Are online searches for the novel coronavirus (COVID-19) related to media or epidemiology? A cross-sectional study (original) (raw)
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JMIR Public Health and Surveillance, 2020
Background The coronavirus disease (COVID-19) is the latest pandemic of the digital age. With the internet harvesting large amounts of data from the general population in real time, public databases such as Google Trends (GT) and the Baidu Index (BI) can be an expedient tool to assist public health efforts. Objective The aim of this study is to apply digital epidemiology to the current COVID-19 pandemic to determine the utility of providing adjunctive epidemiologic information on outbreaks of this disease and evaluate this methodology in the case of future pandemics. Methods An epidemiologic time series analysis of online search trends relating to the COVID-19 pandemic was performed from January 9, 2020, to April 6, 2020. BI was used to obtain online search data for China, while GT was used for worldwide data, the countries of Italy and Spain, and the US states of New York and Washington. These data were compared to real-world confirmed cases and deaths of COVID-19. Chronologic patt...
The Evolution of the COVID-19 Pandemic Through the Lens of Google Searches
Real-time data is essential for policymakers to adapt to a rapidly evolving situation like the COVID-19 pandemic. Relying on Google search interest data across 207 countries and territories, we demonstrate the capacity of publicly-available, real-time data to anticipate COVID-19 cases; evaluate the economic, mental health, and social impacts of containment policies; and identify demand for (mis)information about COVID-19 vaccines. We show that: (1) search interest in COVID-specific symptoms can anticipate rising COVID-19 cases across both high- and low-income settings; (2) countries with more restrictive containment policies experienced larger socio-economic externalities; in addition, lower-income countries experienced less searches for unemployment, but more pronounced mental health externalities; and (3) high vaccination rates are associated with strong demand for information about vaccine appointments and side effects; in some settings, high interest in misinformation search ter...
COVID-19 Symptom-Related Google Searches and Local COVID-19 Incidence in Spain: Correlational Study
Journal of Medical Internet Research
Background COVID-19 is one of the biggest pandemics in human history, along with other disease pandemics, such as the H1N1 influenza A, bubonic plague, and smallpox pandemics. This study is a small contribution that tries to find contrasted formulas to alleviate global suffering and guarantee a more manageable future. Objective In this study, a statistical approach was proposed to study the correlation between the incidence of COVID-19 in Spain and search data provided by Google Trends. Methods We assessed the linear correlation between Google Trends search data and the data provided by the National Center of Epidemiology in Spain—which is dependent on the Instituto de Salud Carlos III—regarding the number of COVID-19 cases reported with a certain time lag. These data enabled the identification of anticipatory patterns. Results In response to the ongoing outbreak, our results demonstrate that by using our correlation test, the evolution of the COVID-19 pandemic can be predicted in S...
Correlations between Web Searches and COVID-19 Epidemiological Indicators in Brazil
Brazilian Archives of Biology and Technology
COVID-19 rapidly spread across the world in an unprecedented outbreak with a massive number of infected and fatalities. The pandemic was heavily discussed and searched on the internet, which generated big amounts of data related to it. This led to the possibility of attempting to forecast coronavirus indicators using the internet data. For this study, Google Trends statistics for 124 selected search terms related to pandemic were used in an attempt to find which keywords had the best Spearman correlations with a lag, as well as a forecasting model. It was found that keywords related to coronavirus testing among some others, such as "I have contracted covid", had high correlations (≥0.7) with few weeks of lag (≤4 weeks). Besides that, the ARIMAX model using those keywords had promising results in predicting the increase or decrease of epidemiological indicators, although it was not able to predict their exact values. Thus, we found that Google Trends data may be useful for predicting outbreaks of coronavirus a few weeks before they happen, and may be used as an auxiliary tool in monitoring and forecasting the disease in Brazil.
Online behavioural patterns for Coronavirus disease 2019 (COVID-19) in the United Kingdom
Epidemiology and Infection, 2020
The current coronavirus (COVID-19) pandemic offers a unique opportunity to conduct an infodemiological study examining patterns in online searching activity about a specific disease and how this relates to news media within a specific country. Google Trends quantifies volumes of online activity. The relative search volume was obtained for ‘Coronavirus’, ‘handwashing’, ‘face mask’ and symptom related keywords, for the United Kingdom, from the date of the first confirmed case until numbers peaked in April. The relationship between online search traffic and confirmed case numbers was examined. Search volumes varied over time; peaks appear related to events in the progression of the epidemic which were reported in the media. Search activity on ‘Coronavirus’ correlated well against confirmed case number as did ‘face mask’ and symptom-related keywords. User-generated online data sources such as Google Trends may aid disease surveillance, being more responsive to changes in disease occurre...
