Maximum generable interest: A universal standard for Google Trends search queries.

The coronavirus or COVID-19 pandemic represents a health event with far-reaching global consequences, triggering a strong search interest in related topics on the Internet worldwide. The use of search engine data has become commonplace in research, but a universal standard for comparing different works is desirable to simplify the comparison. The coronavirus pandemic's enormous impact and media coverage have triggered an exceptionally high search interest. Consequently, the maximum generable interest (MGI) on coronavirus is proposed as a universal reference for objectifying and comparing relative search interest in the future. This search interest can be explored with search engine data such as Google Trends data. Additional standards for medium and low search volumes can also be used to reflect the search interest of topics at different levels. Size standards, such as reference to MGI, may help make research more comparable and better evaluate relative search volumes. This study presents a framework for this purpose using the example of stroke.

Can Google Trends analysis confirm the public's need for information about the rare association of facial nerve paralysis with COVID-19 or the COVID-19 vaccination?

Facial nerve paralysis or Bell's palsy have been suggested as possible consequences of SARS-CoV-2 infections, as well as possible side effects of COVID-19 vaccinations. Google Trends data have been used to evaluate worldwide levels of public awareness for these topics for pre- and post-pandemic years. The results demonstrate a relatively low public interest in facial nerve paralysis in comparison to other more common COVID-19 related topics. Some peaks of interest in Bell's palsy can most likely be explained as triggered by the media. Therefore, Google Trends has shown public's relatively low awareness of this rare neurological phenomenon during the pandemic.

Detection of Infectious Disease Outbreaks in Search Engine Time Series Using Non-Specific Syndromic Surveillance with Effect-Size Filtering

Novel infectious disease outbreaks, including most recently that of the COVID-19 pandemic, could be detected by non-specific syndromic surveillance systems. Such systems, utilizing a variety of data sources ranging from Electronic Health Records to internet data such as aggregated search engine queries, create alerts when unusually high rates of symptom reports occur. This is especially important for the detection of novel diseases, where their manifested symptoms are unknown. Here we improve upon a set of previously-proposed non-specific syndromic surveillance methods by taking into account both how unusual a preponderance of symptoms is and their effect size. We demonstrate that our method is as accurate as previously-proposed methods for low dimensional data and show its effectiveness for high-dimensional aggregated data by applying it to aggregated time-series health-related search engine queries. We find that in 2019 the method would have raised alerts related to several disease outbreaks earlier than health authorities did. During the COVID-19 pandemic the system identified the beginning of pandemic waves quickly, through combinations of symptoms which varied from wave to wave. Thus, the proposed method could be used as a practical tool for decision makers to detect new disease outbreaks using time series derived from search engine data even in the absence of specific information on the diseases of interest and their symptoms. © 2022 ACM.

Public awareness for "classic" childhood diseases and inflammatory syndromes in children during the COVID-19 pandemic.

OBJECTIVES: The objective was to analyze in silico public search interest during the COVID-19 pandemic for some classic infectious childhood diseases, e.g., measles, mumps, chickenpox, scarlet fever, and inflammatory diseases like Kawasaki disease and the pediatric inflammatory multisystem syndrome (PIMS). STUDY DESIGN: In this study, a comparison of five childhood diseases in public search trends with the pediatric inflammatory multisystem syndrome was performed. METHODS: Google Trends data for the period of five years for six childhood diseases were used. We used topics coverings all languages worldwide and all connected search queries. RESULTS: Public search interest decreased during the COVID-19 pandemic for some classic infectious childhood diseases. Search interest for the pediatric inflammatory multisystem syndrome, despite strong indication of a connection with COVID-19, remained relatively low compared to Kawasaki disease. PRACTICE IMPLICATIONS: Better understanding of Google Trends can map public awareness of childhood diseases in terms of time course and search intensity. CONCLUSIONS: Public interest during the pandemic was generated for diseases with suspected connection to COVID-19, presumably due to media triggers.

Influence of the COVID-19 pandemic measures on incidence and representation of other infectious diseases in Germany: a lesson to be learnt.

Aim: The COVID-19 pandemic resulted in a wide range of serious health, social and economic consequences. To counteract the pandemic, various measures and restrictions such as lockdowns, closures, social distancing, hygiene, and protective measures such as wearing face masks have been enforced. Apart from the COVID-19 pandemic, these measures also had effects on other transmittable diseases. This study therefore determined the impact on case numbers and interest for other infectious diseases as well. Subject and methods: Anonymized data on reported case numbers from the German Robert Koch Institute and data from Google Trends about the search interest have been used in this study to track courses of infectious diseases before and during the coronavirus pandemic in Germany. Results: The results of this analysis clearly demonstrated that the case numbers of influenza, whooping cough, measles, mumps, scarlet fever and chicken pox decreased in the pandemic years, most probably due to anti-pandemic measures in Germany. Additionally, the Google Trends analysis demonstrated public awareness, documented by a corresponding search interest, for the new topic COVID-19 and for other infectious diseases. Conclusion: Online available data provided valuable sources for research purposes in infodemiology or infoveillance.

Uncertainty, Search Engine Data, and Stock Market Returns During a Pandemic.

In recent years, a series of uncertain events, including the spread of COVID-19, has affected the Chinese stock market. When people face uncertainty, they often turn to internet search engines to obtain more information to support their investment decisions. This paper uses the uncertainty index, investor sentiment reflected by search engine data, and Chinese stock return data during the pandemic to examine the relationships among the three. Using daily data from March 2, 2020, to March 2, 2021, our empirical findings reveal that stock returns during a pandemic lead to an increase in investor retrieval of search engine data and that uncertainty affects stock returns during a pandemic. However, the reverse is not true. Therefore, in the face of an uncertainty such as market volatility caused by the spread of the pandemic, the active release of favorable information by regulators can help guide investor sentiment, prevent sharp stock market volatility, and improve the effectiveness of policy governance.

Representation of long COVID syndrome in the awareness of the population is revealed by Google Trends analysis.

In some COVID-19 patients, symptoms persist for several weeks and sometimes, after the acute disease phase, these patients develop new symptoms, which then represents a transition into the so-called long COVID. The exact demarcation of the terms and generally applicable definitions are still discussed, but the phenomenon is most commonly referred to as long COVID. In this study, Google Trends data have been used to track levels of public awareness for long COVID and some important symptoms during the course of the COVID-19 pandemic. The results of this analysis clearly demonstrate the public interest in the new topic of long COVID, as documented by a corresponding search volume. This is related to the disease COVID-19, which is being spread by the corona pandemic. Relevant symptoms for COVID-19 or long COVID, for example ageusia and anosmia, only started to receive more public attention during the pandemic. Therefore, Google Trends is a useful tool to demonstrate the population's awareness of certain infodemiological topics like long COVID.

The rise of infodemiology and infoveillance during COVID-19 crisis.

We noticed an increase in the relative number of published papers on topics such as infoveillance, infodemiology and Google Trends. Collected PubMed data are from the period of January 2020 to March 2021 and were searched with the use of five keywords: infoveillance, infodemiology, Google Trends, diabetes and in silico. We compared an increase in the number of papers from PubMed with search interest expressed in Google Trends. Collected Google Trends data is from the same period, covering fifteen months starting January 2020 and were searched with the use of three search topics: coronavirus, lockdown and social distancing. The geographic setting for search engine users was worldwide. We propose a hypothesis that after increased interest in searches during the pandemic's initial months came an increased number of published papers on topics such as infoveillance, infodemiology and Google Trends.