scholarly journals The correlation between Google trends and salmonellosis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ming-Yang Wang ◽  
Nai-jun Tang
Keyword(s):  
Infectious Disease ◽  
Cross Correlation ◽  
Hypovolemic Shock ◽  
Google Trends ◽  
Search Terms ◽  
Cross Correlation Analysis ◽  
Common Infectious Disease ◽  
The Usa ◽  
Google Search ◽  
The Relationship

Abstract Background Salmonella infection (salmonellosis) is a common infectious disease leading to gastroenteritis, dehydration, uveitis, etc. Internet search is a new method to monitor the outbreak of infectious disease. An internet-based surveillance system using internet data is logistically advantageous and economical to show term-related diseases. In this study, we tried to determine the relationship between salmonellosis and Google Trends in the USA from January 2004 to December 2017. Methods We downloaded the reported salmonellosis in the USA from the National Outbreak Reporting System (NORS) from January 2004 to December 2017. Additionally, we downloaded the Google search terms related to salmonellosis from Google Trends in the same period. Cross-correlation analysis and multiple regression analysis were conducted. Results The results showed that 6 Google Trends search terms appeared earlier than reported salmonellosis, 26 Google Trends search terms coincided with salmonellosis, and 16 Google Trends search terms appeared after salmonellosis were reported. When the search terms preceded outbreaks, “foods” (t = 2.927, P = 0.004) was a predictor of salmonellosis. When the search terms coincided with outbreaks, “hotel” (t = 1.854, P = 0.066), “poor sanitation” (t = 2.895, P = 0.004), “blueberries” (t = 2.441, P = 0.016), and “hypovolemic shock” (t = 2.001, P = 0.047) were predictors of salmonellosis. When the search terms appeared after outbreaks, “ice cream” (t = 3.077, P = 0.002) was the predictor of salmonellosis. Finally, we identified the most important indicators of Google Trends search terms, including “hotel” (t = 1.854, P = 0.066), “poor sanitation” (t = 2.895, P = 0.004), “blueberries” (t = 2.441, P = 0.016), and “hypovolemic shock” (t = 2.001, P = 0.047). In the future, the increased search activities of these terms might indicate the salmonellosis. Conclusion We evaluated the related Google Trends search terms with salmonellosis and identified the most important predictors of salmonellosis outbreak.

scholarly journals Nowcasting Sexually Transmitted Infections in Chicago: Predictive Modeling and Evaluation Study Using Google Trends

10.2196/20588 ◽  
2020 ◽  
Vol 6 (4) ◽  
pp. e20588
Author(s):  
Amy Kristen Johnson ◽  
Runa Bhaumik ◽  
Irina Tabidze ◽  
Supriya D Mehta
Keyword(s):  
Public Health ◽  
Correlation Analysis ◽  
Prediction Accuracy ◽  
Cross Correlation ◽  
Secondary Syphilis ◽  
Google Trends ◽  
Sexually Transmitted ◽  
Search Terms ◽  
Cross Correlation Analysis ◽  
Case Data

Background Sexually transmitted infections (STIs) pose a significant public health challenge in the United States. Traditional surveillance systems are adversely affected by data quality issues, underreporting of cases, and reporting delays, resulting in missed prevention opportunities to respond to trends in disease prevalence. Search engine data can potentially facilitate an efficient and economical enhancement to surveillance reporting systems established for STIs. Objective We aimed to develop and train a predictive model using reported STI case data from Chicago, Illinois, and to investigate the model’s predictive capacity, timeliness, and ability to target interventions to subpopulations using Google Trends data. Methods Deidentified STI case data for chlamydia, gonorrhea, and primary and secondary syphilis from 2011-2017 were obtained from the Chicago Department of Public Health. The data set included race/ethnicity, age, and birth sex. Google Correlate was used to identify the top 100 correlated search terms with “STD symptoms,” and an autocrawler was established using Google Health Application Programming Interface to collect the search volume for each term. Elastic net regression was used to evaluate prediction accuracy, and cross-correlation analysis was used to identify timeliness of prediction. Subgroup elastic net regression analysis was performed for race, sex, and age. Results For gonorrhea and chlamydia, actual and predicted STI values correlated moderately in 2011 (chlamydia: r=0.65; gonorrhea: r=0.72) but correlated highly (chlamydia: r=0.90; gonorrhea: r=0.94) from 2012 to 2017. However, for primary and secondary syphilis, the high correlation was observed only for 2012 (r=0.79), 2013 (r=0.77), 2016 (0.80), and 2017 (r=0.84), with 2011, 2014, and 2015 showing moderate correlations (r=0.55-0.70). Model performance was the most accurate (highest correlation and lowest mean absolute error) for gonorrhea. Subgroup analyses improved model fit across disease and year. Regression models using search terms selected from the cross-correlation analysis improved the prediction accuracy and timeliness across diseases and years. Conclusions Integrating nowcasting with Google Trends in surveillance activities can potentially enhance the prediction and timeliness of outbreak detection and response as well as target interventions to subpopulations. Future studies should prospectively examine the utility of Google Trends applied to STI surveillance and response.

