Providing early indication of regional anomalies in COVID-19 case counts in England using search engine queries.

Prior work has shown the utility of using Internet searches to track the incidence of different respiratory illnesses. Similarly, people who suffer from COVID-19 may query for their symptoms prior to accessing the medical system (or in lieu of it). To assist in the UK government's response to the COVID-19 pandemic we analyzed searches for relevant symptoms on the Bing web search engine from users in England to identify areas of the country where unexpected rises in relevant symptom searches occurred. These were reported weekly to the UK Health Security Agency to assist in their monitoring of the pandemic. Our analysis shows that searches for "fever" and "cough" were the most correlated with future case counts during the initial stages of the pandemic, with searches preceding case counts by up to 21 days. Unexpected rises in search patterns were predictive of anomalous rises in future case counts within a week, reaching an Area Under Curve of 0.82 during the initial phase of the pandemic, and later reducing due to changes in symptom presentation. Thus, analysis of regional searches for symptoms can provide an early indicator (of more than one week) of increases in COVID-19 case counts.

Associations between COVID-19 mobility restrictions and economic, mental health, and suicide-related concerns in the US using cellular phone GPS and Google search volume data.

During the COVID-19 pandemic, US populations have experienced elevated rates of financial and psychological distress that could lead to increases in suicide rates. Rapid ongoing mental health monitoring is critical for early intervention, especially in regions most affected by the pandemic, yet traditional surveillance data are available only after long lags. Novel information on real-time population isolation and concerns stemming from the pandemic's social and economic impacts, via cellular mobility tracking and online search data, are potentially important interim surveillance resources. Using these measures, we employed transfer function model time-series analyses to estimate associations between daily mobility indicators (proportion of cellular devices completely at home and time spent at home) and Google Health Trends search volumes for terms pertaining to economic stress, mental health, and suicide during 2020 and 2021 both nationally and in New York City. During the first pandemic wave in early-spring 2020, over 50% of devices remained completely at home and searches for economic stressors exceeded 60,000 per 10 million. We found large concurrent associations across analyses between declining mobility and increasing searches for economic stressor terms (national proportion of devices at home: cross-correlation coefficient (CC) = 0.6 (p-value <0.001)). Nationally, we also found strong associations between declining mobility and increasing mental health and suicide-related searches (time at home: mood/anxiety CC = 0.53 (<0.001), social stressor CC = 0.51 (<0.001), suicide seeking CC = 0.37 (0.006)). Our findings suggest that pandemic-related isolation coincided with acute economic distress and may be a risk factor for poor mental health and suicidal behavior. These emergent relationships warrant ongoing attention and causal assessment given the potential for long-term psychological impact and suicide death. As US populations continue to face stress, Google search data can be used to identify possible warning signs from real-time changes in distributions of population thought patterns.

Measuring Public Concern About COVID-19 in Japanese Internet Users Through Search Queries: Infodemiological Study.

BACKGROUND: COVID-19 has disrupted lives and livelihoods and caused widespread panic worldwide. Emerging reports suggest that people living in rural areas in some countries are more susceptible to COVID-19. However, there is a lack of quantitative evidence that can shed light on whether residents of rural areas are more concerned about COVID-19 than residents of urban areas. OBJECTIVE: This infodemiology study investigated attitudes toward COVID-19 in different Japanese prefectures by aggregating and analyzing Yahoo! JAPAN search queries. METHODS: We measured COVID-19 concerns in each Japanese prefecture by aggregating search counts of COVID-19-related queries of Yahoo! JAPAN users and data related to COVID-19 cases. We then defined two indices-the localized concern index (LCI) and localized concern index by patient percentage (LCIPP)-to quantitatively represent the degree of concern. To investigate the impact of emergency declarations on people's concerns, we divided our study period into three phases according to the timing of the state of emergency in Japan: before, during, and after. In addition, we evaluated the relationship between the LCI and LCIPP in different prefectures by correlating them with prefecture-level indicators of urbanization. RESULTS: Our results demonstrated that the concerns about COVID-19 in the prefectures changed in accordance with the declaration of the state of emergency. The correlation analyses also indicated that the differentiated types of public concern measured by the LCI and LCIPP reflect the prefectures' level of urbanization to a certain extent (ie, the LCI appears to be more suitable for quantifying COVID-19 concern in urban areas, while the LCIPP seems to be more appropriate for rural areas). CONCLUSIONS: We quantitatively defined Japanese Yahoo users' concerns about COVID-19 by using the search counts of COVID-19-related search queries. Our results also showed that the LCI and LCIPP have external validity.

