Dengue fever(DF),caused by the Dengue virus through the Aedes mosquito vector,is a dangerous infectious disease with the potential to become a global epidemic.Vietnam,particularly Ba Ria-Vung Tau(BRVT)province,is faci...Dengue fever(DF),caused by the Dengue virus through the Aedes mosquito vector,is a dangerous infectious disease with the potential to become a global epidemic.Vietnam,particularly Ba Ria-Vung Tau(BRVT)province,is facing a high risk of DF.This study aims to determine the relationship between the search volume for DF on Google Trends and DF cases in BRVT province,thereby constructing a model to predict the early outbreak risk of DF locally.Using Poisson regression(adjusted by quasi-Poisson),considering the lagged effect of Google Trends Index(GTI)search volume on DF cases,and removing the autocorrelation(AC)of DF cases by using appropriate transformations,seven forecast models were surveyed based on the dataset of DF cases and GTI search volume weekly with the phrase"sôt xuàt huyêt"(dengue fever)in BRVT province from January 2019 to August 2023(243 weeks).The model selected is the one with the lowest dispersion index.The results show that the correlation coefficient(95%confidence interval)and dispersion index of the 7 models including Basis TSR;Basis TSR t AC:Lag(Residuals,1);Basis TSR t AC:Lag(SXH,1);Basis TSR t AC:Lag(log(SXHt1),1);TSR Lag(GTI,2)t AC:Lag(log(SXHt1),2);TSR Lag(GTI,3)t AC:Lag(log(SXHt1),3);TSR Lag(GTI,0)t AC:Lag(log(SXHt1),1)are 0.71(0.63-0.76)and 74.2;0.79(0.73-0.83)and 48.6;0.89(0.87-0.92)and 37.3;0.98(0.97-0.99)and 7.2;0.96(0.95-0.97)and 14.3;0.93(0.91-0.94)and 25.7;0.98(0.97-0.99)and 6.8,respectively.Therefore,the final model is the most suitable one selected.Testing the accuracy of the selected model using the ROC curve with the Youden criterion,the AUC(threshold 75%)is 0.982,and the AUC(threshold 95%)is 0.984,indicating the very good predictive ability of the model.In summary,the research results show the potential for applying this model in Vietnam,especially in BRVT,to enhance the effectiveness of epidemic prevention measures and protect public health.展开更多
Background:The 2014 Ebola epidemic in West Africa has attracted public interest worldwide,leading to millions of Ebola-related Internet searches being performed during the period of the epidemic.This study aimed to ev...Background:The 2014 Ebola epidemic in West Africa has attracted public interest worldwide,leading to millions of Ebola-related Internet searches being performed during the period of the epidemic.This study aimed to evaluate and interpret Google search queries for terms related to the Ebola outbreak both at the global level and in all countries where primary cases of Ebola occurred.The study also endeavoured to look at the correlation between the number of overall and weekly web searches and the number of overall and weekly new cases of Ebola.Methods:Google Trends(GT)was used to explore Internet activity related to Ebola.The study period was from 29 December 2013 to 14 June 2015.Pearson’s correlation was performed to correlate Ebola-related relative search volumes(RSVs)with the number of weekly and overall Ebola cases.Multivariate regression was performed using Ebola-related RSV as a dependent variable,and the overall number of Ebola cases and the Human Development Index were used as predictor variables.Results:The greatest RSV was registered in the three West African countries mainly affected by the Ebola epidemic.The queries varied in the different countries.Both quantitative and qualitative differences between the affected African countries and other Western countries with primary cases were noted,in relation to the different flux volumes and different time courses.In the affected African countries,web query search volumes were mostly concentrated in the capital areas.However,in Western countries,web queries were uniformly distributed over the national territory.In terms of the three countries mainly affected by the Ebola epidemic,the correlation between the number of new weekly cases of Ebola and the weekly GT index varied from weak to moderate.The correlation between the number of Ebola cases registered in all countries during the study period and the GT index was very high.Conclusion:Google Trends showed a coarse-grained nature,strongly correlating with global epidemiological data,but was weaker at country level,as it was prone to distortions induced by unbalanced media coverage and the digital divide.Global and local health agencies could usefully exploit GT data to identify disease-related information needs and plan proper communication strategies,particularly in the case of health-threatening events.展开更多
While incomplete non-medical data has been integrated into prediction models for epidemics,the accuracy and the generalizability of the data are difficult to guarantee.To comprehensively evaluate the ability and appli...While incomplete non-medical data has been integrated into prediction models for epidemics,the accuracy and the generalizability of the data are difficult to guarantee.To comprehensively evaluate the ability and applicability of using social media data to predict the development of COVID-19,a new confirmed case prediction algorithm improving the Google Flu Trends algorithm is established,called Weibo COVID-19 Trends(WCT),based on the post dataset generated by all users in Wuhan on Sina Weibo.A genetic algorithm is designed to select the keyword set for filtering COVID-19 related posts.WCT can constantly outperform the highest average test score in the training set between daily new confirmed case counts and the prediction results.It remains to produce the best prediction results among other algorithms when the number of forecast days increases from one to eight days with the highest correlation score from 0.98(P<0.01)to 0.86(P<0.01)during all analysis period.Additionally,WCT effectively improves the Google Flu Trends algorithm's shortcoming of overestimating the epidemic peak value.This study offers a highly adaptive approach for feature engineering of third-party data in epidemic prediction,providing useful insights for the prediction of newly emerging infectious diseases at an early stage.展开更多
Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health resources.Due to the exponential spread of the COVID-19...Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health resources.Due to the exponential spread of the COVID-19 infection worldwide,several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the literature.To accelerate scientific and public health insights into the spread and impact of COVID-19,Google released the Google COVID-19 search trends symptoms open-access dataset.Our objective is to develop 7 and 14-day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related symptoms.Specifically,we propose a stacked long short-term memory(SLSTM)architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms dataset.Considering the SLSTM networks trained using historical data only as the base models,our base models for 7 and 14-day-ahead forecasting of COVID cases had the mean absolute percentage error(MAPE)values of 6.6%and 8.8%,respectively.On the other side,our proposed models had improved MAPE values of 3.2%and 5.6%,respectively.For 7 and 14-day-ahead forecasting of COVID-19 deaths,the MAPE values of the base models were 4.8%and 11.4%,while the improved MAPE values of our proposed models were 4.7%and 7.8%,respectively.We found that the Google search trends for“pneumonia,”“shortness of breath,”and“fever”are the most informative search trends for predicting COVID-19 transmission.We also found that the search trends for“hypoxia”and“fever”were the most informative trends for forecasting COVID-19 mortality.展开更多
Objective:Minimally invasive treatments for benign prostatic hyperplasia (BPH) have seen an increase in usage in recent years. We aimed to determine what types of events may influence patient search habits related to ...Objective:Minimally invasive treatments for benign prostatic hyperplasia (BPH) have seen an increase in usage in recent years. We aimed to determine what types of events may influence patient search habits related to surgical BPH treatments.Methods:Google Trends was used to determine the frequency of searches for different minimally invasive and prostatic ablative treatments for BPH in the United States. The procedures including transurethral resection of the prostate (TURP), Aquablation therapy (Aquablation), Greenlight laser therapy (Greenlight), transurethral needle ablation, transurethral microwave thermotherapy, Urolift (prostatic urethral lift [PUL]), Rezum, iTind, holmium laser enucleation of the prostate, simple prostatectomy, and prostatic artery embolization were compared.Results:From January 1, 2004 to February 28, 2023, the number of internet search queries have increased for TURP, PUL, Rezum, prostatic artery embolization, and holmium laser enucleation of the prostate. There has been a slight decrease in searches for Greenlight, transurethral needle ablation, transurethral microwave thermotherapy, iTind, simple prostatectomy, and Aquablation.Conclusion:Despite increased searches of alternatives, TURP remains the most searched BPH procedure. Additionally, search habits may be influenced by several factors including government approval, corporate acquisition, and marketing campaigns. It is important for physicians to understand the types of events that may cause patients to inquire about certain treatments for better quality health information and clinical visits.展开更多
This study systematically reviewed the literature on using the Google Search Volume Index(GSVI)as a proxy variable for investor attention and stock market movements.We analyzed 56 academic studies published between 20...This study systematically reviewed the literature on using the Google Search Volume Index(GSVI)as a proxy variable for investor attention and stock market movements.We analyzed 56 academic studies published between 2010 and 2021 using the Web of Sciences and ScienceDirect databases.The articles were classified and synthesized based on the selection criteria for building the GSVI:keywords of the search term,market region,and frequency of the data sample.Next,we analyze the effect of returns,volatility,and trading volume on the financial variables.The main results can be summarized as follows.(1)The GSVI is positively related to volatility and trading volume regardless of the keyword,market region,or frequency used for the sample.Hence,increasing investor attention toward a specific financial term will increase volatility and trading volume.(2)The GSVI can improve forecasting models for stock market movements.To conclude,this study consolidates,for the first time,the research literature on GSVI,which is highly valuable for academic practitioners in the area.展开更多