Exploratory analysis of internet search trends during the COVID-19 outbreak
ACIMED, 2020
Coronavirus disease 2019 has put the world in a health emergency. Searching for information on the Internet largely reflects people's interest in this pandemic. Objective: Conduct an exploratory analysis of Internet search trends during the 2019 coronavirus disease outbreak. Methods: Google Trends was used to provide data on the relative volume of Google searches for terms related to 2019 coronavirus disease. The evaluation period was from January 01 to May 17, 2020. Results : The search term used to know this pandemic was “coronavirus”, the most searched symptom was “fever”, followed by “sore throat” and “cough”, in addition, the interest of users to know the transmission routes of the acute respiratory syndrome coronavirus 2. As for preventive measures, the most searched term was “stay home”, followed by “facial masks”, “social distancing” and “washing hands”. Conclusions: The results confirmed interest in COVID-19 via Internet. Using information from people's Internet se...
Online Search Behavior Related to COVID-19 Vaccines: Infodemiology Study
JMIR Infodemiology, 2021
Background Vaccination against COVID-19 is an important public health strategy to address the ongoing pandemic. Examination of online search behavior related to COVID-19 vaccines can provide insights into the public's awareness, concerns, and interest regarding COVID-19 vaccination. Objective The aim of this study is to describe online search behavior related to COVID-19 vaccines during the start of public vaccination efforts in the United States. Methods We examined Google Trends data from January 1, 2021, through March 16, 2021, to determine the relative search volume for vaccine-related searches on the internet. We also examined search query log data for COVID-19 vaccine-related searches and identified 5 categories of searches: (1) general or other information, (2) vaccine availability, (3) vaccine manufacturer, (4) vaccine side-effects and safety, and (5) vaccine myths and conspiracy beliefs. In this paper, we report on the proportion and trends for these different categorie...
Exploring the use of web searches for risk communication during COVID-19 in Germany
Scientific Reports, 2021
Risk communication during pandemics is an element of utmost importance. Understanding the level of public attention—a prerequisite for effective communication—implicates expensive and time-consuming surveys. We hypothesise that the relative search volume from Google Trends could be used as an indicator of public attention of a disease and its prevention measures. The search terms ‘RKI’ (Robert Koch Institute, national public health authority in Germany), ‘corona’ and ‘protective mask’ in German language were shortlisted. Cross-correlations between these terms and the reported cases from 15 February to 27 April were conducted for each German federal state. The findings were contrasted against a timeline of official communications concerning COVID-19. The highest correlations of the term ‘RKI’ with reported COVID-19 cases were found between lags of − 2 and − 12 days, meaning web searches were already performed from 2 to 12 days before case numbers increased. A similar pattern was seen...
Google search volume predicts the emergence of COVID-19 outbreaks
Acta Bio Medica : Atenei Parmensis, 2020
Background and aim: Digital epidemiology is increasingly used for supporting traditional epidemiology. This study was hence aimed to explore whether the Google search volume may have been useful to predict the trajectory of coronavirus disease 2019 (COVID-19) outbreak in Italy. Materials and Methods: We accessed Google Trends for collecting data on weekly Google searches for the keywords “tosse” (i.e., cough), “febbre” (i.e., fever) and “dispnea” (dyspnea) in Italy, between February and May 2020. The number of new weekly cases of COVID-19 in Italy was also obtained from the website of the National Institute of Health. Results: The peaks of Google searches for the three terms predicted by 3 weeks that of newly diagnosed COVID-19 cases. The peaks of weekly Google searches for “febbre” (fever), “tosse”( cough) and “dispnea” (dyspnea) were 1.7-, 2.2- and 7.7-fold higher compared to the week before the diagnosis of the first national case. No significant correlation was found between the...
2021
Background The use of the internet and web-based platforms to obtain public health information and manage health-related issues has become widespread in this digital age. The practice is so pervasive that the first reaction to obtaining health information is to “Google it.” As SARS-CoV-2 broke out in Wuhan, China, in December 2019 and quickly spread worldwide, people flocked to the internet to learn about the novel coronavirus and the disease, COVID-19. Lagging responses by governments and public health agencies to prioritize the dissemination of information about the coronavirus outbreak through the internet and the World Wide Web and to build trust gave room for others to quickly populate social media, online blogs, news outlets, and websites with misinformation and conspiracy theories about the COVID-19 pandemic, resulting in people’s deviant behaviors toward public health safety measures. Objective The goals of this study were to determine what people learned about the COVID-19 ...