Decreased Global Interest in Oral Cancer During the COVID-19 Pandemic.

OBJECTIVE: Oral cancer is one of the most common malignancies in developing countries, but studies using global data are scarce. The aim of this study is to analyze the search interests for oral cancer using mouth cancer, tongue cancer, gum cancer, and lip cancer as common keywords. METHODS: Internet searches relating to oral cancer from 2010 to 2020, from 250 countries and dependent areas, were retrieved from Google Trends. Color densities in a heat map were used to show geographic differences. Kruskal-Wallis test with post hoc Dunn's analysis was used to perform yearly comparisons of searches for mouth cancer, tongue cancer, gum cancer, and lip cancer. Search results within 2020 were also compared to determine differences. Forecasting searches from 2021 to 2022 were done by fitting time series models. RESULTS: From 29 of 250 (11.6%) countries, the highest search values were observed for mouth cancer in Sri Lanka, Qatar, Bangladesh, Finland, Netherlands, Spain, and France. Compared to 2020, greater searches were seen in 2018 (Mdn = 91%, P = 0.023) and 2019 (Mdn = 94%, P = 0.012) for mouth cancer, and 2019 (Mdn = 17%, P = 0.035) for lip cancer. The relative search volumes for gum cancer and lip cancer were substantially lower than mouth cancer during the COVID-19 pandemic. CONCLUSION: Higher-income countries tend to be more interested in seeking information about oral cancer. Noteworthy decline in the interest in seeking information online for oral cancer may have crucial implications during the COVID-19 pandemic. Google Trends offer an invaluable and inexpensive means for oral cancer surveillance and health-seeking behavior. 
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The relationship between Google search interest for pulmonary symptoms and COVID-19 cases using dynamic conditional correlation analysis.

This study aims to evaluate the monitoring and predictive value of web-based symptoms (fever, cough, dyspnea) searches for COVID-19 spread. Daily search interests from Turkey, Italy, Spain, France, and the United Kingdom were obtained from Google Trends (GT) between January 1, 2020, and August 31, 2020. In addition to conventional correlational models, we studied the time-varying correlation between GT search and new case reports; we used dynamic conditional correlation (DCC) and sliding windows correlation models. We found time-varying correlations between pulmonary symptoms on GT and new cases to be significant. The DCC model proved more powerful than the sliding windows correlation model. This model also provided better at time-varying correlations (r ≥ 0.90) during the first wave of the pandemic. We used a root means square error (RMSE) approach to attain symptom-specific shift days and showed that pulmonary symptom searches on GT should be shifted separately. Web-based search interest for pulmonary symptoms of COVID-19 is a reliable predictor of later reported cases for the first wave of the COVID-19 pandemic. Illness-specific symptom search interest on GT can be used to alert the healthcare system to prepare and allocate resources needed ahead of time.

Public Interest in Immunity and the Justification for Intervention in the Early Stages of the COVID-19 Pandemic: Analysis of Google Trends Data.

BACKGROUND: The use of social big data is an important emerging concern in public health. Internet search volumes are useful data that can sensitively detect trends of the public's attention during a pandemic outbreak situation. OBJECTIVE: Our study aimed to analyze the public's interest in COVID-19 proliferation, identify the correlation between the proliferation of COVID-19 and interest in immunity and products that have been reported to confer an enhancement of immunity, and suggest measures for interventions that should be implemented from a health and medical point of view. METHODS: To assess the level of public interest in infectious diseases during the initial days of the COVID-19 outbreak, we extracted Google search data from January 20, 2020, onward and compared them to data from March 15, 2020, which was approximately 2 months after the COVID-19 outbreak began. In order to determine whether the public became interested in the immune system, we selected coronavirus, immune, and vitamin as our final search terms. RESULTS: The increase in the cumulative number of confirmed COVID-19 cases that occurred after January 20, 2020, had a strong positive correlation with the search volumes for the terms coronavirus (R=0.786; P<.001), immune (R=0.745; P<.001), and vitamin (R=0.778; P<.001), and the correlations between variables were all mutually statistically significant. Moreover, these correlations were confirmed on a country basis when we restricted our analyses to the United States, the United Kingdom, Italy, and Korea. Our findings revealed that increases in search volumes for the terms coronavirus and immune preceded the actual occurrences of confirmed cases. CONCLUSIONS: Our study shows that during the initial phase of the COVID-19 crisis, the public's desire and actions of strengthening their own immune systems were enhanced. Further, in the early stage of a pandemic, social media platforms have a high potential for informing the public about potentially helpful measures to prevent the spread of an infectious disease and provide relevant information about immunity, thereby increasing the public's knowledge.

