Suitability of Google Trends™ for digital surveillance during ongoing COVID-19 epidemic: a case study from India (Preprint)

Mapping Intimacies ◽

10.2196/preprints.25985 ◽

2020 ◽

Author(s):

Parmeshwar Satpathy ◽

Sanjeev Kumar ◽

Pankaj Prasad

Keyword(s):

Media Coverage ◽

Time Lag ◽

National Level ◽

High Time ◽

Google Trends ◽

High Burden ◽

Social Distancing ◽

Digital Surveillance ◽

Traditional Surveillance ◽

Time Lag Correlation

BACKGROUND Digital surveillance has shown mixed results as supplement to the traditional surveillance. Google Trends™ (GT) is a digital platform explored for surveillance during pandemics of H1N1, Ebola and MERS. Similar efforts have been reported for the ongoing COVID-19 pandemic too. OBJECTIVE We used GT to correlate the information seeking on COVID-19 with number of tests and cases reported both at national and state levels. METHODS We obtained data on daily tests and cases from WHO, ECDC and covid19india.org websites. We retrieved GT data between 1st January 2020 to 31st May 2020 for India using a comprehensive search strategy in the form of relative search volume (RSV). We used time-lag correlation analysis to assess the temporal relationships between RSV and daily new COVID-19 cases and daily tests. RESULTS High time-lag correlation was observed between both the daily reported number of tests and cases and RSV for the terms “COVID 19”, “COVID”, “social distancing”, “soap” and “lockdown” at national level. Similar high time-lag correlation was observed for the terms “COVID 19”, “COVID”, “Corona”, “social distancing”, “soap”, “lockdown” in five high-burden states. Peaks in RSV both at national level and high-burden states corresponded with media coverage or government declarations on the ongoing pandemic. CONCLUSIONS The results of this study show that Google Trends™ data are highly correlated with COVID-19 tests and cases in India. This correlation may be because of search behaviour induced either by media-coverage of the pandemic or health-seeking for COVID-19 illness. We argue that GT can supplement the traditional surveillance system, more easily and at a lower cost.

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Suitability of Google Trends ™ for digital surveillance during ongoing COVID-19 epidemic: a case study from India

10.1101/2020.08.24.20176321 ◽

2020 ◽

Author(s):

Parmeshwar D Satpathy ◽

Sanjeev Kumar ◽

Pankaj Prasad

Keyword(s):

Media Coverage ◽

Time Lag ◽

National Level ◽

High Time ◽

Google Trends ◽

High Burden ◽

Social Distancing ◽

Digital Surveillance ◽

Traditional Surveillance ◽

Time Lag Correlation

Background: India went into the largest population-level lockdown on 25th March 2020 in response to the declaration of COVID-19 pandemic by World Health Organization (WHO). Digital surveillance has been shown to be useful to supplement the traditional surveillance. Google Trends ™ (GT) is one such platform reported to be useful during pandemics of H1N1, Ebola and MERS. Objective: We used GT to correlate the information seeking behaviour regarding COVID-19 of Indians with curiosity and apprehensiveness generated through media coverage as well as status of the epidemic both at national and state levels. Methods: We retrieved GT data between 1st January 2020 to 31st May 2020 for India using a comprehensive search strategy. We obtained data on daily tests and cases from WHO, ECDC and covid19india.org websites. We explored the trends of COVID-19 in the form of relative search volume (RSV) from GT platform and correlated them with media reports. We used time-lag correlation analysis to assess the temporal relationships between Google search terms and daily new COVID-19 cases and daily tests for 14 days. Results: Peaks in RSV correlated with media coverage or government declarations suggestive of curiosity and apprehensiveness both at national level and high-burden states. High time-lag correlation was observed between both the daily reported number of tests and cases and RSV for the terms ″COVID 19″, ″COVID″, ″social distancing″, ″soap″ and ″lockdown″ at national level. Similar high time-lag correlation was observed for the terms ″COVID 19″, ″COVID″, ″Corona″, ″social distancing″, ″soap″, ″lockdown″ in five high-burden states. Conclusion: This study reveals the advantages of infodemiology using GT to monitor an emerging infectious disease like COVID-19 in India. Google searches in India during the ongoing COVID-19 pandemic reflects mostly curiosity and apprehension of citizens. GT can also complement traditional surveillance in India as well as high burden states.