Recently,internet users have significantly increased their use of search engines,and market investors are no exception.As a result,predictive models that incorporate scattered web-based information are developing as a...Recently,internet users have significantly increased their use of search engines,and market investors are no exception.As a result,predictive models that incorporate scattered web-based information are developing as an area of forecasting.The objective of this research is to compare the predictive accuracy of fundamental macroeconomic variables,online attention series measured by the Google Trends search volume index,and a combination of both data types for the Mexican,Brazilian,Chilean,and Colombian currencies paired with the USD.The exchange rate series used in this study are sourced from a real-time platform.Four indicators capturing the fundamental macroeconomic differences between these emerging economies and the U.S.from January 2004 to March 2021(monthly)were analyzed.To assess the predictive performance of the KNN algorithm,OLS regression and the random walk with drift model were compared.Considering in-sample predictions,the results generally exhibit lower estimation errors in the random walk with drift model,but in the joint fundamental–online attention data,the KNN and OLS predictions are more accurate than those of the random walk with drift.However,the KNN predictions based on out-of-sample fit generate the lowest estimation errors and the most accurate predictions for the joint fundamental–online attention data.Additionally,performance testing indicates that the KNN extended model outperforms the out-ofsample forecast for the OLS regression and the random walk with drift model.展开更多
The increasing attention on Bitcoin since 2013 prompts the issue of possible evidence for a causal relationship between the Bitcoin market and internet attention.Taking the Google search volume index as the measure of...The increasing attention on Bitcoin since 2013 prompts the issue of possible evidence for a causal relationship between the Bitcoin market and internet attention.Taking the Google search volume index as the measure of internet attention,time-varying Granger causality between the global Bitcoin market and internet attention is examined.Empirical results show a strong Granger causal relationship between internet attention and trading volume.Moreover,they indicate,beginning in early 2018,an even stronger impact of trading volume on internet attention,which is consistent with the rapid increase in Bitcoin users following the 2017 Bitcoin bubble.Although Bitcoin returns are found to strongly affect internet attention,internet attention only occasionally affects Bitcoin returns.Further investigation reveals that interactions between internet attention and returns can be amplified by extreme changes in prices,and internet attention is more likely to lead to returns during Bitcoin bubbles.These empirical findings shed light on cryptocurrency investor attention theory and imply trading strategy in Bitcoin markets.展开更多
The synchronicity effect between the financial market and online response for time-series forecasting is an important task with wide applications.This study combines data from the Baidu index(BDI),Google trends(GT),an...The synchronicity effect between the financial market and online response for time-series forecasting is an important task with wide applications.This study combines data from the Baidu index(BDI),Google trends(GT),and transfer entropy(TE)to forecast a wide range of futures prices with a focus on China.A forecasting model based on a hybrid gray wolf optimizer(GWO),convolutional neural network(CNN),and long short-term memory(LSTM)is developed.First,Baidu and Google dual-platform search data were selected and constructed as Internetbased consumer price index(ICPI)using principal component analysis.Second,TE is used to quantify the information between online behavior and futures markets.Finally,the effective Internet-based consumer price index(ICPI)and TE are introduced into the GWO-CNN-LSTM model to forecast the daily prices of corn,soybean,polyvinyl chloride(PVC),egg,and rebar futures.The results show that the GWO-CNN-LSTM model has a significant improvement in predicting future prices.Internet-based CPI built on Baidu and Google platforms has a high degree of real-time performance and reduces the platform and language bias of the search data.Our proposed framework can provide predictive decision support for government leaders,market investors,and production activities.展开更多
Roads may influence the selection of phenotypic traits of wildlife.In particular,the likelihood of vehicle collisions with wildlife may vary depending on body coloration in contrast to the road,which may be exaggerate...Roads may influence the selection of phenotypic traits of wildlife.In particular,the likelihood of vehicle collisions with wildlife may vary depending on body coloration in contrast to the road,which may be exaggerated by cultural attitudes toward the species.The timber rattlesnake Crotalus horridus is a threatened species that varies widely in coloration,and their color pattern could influence thermoregulatory use of roads and visibility to motorists.Moreover,better-camouflaged snakes may have higher road mortality in areas where environmental interest is lower and,perhaps,negative attitudes toward wildlife are more prevalent.We used citizen scientist observations of timber rattlesnakes from iNaturalist and categorized for each rattlesnake the surface they were on,its color pattern,and whether they were alive.We combined iNaturalist data with Google Trends data to characterize regional variation in environmental interest.We discovered that lighter-colored snakes were more likely to be found on roads,as were snakes further south,west,and on warmer days.Once on a road,coloration did not influence survival regardless of road type or environmental interest.However,snakes on asphalt roads or on southern roads were more likely to be found dead.The higher likelihood of lighter-colored snakes being found on roads suggests that they are at a greater overall risk of road death,potentially selecting for darker coloration.Citizen scientist behavior may at least partly underlie the influence of latitude on the results,however,and further work in the application of citizen science data to such research questions is warranted.展开更多