Google Trends for Pain Search Terms in the World's Most Populated Regions Before and After the First Recorded COVID-19 Case: Infodemiological Study.

BACKGROUND: Web-based analysis of search queries has become a very useful method in various academic fields for understanding timely and regional differences in the public interest in certain terms and concepts. Particularly in health and medical research, Google Trends has been increasingly used over the last decade. OBJECTIVE: This study aimed to assess the search activity of pain-related parameters on Google Trends from among the most populated regions worldwide over a 3-year period from before the report of the first confirmed COVID-19 cases in these regions (January 2018) until December 2020. METHODS: Search terms from the following regions were used for the analysis: India, China, Europe, the United States, Brazil, Pakistan, and Indonesia. In total, 24 expressions of pain location were assessed. Search terms were extracted using the local language of the respective country. Python scripts were used for data mining. All statistical calculations were performed through exploratory data analysis and nonparametric Mann-Whitney U tests. RESULTS: Although the overall search activity for pain-related terms increased, apart from pain entities such as headache, chest pain, and sore throat, we observed discordant search activity. Among the most populous regions, pain-related search parameters for shoulder, abdominal, and chest pain, headache, and toothache differed significantly before and after the first officially confirmed COVID-19 cases (for all, P<.001). In addition, we observed a heterogenous, marked increase or reduction in pain-related search parameters among the most populated regions. CONCLUSIONS: As internet searches are a surrogate for public interest, we assume that our data are indicative of an increased incidence of pain after the onset of the COVID-19 pandemic. However, as these increased incidences vary across geographical and anatomical locations, our findings could potentially facilitate the development of specific strategies to support the most affected groups.

Association of Search Query Interest in Gastrointestinal Symptoms With COVID-19 Diagnosis in the United States: Infodemiology Study.

BACKGROUND: Coronavirus disease (COVID-19) is a novel viral illness that has rapidly spread worldwide. While the disease primarily presents as a respiratory illness, gastrointestinal symptoms such as diarrhea have been reported in up to one-third of confirmed cases, and patients may have mild symptoms that do not prompt them to seek medical attention. Internet-based infodemiology offers an approach to studying symptoms at a population level, even in individuals who do not seek medical care. OBJECTIVE: This study aimed to determine if a correlation exists between internet searches for gastrointestinal symptoms and the confirmed case count of COVID-19 in the United States. METHODS: The search terms chosen for analysis in this study included common gastrointestinal symptoms such as diarrhea, nausea, vomiting, and abdominal pain. Furthermore, the search terms fever and cough were used as positive controls, and constipation was used as a negative control. Daily query shares for the selected symptoms were obtained from Google Trends between October 1, 2019 and June 15, 2020 for all US states. These shares were divided into two time periods: pre-COVID-19 (prior to March 1) and post-COVID-19 (March 1-June 15). Confirmed COVID-19 case numbers were obtained from the Johns Hopkins University Center for Systems Science and Engineering data repository. Moving averages of the daily query shares (normalized to baseline pre-COVID-19) were then analyzed against the confirmed disease case count and daily new cases to establish a temporal relationship. RESULTS: The relative search query shares of many symptoms, including nausea, vomiting, abdominal pain, and constipation, remained near or below baseline throughout the time period studied; however, there were notable increases in searches for the positive control symptoms of fever and cough as well as for diarrhea. These increases in daily search queries for fever, cough, and diarrhea preceded the rapid rise in number of cases by approximately 10 to 14 days. The search volumes for these terms began declining after mid-March despite the continued rises in cumulative cases and daily new case counts. CONCLUSIONS: Google searches for symptoms may precede the actual rises in cases and hospitalizations during pandemics. During the current COVID-19 pandemic, this study demonstrates that internet search queries for fever, cough, and diarrhea increased prior to the increased confirmed case count by available testing during the early weeks of the pandemic in the United States. While the search volumes eventually decreased significantly as the number of cases continued to rise, internet query search data may still be a useful tool at a population level to identify areas of active disease transmission at the cusp of new outbreaks.

