research, because such terms include the search volumes for all specific search terms that share similar concepts into the specific topics ( Choi & Varian, 2012 ), irrespective of which languages are used to search these terms ( Dilmaghani, 2020 , Yeung, 2019 ). Therefore, using topic search terms can 1) capture similar concepts related to the religious search terms (e.g., Christ was related to Jesus ), 2) rule out linguistic influences (e.g., different languages could be used to search for the religious terms), and 3) minimize the influences of noises (e.g., taboo words such as “Jesus Christ"; see Jay, 2009 ).…”

Infodemiology of RSV in Italy (2017–2022): An Alternative Option for the Surveillance of Incident Cases in Pediatric Age?

“…While some observational studies based on the regional database have provided some snapshots of the clinical data [ 4 , 19 , 21 , 25 , 26 , 27 , 28 , 29 ], alternative options for monitoring RSV infection trends are therefore required, at least until epidemiological and virological surveillance is able to deliver appropriate and reliable information. In recent years, infodemiology (i.e., the science of distribution and determinants of information in an electronic medium, specifically the Internet, or in a population, with the ultimate aim to inform public health and public policy) [ 30 ] and infoveillance (i.e., epidemic surveillance performed by means of infodemiology) [ 31 , 32 ] have emerged as effective tools in predicting outbreaks of several infectious diseases, ranging from influenza to COVID-19 [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. The rationale of infodemiology is that the appropriate analysis of research trends in specific search engines, web platforms and social media may reflect or even anticipate the epidemiological features of certain disorders [ 41 , 42 , 43 , 44 , 45 , 46 , 47 ].…”