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Suitability of Google Trends™ for digital surveillance during ongoing COVID-19 epidemic: a case study from India

Disaster Medicine and Public Health Preparedness ◽

10.1017/dmp.2021.249 ◽

2021 ◽

pp. 1-28

Author(s):

Parmeshwar Satpathy ◽

Sanjeev Kumar ◽

Pankaj Prasad

Keyword(s):

Information Seeking ◽

Media Coverage ◽

Time Lag ◽

National Level ◽

Google Trends ◽

Health Seeking ◽

High Burden ◽

Search Volume ◽

Digital Surveillance ◽

Time Lag Correlation

Abstract Objective: Digital surveillance has shown mixed results as supplement to traditional surveillance. Google Trends™ (GT) has been used for digital surveillance of H1N1, Ebola and MERS. We used GT to correlate the information seeking on COVID-19 with number of tests and cases in India. Methods: We obtained data on daily tests and cases from WHO, ECDC and covid19india.org. We used a comprehensive search strategy to retrieve GT data on COVID-19 related information-seeking behaviour in India between 1st January and 31st May 2020 in the form of relative search volume (RSV). We used time-lag correlation analysis to assess the temporal relationships between RSV and daily new COVID-19 cases and tests. Results: GT RSV showed high time-lag correlation with both daily reported tests and cases for the terms “COVID 19”, “COVID”, “social distancing”, “soap” and “lockdown” at national level. In five high-burden states, high correlation was observed for these five terms along with “Corona”. Peaks in RSV both at national level and high-burden states corresponded with media coverage or government declarations on the ongoing pandemic. Conclusion: The correlation observed between GT data and COVID-19 tests/cases in India may be either due to media-coverage induced curiosity or health-seeking.

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Increase in public interest concerning alternative medicine during the COVID-19 pandemic in Indonesia: a Google Trends study

F1000Research ◽

10.12688/f1000research.25525.2 ◽

2021 ◽

Vol 9 ◽

pp. 1201

Author(s):

Dewi Rokhmah ◽

Khaidar Ali ◽

Serius Miliyani Dwi Putri ◽

Khoiron Khoiron

Keyword(s):

Alternative Medicine ◽

Public Interest ◽

Time Lag ◽

Google Trends ◽

Rank Test ◽

Search Term ◽

Alternative Medicines ◽

Search Volume ◽

Relative Search Volume ◽

Time Lag Correlation

Background: The COVID-19 pandemic has triggered individuals to increase their healthy behaviour in order to prevent transmission, including improving their immunity potentially through the use of alternative medicines. This study aimed to examine public interest on alternative medicine during the COVID-19 pandemic using Google Trends in Indonesia. Methods: Employing a quantitative study, the Spearman rank test was used to analyze the correlation between Google Relative Search Volume (RSV) of various search terms, within the categories of alternative medicine, herbal medicine and practical activity, with COVID-19 cases. In addition, time lag correlation was also investigated. Results: Public interest toward alternative medicine during COVID-19 pandemic in Indonesia is dramatically escalating. All search term categories (alternative medicine, medical herbal, and alternative medicine activities) were positively associated with COVID-19 cases (p<0.05). The terms ‘ginger’ (r=0.6376), ‘curcumin’ (r=0.6550) and ‘planting ginger’ (0.6713) had the strongest correlation. Furthermore, time lag correlation between COVID-19 and Google RSV was also positively significant (p<0.05). Conclusion: Public interest concerning alternative medicine related terms dramatically increased after the first COVID-19 confirmed case was reported in Indonesia. Time lag correlation showed good performance using weekly data. The Indonesian Government will play an important role to provide and monitor information related to alternative medicine in order for the population to receive the maximum benefit.