Clinical Predictors of SARS-CoV-2 Testing Pressure on Clinical Laboratories: A Multinational Study Analyzing Google Trends and Over 100 Million Diagnostic Tests.

OBJECTIVE: Evidence has shown that Google searches for clinical symptom keywords correlates with the number of new weekly patients with COVID-19. This multinational study assessed whether demand for SARS-CoV-2 tests could also be predicted by Google searches for key COVID-19 symptoms. METHODS: The weekly number of SARS-CoV-2 tests performed in Italy and the United States was retrieved from official sources. A concomitant electronic search was performed in Google Trends, using terms for key COVID-19 symptoms. RESULTS: The model that provided the highest coefficient of determination for the United States (R2 = 82.8%) included a combination of searching for cough (with a time lag of 2 weeks), fever (with a time lag of 2 weeks), and headache (with a time lag of 3 weeks; the time lag refers to the amount of time between when a search was conducted and when a test was administered). In Italy, headache provided the model with the highest adjusted R2 (86.8%), with time lags of both 1 and 2 weeks. CONCLUSION: Weekly monitoring of Google Trends scores for nonspecific COVID-19 symptoms is a reliable approach for anticipating SARS-CoV-2 testing demands ~2 weeks in the future.

A predictive internet-based model for COVID-19 hospitalization census.

The COVID-19 pandemic has strained hospital resources and necessitated the need for predictive models to forecast patient care demands in order to allow for adequate staffing and resource allocation. Recently, other studies have looked at associations between Google Trends data and the number of COVID-19 cases. Expanding on this approach, we propose a vector error correction model (VECM) for the number of COVID-19 patients in a healthcare system (Census) that incorporates Google search term activity and healthcare chatbot scores. The VECM provided a good fit to Census and very good forecasting performance as assessed by hypothesis tests and mean absolute percentage prediction error. Although our study and model have limitations, we have conducted a broad and insightful search for candidate Internet variables and employed rigorous statistical methods. We have demonstrated the VECM can potentially be a valuable component to a COVID-19 surveillance program in a healthcare system.

Google search volume trends for cancer screening terms during the COVID-19 pandemic.

The COVID-19 pandemic has led to delays in cancer diagnosis, in part due to postponement of cancer screening. We used Google Trends data to assess public attention to cancer screening during the first peak of the COVID-19 pandemic. Search volume for terms related to established cancer screening tests ("colonoscopy," "mammogram," "lung cancer screening," and "pap smear") showed a marked decrease of up to 76% compared to the pre-pandemic period, a significantly greater drop than for search volume for terms denoting common chronic diseases. Maintaining awareness of cancer screening during future public health crises may decrease delays in cancer diagnosis.

Retrospective analysis of the accuracy of predicting the alert level of COVID-19 in 202 countries using Google Trends and machine learning.

BACKGROUND: Internet search engine data, such as Google Trends, was shown to be correlated with the incidence of COVID-19, but only in several countries. We aim to develop a model from a small number of countries to predict the epidemic alert level in all the countries worldwide. METHODS: The "interest over time" and "interest by region" Google Trends data of Coronavirus, pneumonia, and six COVID symptom-related terms were searched. The daily incidence of COVID-19 from 10 January to 23 April 2020 of 202 countries was retrieved from the World Health Organization. Three alert levels were defined. Ten weeks' data from 20 countries were used for training with machine learning algorithms. The features were selected according to the correlation and importance. The model was then tested on 2830 samples of 202 countries. RESULTS: Our model performed well in 154 (76.2%) countries, of which each had no more than four misclassified samples. In these 154 countries, the accuracy was 0.8133, and the kappa coefficient was 0.6828. While in all 202 countries, the accuracy was 0.7527, and the kappa coefficient was 0.5841. The proposed algorithm based on Random Forest Classification and nine features performed better compared to other machine learning methods and the models with different numbers of features. CONCLUSIONS: Our result suggested that the model developed from 20 countries with Google Trends data and Random Forest Classification can be applied to predict the epidemic alert levels of most countries worldwide.

