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.

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.

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.

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Google Trends data underline the importance and duration of the COVID-19 effects.

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PubMed data reveal an increase in the number of papers in infoveillance, infodemiology and Google Trends

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Current restrictions such as lockdown and social distancing are reflected in the number of internet searches

Searching Choices: Quantifying Decision-Making Processes Using Search Engine Data

When making a decision, humans consider two types of information: information they have acquired through their prior experience of the world, and further information they gather to support the decision in question. Here, we present evidence that data from search engines such as Google can help us model both sources of information. We show that statistics from search engines on the frequency of content on the Internet can help us estimate the statistical structure of prior experience; and, specifically, we outline how such statistics can inform psychological theories concerning the valuation of human lives, or choices involving delayed outcomes. Turning to information gathering, we show that search query data might help measure human information gathering, and it may predict subsequent decisions. Such data enable us to compare information gathered across nations, where analyses suggest, for example, a greater focus on the future in countries with a higher per capita GDP. We conclude that search engine data constitute a valuable new resource for cognitive scientists, offering a fascinating new tool for understanding the human decision-making process.