scholarly journals Nowcasting Sexually Transmitted Infections in Chicago: Predictive Modeling and Evaluation Study Using Google Trends (Preprint)

2020 ◽  
Author(s):  
Amy Kristen Johnson ◽  
Runa Bhaumik ◽  
Irina Tabidze ◽  
Supriya D Mehta
Keyword(s):  
Public Health ◽  
Correlation Analysis ◽  
Prediction Accuracy ◽  
Cross Correlation ◽  
Secondary Syphilis ◽  
Google Trends ◽  
Sexually Transmitted ◽  
Search Terms ◽  
Cross Correlation Analysis ◽  
Case Data

BACKGROUND Sexually transmitted infections (STIs) pose a significant public health challenge in the United States. Traditional surveillance systems are adversely affected by data quality issues, underreporting of cases, and reporting delays, resulting in missed prevention opportunities to respond to trends in disease prevalence. Search engine data can potentially facilitate an efficient and economical enhancement to surveillance reporting systems established for STIs. OBJECTIVE We aimed to develop and train a predictive model using reported STI case data from Chicago, Illinois, and to investigate the model’s predictive capacity, timeliness, and ability to target interventions to subpopulations using Google Trends data. METHODS Deidentified STI case data for chlamydia, gonorrhea, and primary and secondary syphilis from 2011-2017 were obtained from the Chicago Department of Public Health. The data set included race/ethnicity, age, and birth sex. Google Correlate was used to identify the top 100 correlated search terms with “STD symptoms,” and an autocrawler was established using Google Health Application Programming Interface to collect the search volume for each term. Elastic net regression was used to evaluate prediction accuracy, and cross-correlation analysis was used to identify timeliness of prediction. Subgroup elastic net regression analysis was performed for race, sex, and age. RESULTS For gonorrhea and chlamydia, actual and predicted STI values correlated moderately in 2011 (chlamydia: <i>r</i>=0.65; gonorrhea: <i>r</i>=0.72) but correlated highly (chlamydia: <i>r</i>=0.90; gonorrhea: <i>r</i>=0.94) from 2012 to 2017. However, for primary and secondary syphilis, the high correlation was observed only for 2012 (<i>r</i>=0.79), 2013 (<i>r</i>=0.77), 2016 (0.80), and 2017 (<i>r</i>=0.84), with 2011, 2014, and 2015 showing moderate correlations (<i>r</i>=0.55-0.70). Model performance was the most accurate (highest correlation and lowest mean absolute error) for gonorrhea. Subgroup analyses improved model fit across disease and year. Regression models using search terms selected from the cross-correlation analysis improved the prediction accuracy and timeliness across diseases and years. CONCLUSIONS Integrating nowcasting with Google Trends in surveillance activities can potentially enhance the prediction and timeliness of outbreak detection and response as well as target interventions to subpopulations. Future studies should prospectively examine the utility of Google Trends applied to STI surveillance and response.