Background:Preliminary evidence suggests that the burden of stress and anxiety may have considerably increased during the coronavirus disease 2019(COVID-19)pandemic.Since these two mental health-related factors are im...Background:Preliminary evidence suggests that the burden of stress and anxiety may have considerably increased during the coronavirus disease 2019(COVID-19)pandemic.Since these two mental health-related factors are important causes of teeth grinding,we carried out an infodemiological analysis to define whether the burden of teeth grinding may have increased as a consequence of COVID-19.Methods:We conducted an electronic search in Google Trends,with the term“teeth grinding”,setting the geographical area to“US”or“UK”and the search period between July 2017 and July 2022.The weekly Google Trends score for“teeth grinding”was downloaded,and the difference in the volume Google searches for“teeth grinding”was compared between the pre-COVID-19 and COVID-19 periods in both countries.Results:The median value of weekly Google Trends score for“teeth grinding”was found to be significantly increased after emergence of the COVID-19 pandemic both in the UK(57 with interquartile range[IQR]51-64 vs.48 with IQR 42-53;+19%and P<0.001)and the US(78 with IQR 73-83 vs.70 with IQR 66-74;+11%and P<0.001),compared to the homologous period before.Conclusion:The results of this infodemiological analysis reveal that the volume of Web searches for“teeth grinding”in both the UK and US has considerably increased after emergence of the COVID-19 pandemic,thus probably reflecting an increased burden of this condition in the general population.展开更多
Understanding the interplay between investor sentiment and cryptocurrency returns has become a critical area of research.Indeed,this study aims to uncover the role of Google investor sentiment on cryptocurrency return...Understanding the interplay between investor sentiment and cryptocurrency returns has become a critical area of research.Indeed,this study aims to uncover the role of Google investor sentiment on cryptocurrency returns(including Bitcoin,Litecoin,Ethereum,and Tether),especially during the 2017-18 bubble(January 01,2017,to December 31,2018)and the COVID-19 pandemic(January 01,2020,to March 15,2022).To achieve this,we use two techniques:quantile causality and wavelet coherence.First,the quantile causality test revealed that investors’optimistic sentiments have notably higher cryptocurrency returns,whereas pessimistic sentiments have significantly opposite effects.Moreover,the wavelet coherence analysis shows that co-movement between investor sentiment and Tether cannot be considered significant.This result supports the role of Tether as a stablecoin in portfolio diversification strategies.In fact,the findings will help investors improve the accuracy of cryptocurrency return forecasts in times of stressful events and pave the way for enhanced decision-making utility.展开更多
Lamar University established a Center for Resiliency in 2021 in response to the increasing natural and human-made disasters in the Southeast Texas region.Now more than ever a robust decision-making framework is essent...Lamar University established a Center for Resiliency in 2021 in response to the increasing natural and human-made disasters in the Southeast Texas region.Now more than ever a robust decision-making framework is essential for this recently established regional interdisciplinary resiliency center to make well-informed decisions,prioritize funding eff ectively,and nurture collaboration across various fields to help communities vulnerable to these threats.This article provides a progress update and presents a methodology integrating VOSviewer and Google Trends to develop such a decision-making framework for the Center for Resiliency at Lamar University in the Southeast Texas region.Four prominent study areas in resilience—Climate Stressors and Disasters,Mental Wellness,Energy and Optimization,and Resilience Planning—were identified.These findings were validated with real-time insights from Google Trends,ensuring practical relevance to recent resilience needs to provide an understanding of evolving resilience dynamics.Furthermore,the paper discusses the status of research conducted at the Lamar University Center for Resiliency,showcasing its commitment to fostering resilience through diverse initiatives across five academic colleges.Integrating VOSviewer and Google Trends off ers a robust framework for informed decision making,aligning research eff orts with the Southeast Texas region’s current and future resilience needs.展开更多
This review analyzes the state and recent progress in the field of information support for pollen allergy sufferers.For decades,information available for the patients and allergologists consisted of pollen counts,whic...This review analyzes the state and recent progress in the field of information support for pollen allergy sufferers.For decades,information available for the patients and allergologists consisted of pollen counts,which are vital but insufficient.New technology paves the way to substantial increase in amount and diversity of the data.This paper reviews old and newly suggested methods to predict pollen and air pollutant concentrations in the air and proposes an allergy risk concept,which combines the pollen and pollution information and transforms it into a qualitative risk index.This new index is available in an app(Mobile Airways Sentinel NetworK-air)that was developed in the frame of the European Union grant Impact of Air POLLution on sleep,Asthma and Rhinitis(a project of European Institute of Innovation and Technology-Health).On-going transformation of the pollen allergy information support is based on new technological solutions for pollen and air quality monitoring and predictions.The new information-technology and artificial-intelligence-based solutions help to convert this information into easy-to-use services for both medical practitioners and allergy sufferers.展开更多