Using Baidu search values to monitor and predict the confirmed cases of COVID-19 in China: - evidence from Baidu index.

BACKGROUND: New coronavirus disease 2019 (COVID-19) has posed a severe threat to human life and caused a global pandemic. The current research aimed to explore whether the search-engine query patterns could serve as a potential tool for monitoring the outbreak of COVID-19. METHODS: We collected the number of COVID-19 confirmed cases between January 11, 2020, and April 22, 2020, from the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU). The search index values of the most common symptoms of COVID-19 (e.g., fever, cough, fatigue) were retrieved from the Baidu Index. Spearman's correlation analysis was used to analyze the association between the Baidu index values for each COVID-19-related symptom and the number of confirmed cases. Regional distributions among 34 provinces/ regions in China were also analyzed. RESULTS: Daily growth of confirmed cases and Baidu index values for each COVID-19-related symptom presented robust positive correlations during the outbreak (fever: rs=0.705, p=9.623× 10- 6; cough: rs=0.592, p=4.485× 10- 4; fatigue: rs=0.629, p=1.494× 10- 4; sputum production: rs=0.648, p=8.206× 10- 5; shortness of breath: rs=0.656, p=6.182× 10-5). The average search-to-confirmed interval (STCI) was 19.8 days in China. The daily Baidu Index value's optimal time lags were the 4 days for cough, 2 days for fatigue, 3 days for sputum production, 1 day for shortness of breath, and 0 days for fever. CONCLUSION: The searches of COVID-19-related symptoms on the Baidu search engine were significantly correlated to the number of confirmed cases. Since the Baidu search engine could reflect the public's attention to the pandemic and the regional epidemics of viruses, relevant departments need to pay more attention to areas with high searches of COVID-19-related symptoms and take precautionary measures to prevent these potentially infected persons from further spreading.

COVID-19-Related Internet Search Patterns Among People in the United States: Exploratory Analysis.

BACKGROUND: The internet is a well-known source of information that patients use to better inform their opinions and to guide their conversations with physicians during clinic visits. The novelty of the recent COVID-19 outbreak has led patients to turn more frequently to the internet to gather more information and to alleviate their concerns about the virus. OBJECTIVE: The aims of the study were to (1) determine the most commonly searched phrases related to COVID-19 in the United States and (2) identify the sources of information for these web searches. METHODS: Search terms related to COVID-19 were entered into Google. Questions and websites from Google web search were extracted to a database using customized software. Each question was categorized into one of 6 topics: clinical signs and symptoms, treatment, transmission, cleaning methods, activity modification, and policy. Additionally, the websites were categorized according to source: World Health Organization (WHO), Centers for Disease Control and Prevention (CDC), non-CDC government, academic, news, and other media. RESULTS: In total, 200 questions and websites were extracted. The most common question topic was transmission (n=63, 31.5%), followed by clinical signs and symptoms (n=54, 27.0%) and activity modification (n=31, 15.5%). Notably, the clinical signs and symptoms category captured questions about myths associated with the disease, such as whether consuming alcohol stops the coronavirus. The most common websites provided were maintained by the CDC, the WHO, and academic medical organizations. Collectively, these three sources accounted for 84.0% (n=168) of the websites in our sample. CONCLUSIONS: In the United States, the most commonly searched topics related to COVID-19 were transmission, clinical signs and symptoms, and activity modification. Reassuringly, a sizable majority of internet sources provided were from major health organizations or from academic medical institutions.

Dental Challenges and the Needs of the Population during the Covid-19 Pandemic Period. Real-Time Surveillance Using Google Trends.