Prediction of norovirus using Internet search data in the US (Preprint)

2020 ◽  
Author(s):  
Kai Yuan ◽  
Guangrui Huang ◽  
Haixu Jiang ◽  
Wenbin Liu ◽  
Ting Wang ◽  
...  
Keyword(s):  
New York ◽  
Disease Outbreaks ◽  
Temporal Correlation ◽  
Google Trends ◽  
Watery Diarrhea ◽  
Norwalk Virus ◽  
Internet Search ◽  
Search Terms ◽  
Cross Correlation Analysis ◽  
Google Search

BACKGROUND Norovirus is a contagious disease leading to vomiting and diarrhea. The transmission of norovirus spreads quickly and easily in various ways. Because effective methods to prevent or treat norovirus have not been discovered, it is important to rapidly recognize and report norovirus outbreaks in the early phase. Internet search has been a useful method for people to access information immediately. With the precise record of Internet search trends, Internet search has been a useful tool to manifest infectious disease outbreaks. OBJECTIVE In this study, we tried to discover the correlation between Internet search terms and norovirus infection. METHODS The Internet search trend data of norovirus were obtained from Google Trends. We used cross-correlation analysis to discover the temporal correlation between norovirus and other terms. We also used multiple linear regression with the stepwise method to recognize the most important predictors of Internet search trends and norovirus. In addition, we evaluated the temporal correlation between actual norovirus cases and Internet search terms in New York, California, and USA. RESULTS Some Google search terms such as gastroenteritis, vomiting, and watery diarrhea were coincided with norovirus Google Trends. Some Google search terms such as contagious, Norwalk virus, travel presented earlier than norovirus Google Trends. Some Google search terms such as dehydration, bar, and restaurant presented several months later than norovirus Google Trends. We found that the symptoms of gastroenteritis, including vomiting and watery diarrhea, were important factors that were significantly correlated with norovirus Google Trends. In actual norovirus cases of New York, California, and USA, some Google search terms presented coincided, earlier, or later than actual norovirus cases. CONCLUSIONS Our study provides novel strategy-based Internet search evidence regarding the epidemiology of norovirus.

scholarly journals Infodemiology of systemic lupus erythematous using Google Trends

Lupus ◽  
2017 ◽  
Vol 26 (8) ◽  
pp. 886-889 ◽  
Author(s):  
M Radin ◽  
S Sciascia
Keyword(s):  
Big Data ◽  
Northern Hemisphere ◽  
Lupus Erythematosus ◽  
Kendall Test ◽  
Google Trends ◽  
Data Monitoring ◽  
Search Term ◽  
Systemic Lupus ◽  
Search Terms ◽  
Google Search

Objective People affected by chronic rheumatic conditions, such as systemic lupus erythematosus (SLE), frequently rely on the Internet and search engines to look for terms related to their disease and its possible causes, symptoms and treatments. ‘Infodemiology’ and ‘infoveillance’ are two recent terms created to describe a new developing approach for public health, based on Big Data monitoring and data mining. In this study, we aim to investigate trends of Internet research linked to SLE and symptoms associated with the disease, applying a Big Data monitoring approach. Methods We analysed the large amount of data generated by Google Trends, considering ‘lupus’, ‘relapse’ and ‘fatigue’ in a 10-year web-based research. Google Trends automatically normalized data for the overall number of searches, and presented them as relative search volumes, in order to compare variations of different search terms across regions and periods. The Menn–Kendall test was used to evaluate the overall seasonal trend of each search term and possible correlation between search terms. Results We observed a seasonality for Google search volumes for lupus-related terms. In the Northern hemisphere, relative search volumes for ‘lupus’ were correlated with ‘relapse’ (τ = 0.85; p = 0.019) and with fatigue (τ = 0.82; p = 0.003), whereas in the Southern hemisphere we observed a significant correlation between ‘fatigue’ and ‘relapse’ (τ = 0.85; p = 0.018). Similarly, a significant correlation between ‘fatigue’ and ‘relapse’ (τ = 0.70; p < 0.001) was seen also in the Northern hemisphere. Conclusion Despite the intrinsic limitations of this approach, Internet-acquired data might represent a real-time surveillance tool and an alert for healthcare systems in order to plan the most appropriate resources in specific moments with higher disease burden.