Official monthly unemployment data is unavailable in China, while intense public interest in unemployment requires timely and accurate information. Using data on web queries from lead search engines in China, Baidu an...Official monthly unemployment data is unavailable in China, while intense public interest in unemployment requires timely and accurate information. Using data on web queries from lead search engines in China, Baidu and Google, I build two indices measuring intensity of online unemployment-related searches. The unemployment-related search indices identify a structural break in the time series between October and November 2008, which corresponds to a turning point indicated by some macroeconomic indicators. The unemployment- related search indices are proven to have significant correlation with Purchasing Managers＇ Employment Indices and a set of macroeconomic indicators that are closely related to changes in unemployment in China. The results of Granger causality analysis show that the unemployment-related search indices can improve predictions of the c indicators. It suggests that unemploy- ment-related searches can potentially provide valuable, timely, and low-cost information for macroeconomic monitoring.展开更多
文摘Dengue fever(DF),caused by the Dengue virus through the Aedes mosquito vector,is a dangerous infectious disease with the potential to become a global epidemic.Vietnam,particularly Ba Ria-Vung Tau(BRVT)province,is facing a high risk of DF.This study aims to determine the relationship between the search volume for DF on Google Trends and DF cases in BRVT province,thereby constructing a model to predict the early outbreak risk of DF locally.Using Poisson regression(adjusted by quasi-Poisson),considering the lagged effect of Google Trends Index(GTI)search volume on DF cases,and removing the autocorrelation(AC)of DF cases by using appropriate transformations,seven forecast models were surveyed based on the dataset of DF cases and GTI search volume weekly with the phrase"sôt xuàt huyêt"(dengue fever)in BRVT province from January 2019 to August 2023(243 weeks).The model selected is the one with the lowest dispersion index.The results show that the correlation coefficient(95%confidence interval)and dispersion index of the 7 models including Basis TSR;Basis TSR t AC:Lag(Residuals,1);Basis TSR t AC:Lag(SXH,1);Basis TSR t AC:Lag(log(SXHt1),1);TSR Lag(GTI,2)t AC:Lag(log(SXHt1),2);TSR Lag(GTI,3)t AC:Lag(log(SXHt1),3);TSR Lag(GTI,0)t AC:Lag(log(SXHt1),1)are 0.71(0.63-0.76)and 74.2;0.79(0.73-0.83)and 48.6;0.89(0.87-0.92)and 37.3;0.98(0.97-0.99)and 7.2;0.96(0.95-0.97)and 14.3;0.93(0.91-0.94)and 25.7;0.98(0.97-0.99)and 6.8,respectively.Therefore,the final model is the most suitable one selected.Testing the accuracy of the selected model using the ROC curve with the Youden criterion,the AUC(threshold 75%)is 0.982,and the AUC(threshold 95%)is 0.984,indicating the very good predictive ability of the model.In summary,the research results show the potential for applying this model in Vietnam,especially in BRVT,to enhance the effectiveness of epidemic prevention measures and protect public health.
文摘Background:The 2014 Ebola epidemic in West Africa has attracted public interest worldwide,leading to millions of Ebola-related Internet searches being performed during the period of the epidemic.This study aimed to evaluate and interpret Google search queries for terms related to the Ebola outbreak both at the global level and in all countries where primary cases of Ebola occurred.The study also endeavoured to look at the correlation between the number of overall and weekly web searches and the number of overall and weekly new cases of Ebola.Methods:Google Trends(GT)was used to explore Internet activity related to Ebola.The study period was from 29 December 2013 to 14 June 2015.Pearson’s correlation was performed to correlate Ebola-related relative search volumes(RSVs)with the number of weekly and overall Ebola cases.Multivariate regression was performed using Ebola-related RSV as a dependent variable,and the overall number of Ebola cases and the Human Development Index were used as predictor variables.Results:The greatest RSV was registered in the three West African countries mainly affected by the Ebola epidemic.The queries varied in the different countries.Both quantitative and qualitative differences between the affected African countries and other Western countries with primary cases were noted,in relation to the different flux volumes and different time courses.In the affected African countries,web query search volumes were mostly concentrated in the capital areas.However,in Western countries,web queries were uniformly distributed over the national territory.In terms of the three countries mainly affected by the Ebola epidemic,the correlation between the number of new weekly cases of Ebola and the weekly GT index varied from weak to moderate.The correlation between the number of Ebola cases registered in all countries during the study period and the GT index was very high.Conclusion:Google Trends showed a coarse-grained nature,strongly correlating with global epidemiological data,but was weaker at country level,as it was prone to distortions induced by unbalanced media coverage and the digital divide.Global and local health agencies could usefully exploit GT data to identify disease-related information needs and plan proper communication strategies,particularly in the case of health-threatening events.
基金National Natural Science Foundation of China(Project No.:91846301,72025405,82041020,11975071,61773248,71771152)Sichuan Science and Technology Plan Project(Project No.:2020YFS0007)+2 种基金Hunan Science and Technology Plan Project(Project No.:2019GK2131,2020TP1013,2020JJ4673)Major Program of National Fund of Philosophy and Social Science of China(Project No.:18ZDA088,20ZDA060)Scientific Research Project of Shanghai Science and Technology Committee(Project No.:19511102202).