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Increase in public interest concerning alternative medicine during the COVID-19 pandemic in Indonesia: a Google Trends study

F1000Research ◽

10.12688/f1000research.25525.1 ◽

2020 ◽

Vol 9 ◽

pp. 1201

Author(s):

Dewi Rokhmah ◽

Khaidar Ali ◽

Serius Miliyani Dwi Putri ◽

Khoiron Khoiron

Keyword(s):

Alternative Medicine ◽

Public Interest ◽

Time Lag ◽

Google Trends ◽

Rank Test ◽

Search Term ◽

Alternative Medicines ◽

Search Volume ◽

Relative Search Volume ◽

Time Lag Correlation

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Potential use of personal protection online search during COVID-19 pandemic for predicting and monitoring public response

International Journal of Public Health Science (IJPHS) ◽

10.11591/ijphs.v9i4.20547 ◽

2020 ◽

Vol 9 (4) ◽

pp. 406

Author(s):

Michael Chandra ◽

Rizma Adlia Syakurah

Keyword(s):

Public Health ◽

Risk Communication ◽

Pearson Correlation ◽

Time Lag ◽

Early Warning Systems ◽

Google Trends ◽

Series Data ◽

Personal Protection ◽

Search Volume ◽

Time Lag Correlation

COVID-19 has become a global public health emergency in almost all over the world, including in Indonesia. Effective risk communication becomes an emergency response to increase awareness and determine appropriate interventions. The study aimed to assess the success of risk communication monitoring using Google Trends during the COVID-19 pandemic in Indonesia. Quantitative and qualitative research uses time-series data (31 December 2019-2 May 2020). The relative search volume (RSV) of keyword „masker‟ (mask) and „cuci tangan‟ (handwashing) from Google Trends (GT) and the number of COVID-19 daily cases were collected. Analyzed qualitatively. RSV search data and daily case comparisons were performed based on Pearson correlation analysis and time lag correlation with significance <0.05. The keyword „masker‟ has four peaks and „cuci tangan‟ has three peaks with fluctuations due to the increase in mask prices, government policies, news, and official WHO recommendations. Validation using time-lag correlation shows the significant results between RSV keywords related to personal protection and the number of COVID-19 cases. The highest correlation was achieved by the keyword „masker‟ three days before the number of COVID-19 cases. Google Trends can potentially be used as a complement and support for early warning systems in the surveillance system and improve public health responses in Indonesia.

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Analysis of public search interest towards immune system improvement during the COVID-19 pandemic using Google Trends

International Journal of Public Health Science (IJPHS) ◽

10.11591/ijphs.v9i4.20518 ◽

2020 ◽

Vol 9 (4) ◽

pp. 414

Author(s):

Linda Amelia ◽

Rizma Adlia Syakurah

Keyword(s):

Immune System ◽

Risk Communication ◽

Public Awareness ◽

Time Lag ◽

Google Trends ◽

Series Data ◽

Search Term ◽

Search Volume ◽

System Improvement ◽

Time Lag Correlation

In combating COVID-19, maintaining the immune system is important. Providing this information to the general population will increase public awareness towards improving their immune system. The use of Google Trends for exploring web behavior related to a topic or search term also considered as a tool for monitoring public awareness to help risk communication during the COVID-19 pandemic. Therefore, this study was conducted to assess the use of Google Trends to monitor public awareness to immune system improvement during the COVID-19 pandemic in Indonesia. This quantitative and qualitative research used time-series data from 31 December 2019 to 2 May 2020. The time-lag correlation analysis was performed to compare between relative search volume (RSV) of “Vitamin C”, “Vaksin” (Vaccine), “Berjemur” (Sunbathing) from Google Trends (GT), and the number of reported COVID-19 new cases. Validation using time-lag correlation shows the significant correlation between RSV keywords related to public awareness towards immune system improvement and the number of COVID-19 cases in Indonesia in 1-3 days before an increase in the number of cases occurs. Google Trends has the potential to become an early warning system and a tool for monitoring risk communication towards immune system improvement during the COVID-19 pandemic by Indonesia Government.