Wavelet Cross-Correlation Analysis of the Unsteady Signals of the Flow in the Open Sump

Volume 1 ◽  
2004 ◽  
Author(s):  
Mansa Kante ◽  
Yulin Wu ◽  
Yong Li ◽  
Shuhong Liu ◽  
Daqing Zhou
Keyword(s):  
Cross Correlation ◽  
Correlation Method ◽  
Vibration Signal ◽  
Structural Similarity ◽  
Signal Distribution ◽  
Cross Correlation Analysis ◽  
Correlation Scale ◽  
Pump Sump ◽  
The Relationship ◽  
Signal Analysis Method

The wavelet cross-correlation method was used to analyze the unsteady signals of the flow of the model open pump sump, which include pressure signal, vibration signal and acoustic signal. The continuous wavelet transform was done first to find the signal distribution at various periods and at any time, then the wavelet cross-correlation was used to find the relationship between the signals taken two a two. Through comparing the result of wavelet cross-correlation and the result of classic cross-correlation, one can find the correlation scale of any two unsteady signals (pressure-vibration, pressure-noise, and vibration-noise). The signal on the correlation scale was reconstruct and its characteristics were obtained using classical signal analysis method same as the structural similarity of a arbitrary two signals.

Analyzing the Impact of COVID-19 on the Cross-Correlations between Financial Search Engine Data and Movie Box Office

2020 ◽  
pp. 2150021
Author(s):  
Renyu Wang ◽  
Yujie Xie ◽  
Hong Chen ◽  
Guozhu Jia
Keyword(s):  
Financial Market ◽  
Cross Correlation ◽  
Multifractal Spectrum ◽  
Movie Industry ◽  
Box Office ◽  
Cross Correlation Analysis ◽  
Cross Correlations ◽  
The Cross ◽  
Google Search ◽  
The Impact

This paper explores the COVID-19 influences on the cross-correlation between the movie market and the financial market. The nonlinear cross-correlations between movie box office data and Google search volumes of financial terms such as Dow Jones Industrial Average (DJIA), NASDAQ and PMI are investigated based on multifractal detrended cross-correlation analysis (MF-DCCA). The empirical results show there are nonlinear cross-correlations between movie market and financial market. Metrics such as Hurst exponents, singular exponents and multifractal spectrum demonstrate that the cross-correlation between movie market and financial market is persistent, and the cross-correlation in long term is more stable than that in short term. In the COVID-19 period, the multifractal features of cross-correlation become stronger implying that COVID-19 enhanced the complexity between the movie industry and the financial market. Furthermore, through the rolling window analysis, the Hurst exponent dynamic trends indicate that COVID-19 has a clear influence on the cross-correlation between movie market and financial market.

Insomnia-related Baidu Index During the COVID-19 Outbreak in China: Infodemiology Study (Preprint)

2021 ◽  
Author(s):  
Yuying Chu ◽  
Xue Wang ◽  
Hongliang Dai
Keyword(s):  
Mental Health ◽  
Correlation Analysis ◽  
Cross Correlation ◽  
Mental Health Problems ◽  
Health Workers ◽  
Descriptive Analysis ◽  
Chinese Government ◽  
Search Terms ◽  
Search Volume ◽  
Cross Correlation Analysis