文摘While incomplete non-medical data has been integrated into prediction models for epidemics,the accuracy and the generalizability of the data are difficult to guarantee.To comprehensively evaluate the ability and applicability of using social media data to predict the development of COVID-19,a new confirmed case prediction algorithm improving the Google Flu Trends algorithm is established,called Weibo COVID-19 Trends(WCT),based on the post dataset generated by all users in Wuhan on Sina Weibo.A genetic algorithm is designed to select the keyword set for filtering COVID-19 related posts.WCT can constantly outperform the highest average test score in the training set between daily new confirmed case counts and the prediction results.It remains to produce the best prediction results among other algorithms when the number of forecast days increases from one to eight days with the highest correlation score from 0.98(P<0.01)to 0.86(P<0.01)during all analysis period.Additionally,WCT effectively improves the Google Flu Trends algorithm's shortcoming of overestimating the epidemic peak value.This study offers a highly adaptive approach for feature engineering of third-party data in epidemic prediction,providing useful insights for the prediction of newly emerging infectious diseases at an early stage.
基金This work is supported in part by the Deanship of Scientific Research at Jouf University under Grant No.(CV-28–41).
文摘Accurate forecasting of emerging infectious diseases can guide public health officials in making appropriate decisions related to the allocation of public health resources.Due to the exponential spread of the COVID-19 infection worldwide,several computational models for forecasting the transmission and mortality rates of COVID-19 have been proposed in the literature.To accelerate scientific and public health insights into the spread and impact of COVID-19,Google released the Google COVID-19 search trends symptoms open-access dataset.Our objective is to develop 7 and 14-day-ahead forecasting models of COVID-19 transmission and mortality in the US using the Google search trends for COVID-19 related symptoms.Specifically,we propose a stacked long short-term memory(SLSTM)architecture for predicting COVID-19 confirmed and death cases using historical time series data combined with auxiliary time series data from the Google COVID-19 search trends symptoms dataset.Considering the SLSTM networks trained using historical data only as the base models,our base models for 7 and 14-day-ahead forecasting of COVID cases had the mean absolute percentage error(MAPE)values of 6.6%and 8.8%,respectively.On the other side,our proposed models had improved MAPE values of 3.2%and 5.6%,respectively.For 7 and 14-day-ahead forecasting of COVID-19 deaths,the MAPE values of the base models were 4.8%and 11.4%,while the improved MAPE values of our proposed models were 4.7%and 7.8%,respectively.We found that the Google search trends for“pneumonia,”“shortness of breath,”and“fever”are the most informative search trends for predicting COVID-19 transmission.We also found that the search trends for“hypoxia”and“fever”were the most informative trends for forecasting COVID-19 mortality.
文摘Objective:Minimally invasive treatments for benign prostatic hyperplasia (BPH) have seen an increase in usage in recent years. We aimed to determine what types of events may influence patient search habits related to surgical BPH treatments.Methods:Google Trends was used to determine the frequency of searches for different minimally invasive and prostatic ablative treatments for BPH in the United States. The procedures including transurethral resection of the prostate (TURP), Aquablation therapy (Aquablation), Greenlight laser therapy (Greenlight), transurethral needle ablation, transurethral microwave thermotherapy, Urolift (prostatic urethral lift [PUL]), Rezum, iTind, holmium laser enucleation of the prostate, simple prostatectomy, and prostatic artery embolization were compared.Results:From January 1, 2004 to February 28, 2023, the number of internet search queries have increased for TURP, PUL, Rezum, prostatic artery embolization, and holmium laser enucleation of the prostate. There has been a slight decrease in searches for Greenlight, transurethral needle ablation, transurethral microwave thermotherapy, iTind, simple prostatectomy, and Aquablation.Conclusion:Despite increased searches of alternatives, TURP remains the most searched BPH procedure. Additionally, search habits may be influenced by several factors including government approval, corporate acquisition, and marketing campaigns. It is important for physicians to understand the types of events that may cause patients to inquire about certain treatments for better quality health information and clinical visits.
文摘This study systematically reviewed the literature on using the Google Search Volume Index(GSVI)as a proxy variable for investor attention and stock market movements.We analyzed 56 academic studies published between 2010 and 2021 using the Web of Sciences and ScienceDirect databases.The articles were classified and synthesized based on the selection criteria for building the GSVI:keywords of the search term,market region,and frequency of the data sample.Next,we analyze the effect of returns,volatility,and trading volume on the financial variables.The main results can be summarized as follows.(1)The GSVI is positively related to volatility and trading volume regardless of the keyword,market region,or frequency used for the sample.Hence,increasing investor attention toward a specific financial term will increase volatility and trading volume.(2)The GSVI can improve forecasting models for stock market movements.To conclude,this study consolidates,for the first time,the research literature on GSVI,which is highly valuable for academic practitioners in the area.
基金“Peso-Dollar Exchange Rate Prediction:Fundamentals vs.Internet Search Indicators”,which was sponsored by the Universidad Autonoma de Nuevo Leon,Mexico(PAICYT Project#375-CSA-2022).