BACKGROUND: The outbreak of the COVID-19 pandemic may lead to changes in the dental needs of the population and new challenges concerning oral health care. METHODS: The Google Trends tool was used to collect data on the Internet search interest. The investigated material was collected from 1 January 2020 to 23 August 2020. Search terms "toothache", "dentist" and "stay at home" were retrieved for the whole world as well as for the US, the UK, Poland, Italy and Sweden. RESULTS: During the lockdown, correlation analysis indicates the lowest public interest in the word "dentist" one week preceding the peak for "toothache", followed by an increase in the word search for "dentist". On 12 April, worldwide, the maximum of Google Trends Relative Search Volume (RSV) for "toothache" was observed. CONCLUSION: Decrease in "dentist" queries during lockdown followed by an increase in "toothache" search predicts greater dental needs in the post-pandemic period. The surveillance shows significant changes in queries for dental-related terms during the course of the COVID-19 pandemic. In order to prepare for future pandemic outbreaks teledentistry programs should be taken into consideration.

[Sex Work during the COVID-19 Pandemic Period in the Ibero-American Context: analysis of advertisements on websites]. / Trabalho sexual em período de pandemia por COVID-19 no contexto íbero-americano: análise de anúncios em websites.

Sex workers become increasingly economically vulnerable due to the restrictive measures implemented to combat the coronavirus pandemic. In this respect, the scope of this study is to analyze the content of prostitution websites and advertisements regarding measures related to the COVID-19 pandemic. It involved a description of the visits and analysis of content of communications on websites that advertise commercial sex transactions. The percentage change in the number of visits for three periods from 02/2019 to 04/2020 was calculated. Subsequently, ads with the terms "corona," "pandemic" and "quarantine" on websites that offer search engines were extracted. The Bardin method was then used for content analysis. There was an increase in the number of visits to prostitution websites between 2019 and 2020, followed by a decrease with the advent of the coronavirus pandemic crisis. With regard to the protection measures during the pandemic, health recommendations and the incentive to engage in virtual sex are highlighted. Of the 1,991,014 advertisements, 0.51% mention the COVID-19 crisis regarding noncompliance with social distancing, protection measures and the offer of online sex.

Trabalhadores do sexo tornam-se cada vez mais vulneráveis economicamente como resultado das medidas restritivas implementadas para responder à pandemia de coronavírus. Nesse sentido, o objetivo deste estudo é analisar o conteúdo dos websites e anúncios de prostituição sobre medidas relacionadas à pandemia por COVID-19. Trata-se de descrição do fluxo de visitas e análise de conteúdo das comunicações em websites que anunciam transações de sexo comercial. Realizou-se cálculo de variação percentual do número de visitas para três períodos compreendidos entre 02/2019 a 04/2020. Posteriormente, extraíram-se anúncios com os termos "corona", "pandemia" e "quarentena" em websites que oferecem mecanismo de busca. Para análise de conteúdo, utilizou-se o método de Bardin. Houve aumento no número de acessos nos websites de prostituição entre o ano de 2019 e 2020, seguido de queda com a advento da crise pandêmica por coronavírus. Dentre as medidas de proteção durante a pandemia, destacam-se as recomendações de saúde e o incentivo ao sexo virtual. Dentre 1.991.014 anúncios, 0,51% mencionam a crise por COVID-19 quanto ao descumprimento do distanciamento social, medidas de proteção e oferta de sexo on-line.

Database Resources of the National Genomics Data Center, China National Center for Bioinformation in 2021.

The National Genomics Data Center (NGDC), part of the China National Center for Bioinformation (CNCB), provides a suite of database resources to support worldwide research activities in both academia and industry. With the explosive growth of multi-omics data, CNCB-NGDC is continually expanding, updating and enriching its core database resources through big data deposition, integration and translation. In the past year, considerable efforts have been devoted to 2019nCoVR, a newly established resource providing a global landscape of SARS-CoV-2 genomic sequences, variants, and haplotypes, as well as Aging Atlas, BrainBase, GTDB (Glycosyltransferases Database), LncExpDB, and TransCirc (Translation potential for circular RNAs). Meanwhile, a series of resources have been updated and improved, including BioProject, BioSample, GWH (Genome Warehouse), GVM (Genome Variation Map), GEN (Gene Expression Nebulas) as well as several biodiversity and plant resources. Particularly, BIG Search, a scalable, one-stop, cross-database search engine, has been significantly updated by providing easy access to a large number of internal and external biological resources from CNCB-NGDC, our partners, EBI and NCBI. All of these resources along with their services are publicly accessible at https://bigd.big.ac.cn.