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Disproportionate Impact of COVID-19 Pandemic on Racial and Ethnic Minorities

The American Surgeon ◽

10.1177/0003134820973356 ◽

2020 ◽

pp. 000313482097335

Author(s):

Brad Boserup ◽

Mark McKenney ◽

Adel Elkbuli

Keyword(s):

Ethnic Groups ◽

Social Vulnerability ◽

Negative Binomial ◽

Demographic Data ◽

National Level ◽

The United States ◽

Death Rates ◽

Social Distancing ◽

The Impact ◽

Racial Ethnic

Background Health disparities are prevalent in many areas of medicine. We aimed to investigate the impact of the COVID-19 pandemic on racial/ethnic groups in the United States (US) and to assess the effects of social distancing, social vulnerability metrics, and medical disparities. Methods A cross-sectional study was conducted utilizing data from the COVID-19 Tracking Project and the Centers for Disease Control and Prevention (CDC). Demographic data were obtained from the US Census Bureau, social vulnerability data were obtained from the CDC, social distancing data were obtained from Unacast, and medical disparities data from the Center for Medicare and Medicaid Services. A comparison of proportions by Fisher’s exact test was used to evaluate differences between death rates stratified by age. Negative binomial regression analysis was used to predict COVID-19 deaths based on social distancing scores, social vulnerability metrics, and medical disparities. Results COVID-19 cumulative infection and death rates were higher among minority racial/ethnic groups than whites across many states. Older age was also associated with increased cumulative death rates across all racial/ethnic groups on a national level, and many minority racial/ethnic groups experienced significantly greater cumulative death rates than whites within age groups ≥ 35 years. All studied racial/ethnic groups experienced higher hospitalization rates than whites. Older persons (≥ 65 years) also experienced more COVID-19 deaths associated with comorbidities than younger individuals. Social distancing factors, several measures of social vulnerability, and select medical disparities were identified as being predictive of county-level COVID-19 deaths. Conclusion COVID-19 has disproportionately impacted many racial/ethnic minority communities across the country, warranting further research and intervention.

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Exploring the Initial Impact of COVID-19 Sentiment on US Stock Market Using Big Data

Sustainability ◽

10.3390/su12166648 ◽

2020 ◽

Vol 12 (16) ◽

pp. 6648

Author(s):

Hee Soo Lee

Keyword(s):

Big Data ◽

Stock Market ◽

Factor Model ◽

Time Lag ◽

Google Trends ◽

Investment Planning ◽

Excess Returns ◽

Time Series Regression ◽

Strategic Investment ◽

Daily News

This study explores the initial impact of COVID-19 sentiment on US stock market using big data. Using the Daily News Sentiment Index (DNSI) and Google Trends data on coronavirus-related searches, this study investigates the correlation between COVID-19 sentiment and 11 select sector indices of the Unites States (US) stock market over the period from 21st of January 2020 to 20th of May 2020. While extensive research on sentiment analysis for predicting stock market movement use tweeter data, not much has used DNSI or Google Trends data. In addition, this study examines whether changes in DNSI predict US industry returns differently by estimating the time series regression model with excess returns of industry as the dependent variable. The excess returns are obtained from the Fama-French three factor model. The results of this study offer a comprehensive view of the initial impact of COVID-19 sentiment on the US stock market by industry and furthermore suggests the strategic investment planning considering the time lag perspectives by visualizing changes in the correlation level by time lag differences.

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