文摘Recently,internet users have significantly increased their use of search engines,and market investors are no exception.As a result,predictive models that incorporate scattered web-based information are developing as an area of forecasting.The objective of this research is to compare the predictive accuracy of fundamental macroeconomic variables,online attention series measured by the Google Trends search volume index,and a combination of both data types for the Mexican,Brazilian,Chilean,and Colombian currencies paired with the USD.The exchange rate series used in this study are sourced from a real-time platform.Four indicators capturing the fundamental macroeconomic differences between these emerging economies and the U.S.from January 2004 to March 2021(monthly)were analyzed.To assess the predictive performance of the KNN algorithm,OLS regression and the random walk with drift model were compared.Considering in-sample predictions,the results generally exhibit lower estimation errors in the random walk with drift model,but in the joint fundamental–online attention data,the KNN and OLS predictions are more accurate than those of the random walk with drift.However,the KNN predictions based on out-of-sample fit generate the lowest estimation errors and the most accurate predictions for the joint fundamental–online attention data.Additionally,performance testing indicates that the KNN extended model outperforms the out-ofsample forecast for the OLS regression and the random walk with drift model.
基金The paper received financial support from the National Natural Science Foundation of China(Nos.71422015,71871213)the National Center for Mathematics and Interdisciplinary Sciences,Chinese Academy of Sciences.
文摘The increasing attention on Bitcoin since 2013 prompts the issue of possible evidence for a causal relationship between the Bitcoin market and internet attention.Taking the Google search volume index as the measure of internet attention,time-varying Granger causality between the global Bitcoin market and internet attention is examined.Empirical results show a strong Granger causal relationship between internet attention and trading volume.Moreover,they indicate,beginning in early 2018,an even stronger impact of trading volume on internet attention,which is consistent with the rapid increase in Bitcoin users following the 2017 Bitcoin bubble.Although Bitcoin returns are found to strongly affect internet attention,internet attention only occasionally affects Bitcoin returns.Further investigation reveals that interactions between internet attention and returns can be amplified by extreme changes in prices,and internet attention is more likely to lead to returns during Bitcoin bubbles.These empirical findings shed light on cryptocurrency investor attention theory and imply trading strategy in Bitcoin markets.
文摘The synchronicity effect between the financial market and online response for time-series forecasting is an important task with wide applications.This study combines data from the Baidu index(BDI),Google trends(GT),and transfer entropy(TE)to forecast a wide range of futures prices with a focus on China.A forecasting model based on a hybrid gray wolf optimizer(GWO),convolutional neural network(CNN),and long short-term memory(LSTM)is developed.First,Baidu and Google dual-platform search data were selected and constructed as Internetbased consumer price index(ICPI)using principal component analysis.Second,TE is used to quantify the information between online behavior and futures markets.Finally,the effective Internet-based consumer price index(ICPI)and TE are introduced into the GWO-CNN-LSTM model to forecast the daily prices of corn,soybean,polyvinyl chloride(PVC),egg,and rebar futures.The results show that the GWO-CNN-LSTM model has a significant improvement in predicting future prices.Internet-based CPI built on Baidu and Google platforms has a high degree of real-time performance and reduces the platform and language bias of the search data.Our proposed framework can provide predictive decision support for government leaders,market investors,and production activities.
文摘Roads may influence the selection of phenotypic traits of wildlife.In particular,the likelihood of vehicle collisions with wildlife may vary depending on body coloration in contrast to the road,which may be exaggerated by cultural attitudes toward the species.The timber rattlesnake Crotalus horridus is a threatened species that varies widely in coloration,and their color pattern could influence thermoregulatory use of roads and visibility to motorists.Moreover,better-camouflaged snakes may have higher road mortality in areas where environmental interest is lower and,perhaps,negative attitudes toward wildlife are more prevalent.We used citizen scientist observations of timber rattlesnakes from iNaturalist and categorized for each rattlesnake the surface they were on,its color pattern,and whether they were alive.We combined iNaturalist data with Google Trends data to characterize regional variation in environmental interest.We discovered that lighter-colored snakes were more likely to be found on roads,as were snakes further south,west,and on warmer days.Once on a road,coloration did not influence survival regardless of road type or environmental interest.However,snakes on asphalt roads or on southern roads were more likely to be found dead.The higher likelihood of lighter-colored snakes being found on roads suggests that they are at a greater overall risk of road death,potentially selecting for darker coloration.Citizen scientist behavior may at least partly underlie the influence of latitude on the results,however,and further work in the application of citizen science data to such research questions is warranted.