BACKGROUND Since December 2019, an unexplained pneumonia has broken out in Wuhan, Hubei Province, China. In order to prevent the rapid spread of this disease, quarantine or lockdown measures were taken by Chinese government. These measures turned out to be effective in containing the contagious disease. Quarantine itself, however, would potentially cause certain health risks among the affected population, such as sleep disorder. OBJECTIVE The aims of this work were to analyze the volume of insomnia-related search during the COVID-19 outbreak in China, to explore the potential use of the Baidu Index for monitoring social and psychological distress, and to help community health workers provide timely and effective interventions for the public. METHODS In the context of the pandemic, we conducted a descriptive analysis of the overall search situation. Spearman's correlation and cross-correlation analysis were used to explore the relationship between daily search index values for insomnia-related terms and daily newly confirmed cases. The means of search volume for insomnia-related terms during the COVID-19 quarantine or knockdown period (January 23rd, 2020 to April 8th, 2020) were compared with those during 2016 to 2019 using a Student's t test. Finally, by analyzing the overall daily mean of insomnia in various provinces, we further evaluated whether there existed regional differences in searching for insomnia during COVID-19 isolation. RESULTS During COVID-19 lockdown, the number of insomnia-related searches increased significantly, especially the average daily the Baidu Index for “the best treatment for insomnia” reaching 5,923.86. Seventeen out of the 24 insomnia related search terms were associated with daily newly confirmed cases, of which “a simple cure for insomnia” had the closest correlation (r=0.676; P<.001). The cross-correlation analysis also verified the forward or backward time correlation between daily newly confirmed cases and the search terms. Compared with the same period in the past four years, a significant change in insomnia-related search volume was found during COVID-19 quarantine period. We also found that all provinces suffered from insomnia during the quarantine period, with Guangdong province representing the leading areas for insomnia-related search. CONCLUSIONS Quarantine measures have led to an increase in insomnia-related searches during the COVID-19 pandemic. Community medical staff should use big data-based tools to screen for insomnia and mental health problems. Early interventions toward insomnia and associated mental health are also essential for prevention and reduction of the long-term impact of the major traumatic events.

The distribution of corticotectal projection neurons correlates with the interblob compartment in macaque striate cortex

1996 ◽  
Vol 13 (3) ◽  
pp. 461-466 ◽  
Author(s):  
Barry Lia ◽  
Jaime F. Olavarria
Keyword(s):  
Cross Correlation ◽  
Random Distribution ◽  
Striate Cortex ◽  
Projection Neurons ◽  
Chi Square ◽  
Cortical Projections ◽  
Cross Correlation Analysis ◽  
Chi Square Analysis ◽  
Relationship Of ◽  
The Relationship

AbstractWhile much attention has been given to the correlation between cytochrome-oxidase (CO) compartments and patterns of cortico-cortical projections originating from supragranular layers in the striate cortex, little is known in this regard about patterns of cortico-subcortical projections originating from infragranular cortex. We studied the tangential distribution of the striate cortex neurons projecting to the superior colliculus and used two approaches to analyze the relationship of this distribution to the arrangement of CO “blobs.” First, chi-square analysis indicated that significantly fewer labeled neurons were found within the CO blob compartment than the number expected for a random distribution. Second, spatial cross-correlation analysis – which circumvents the inherent subjectivity of delineating blob boundaries – revealed an area around blob centers in which there was a decreased probability of encountering labeled cells. The size of this area compared well with that of our outlines of CO blobs. We conclude that corticotectal projection neurons in the striate cortex are distributed preferentially within the interblob compartment of the infragranular striate cortex. These results demonstrate that the spatial distribution of cortico-subcortical projection neurons within infragranular cortex can correlate with the CO architecture of the primary visual cortex.

scholarly journals How popular is Islamic finance in the USA? Findings from Google Trends

2019 ◽  
Vol 8 (3) ◽  
pp. 58-65
Author(s):  
Wesal M. Aldarabseh
Keyword(s):  
New York ◽  
New Jersey ◽  
Longitudinal Survey ◽  
Global Distribution ◽  
Google Trends ◽  
Islamic Finance ◽  
Us States ◽  
Islamic Bank ◽  
Search Terms ◽  
The Usa

Islamic finance is a growing industry with global distribution in all continents including Europe and America. The aim of the current study was to examine how popular is Islamic finance in the USA during the period 2014-2019 using Google Trends. In addition, the interest in Islamic finance across different US states was also investigated. Using “Islamic finance” and “Islamic bank” as search terms in Goggle Trends, the trend curve showed decreases in search volumes, suggesting a decline in the popularity of Islamic finance in the USA with years. Search volumes were detected in seven out of 50 states, suggesting low interest in Islamic finance in the majority of US states. The order of the popularity in the seven states was: Virginia > New York > New Jersey > Illinois > Texas > California > Pennsylvania > Georgia > Florida > Massachusetts. Longitudinal survey studies are needed to confirm the present findings.

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