文摘Background:Preliminary evidence suggests that the burden of stress and anxiety may have considerably increased during the coronavirus disease 2019(COVID-19)pandemic.Since these two mental health-related factors are important causes of teeth grinding,we carried out an infodemiological analysis to define whether the burden of teeth grinding may have increased as a consequence of COVID-19.Methods:We conducted an electronic search in Google Trends,with the term“teeth grinding”,setting the geographical area to“US”or“UK”and the search period between July 2017 and July 2022.The weekly Google Trends score for“teeth grinding”was downloaded,and the difference in the volume Google searches for“teeth grinding”was compared between the pre-COVID-19 and COVID-19 periods in both countries.Results:The median value of weekly Google Trends score for“teeth grinding”was found to be significantly increased after emergence of the COVID-19 pandemic both in the UK(57 with interquartile range[IQR]51-64 vs.48 with IQR 42-53;+19%and P<0.001)and the US(78 with IQR 73-83 vs.70 with IQR 66-74;+11%and P<0.001),compared to the homologous period before.Conclusion:The results of this infodemiological analysis reveal that the volume of Web searches for“teeth grinding”in both the UK and US has considerably increased after emergence of the COVID-19 pandemic,thus probably reflecting an increased burden of this condition in the general population.
文摘Understanding the interplay between investor sentiment and cryptocurrency returns has become a critical area of research.Indeed,this study aims to uncover the role of Google investor sentiment on cryptocurrency returns(including Bitcoin,Litecoin,Ethereum,and Tether),especially during the 2017-18 bubble(January 01,2017,to December 31,2018)and the COVID-19 pandemic(January 01,2020,to March 15,2022).To achieve this,we use two techniques:quantile causality and wavelet coherence.First,the quantile causality test revealed that investors’optimistic sentiments have notably higher cryptocurrency returns,whereas pessimistic sentiments have significantly opposite effects.Moreover,the wavelet coherence analysis shows that co-movement between investor sentiment and Tether cannot be considered significant.This result supports the role of Tether as a stablecoin in portfolio diversification strategies.In fact,the findings will help investors improve the accuracy of cryptocurrency return forecasts in times of stressful events and pave the way for enhanced decision-making utility.
文摘Lamar University established a Center for Resiliency in 2021 in response to the increasing natural and human-made disasters in the Southeast Texas region.Now more than ever a robust decision-making framework is essential for this recently established regional interdisciplinary resiliency center to make well-informed decisions,prioritize funding eff ectively,and nurture collaboration across various fields to help communities vulnerable to these threats.This article provides a progress update and presents a methodology integrating VOSviewer and Google Trends to develop such a decision-making framework for the Center for Resiliency at Lamar University in the Southeast Texas region.Four prominent study areas in resilience—Climate Stressors and Disasters,Mental Wellness,Energy and Optimization,and Resilience Planning—were identified.These findings were validated with real-time insights from Google Trends,ensuring practical relevance to recent resilience needs to provide an understanding of evolving resilience dynamics.Furthermore,the paper discusses the status of research conducted at the Lamar University Center for Resiliency,showcasing its commitment to fostering resilience through diverse initiatives across five academic colleges.Integrating VOSviewer and Google Trends off ers a robust framework for informed decision making,aligning research eff orts with the Southeast Texas region’s current and future resilience needs.
文摘This review analyzes the state and recent progress in the field of information support for pollen allergy sufferers.For decades,information available for the patients and allergologists consisted of pollen counts,which are vital but insufficient.New technology paves the way to substantial increase in amount and diversity of the data.This paper reviews old and newly suggested methods to predict pollen and air pollutant concentrations in the air and proposes an allergy risk concept,which combines the pollen and pollution information and transforms it into a qualitative risk index.This new index is available in an app(Mobile Airways Sentinel NetworK-air)that was developed in the frame of the European Union grant Impact of Air POLLution on sleep,Asthma and Rhinitis(a project of European Institute of Innovation and Technology-Health).On-going transformation of the pollen allergy information support is based on new technological solutions for pollen and air quality monitoring and predictions.The new information-technology and artificial-intelligence-based solutions help to convert this information into easy-to-use services for both medical practitioners and allergy sufferers.
基金The Project is sponsored by the Scientific Research Foundation for the Retttmed Overseas Chinese Scholars, Ministry of Education of PRC, and supported by Beijing Natural Science Foundation （No. 9144025）. I would like to thank the reviewers who provide insightful comments and suggestions for improving this paper. I also would like to thank the editors who proofread and edit the paper. Without the supportive work of the reviewers and editors, this paper would not have been possible.
文摘Official monthly unemployment data is unavailable in China, while intense public interest in unemployment requires timely and accurate information. Using data on web queries from lead search engines in China, Baidu and Google, I build two indices measuring intensity of online unemployment-related searches. The unemployment-related search indices identify a structural break in the time series between October and November 2008, which corresponds to a turning point indicated by some macroeconomic indicators. The unemployment- related search indices are proven to have significant correlation with Purchasing Managers＇ Employment Indices and a set of macroeconomic indicators that are closely related to changes in unemployment in China. The results of Granger causality analysis show that the unemployment-related search indices can improve predictions of the c indicators. It suggests that unemploy- ment-related searches can potentially provide valuable, timely, and low-cost information for macroeconomic monitoring.
