Seasonality  of cellulitis: evidence from Google Trends

Zhang, Xin; Dang, Shuangsuo; Ji, Fanpu; Shi, Jianghong; Li, Yaping; Li, Mei; Jia, Xiaoli; Yueqiang, Wan; Bao, Xiongxiong; Wang, Wenjun

doi:10.2147/idr.s163290

Cited by 30 publications

(16 citation statements)

References 13 publications

(13 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other topics studied using GT data are skin cancer [55], sunscreen use [56], sunburn [57], seasonality of bruxism [58], multiple sclerosis [59], cancer [60], stroke [61], HIV [62], lupus [63], norovirus [64], sepsis [65], pertussis [66], epistaxis [67], plague [68], rheumatoid arthritis [69], and prostate cancer [70]. In terms of general population behavior, research was done using GT data on pharmaceutical data [71], vaccinations [72], movement disorders [73], digital epidemiology [74], kidney stone surgery [75], foot and ankle pain [76], knee injuries [77], osteoarthritis [78], seasonality of cellulitis [79], tracking influenza epidemics using climate data [80], palliative care [81], cosmetic body procedures [82], and anesthesia [83]. GT is also used for forecasting [84], real-time surveillance [85], or prevention [86] of diseases.…”

Section: Introductionmentioning

confidence: 99%

Google Medical Update: Why Is the Search Engine Decreasing Visibility of Health and Medical Information Websites?

Strzelecki

2020

IJERPH

View full text Add to dashboard Cite

The Google search engine answers many health and medical information queries every day. People have become used to searching for this type of information. This paper presents a study which examined the visibility of health and medical information websites. The purpose of this study was to find out why Google is decreasing the visibility of such websites and how to measure this decrease. Since August 2018, Google has been more rigorously rating these websites, since they can potentially impact people’s health. The method of the study was to collect data about the visibility of health and medical information websites in sequential time snapshots. Visibility consists of combined data of unique keywords, positions, and URL results. The sample under study was made up of 21 websites selected from 10 European countries. The findings reveal that in sequential time snapshots, search visibility decreased. The decrease was not dependent on the country or the language. The main reason why Google is decreasing the visibility of such websites is that they do not meet high ranking criteria.

show abstract

Section: Introductionmentioning

confidence: 99%

Google Medical Update: Why Is the Search Engine Decreasing Visibility of Health and Medical Information Websites?

Strzelecki

2020

IJERPH

View full text Add to dashboard Cite

show abstract

“…In spite of the failure of GFT, the number of studies utilizing Google searches is continuously increasing (Adawi et al, ; Alicino et al, ; Mahroum et al, ; Martinez‐Lopez, Ruiz‐Villaverde, & Molina‐Leyva, ; McGough, Brownstein, Hawkins, & Santillana, ; Pollett et al, ; Santillana et al, ; Savelkoel, Claushuis, van Engelen, Scheres, & Wiersinga, ; Sciascia & Radin, ; Strauss et al, ; Teng et al, ; Wang & Chen, ; Wang et al, ; Yang, Santillana, & Kou, ; Zhang, Dang, et al, ; Zhang, Bambrick, Mengersen, Tong, & Hu, ). We believe that it should be investigated whether Google Trends can have a role in extending the traditional epidemiological models instead of replacing traditional surveillance methods.…”

Section: Introductionmentioning

confidence: 99%

“…In spite of the failure of GFT, the number of studies utilizing Google searches is continuously increasing (Adawi et al, 2017;Alicino et al, 2015;Mahroum et al, 2018;Martinez-Lopez, Ruiz-Villaverde, & Molina-Leyva, 2018;McGough, Brownstein, Hawkins, & Santillana, 2017;Pollett et al, 2017;Santillana et al, 2015;Savelkoel, Claushuis, van Engelen, Scheres, & Wiersinga, 2018;Sciascia & Radin, 2017;Strauss et al, 2017;Teng et al, 2017;Yang, Santillana, & Kou, 2015;Zhang, Dang, et al, 2018;Zhang, Bambrick, Mengersen, Tong, & Hu, 2018).…”

mentioning

confidence: 99%

Can Google Trends data improve forecasting of Lyme disease incidence?

Kapitány-Fövény

Ferenci

Sulyok

et al. 2018

Zoonoses and Public Health

View full text Add to dashboard Cite

Background Online activity‐based epidemiological surveillance and forecasting is getting more and more attention. To date, Google search volumes have not been assessed for forecasting of tick‐borne diseases. Thus, we performed an analysis of forecasting of the Lyme disease incidence based on the traditional data extended with Google Trends. Methods Data on the weekly incidence of Lyme disease in Germany from 16 June 2013 to 27 May 2018 were obtained from the database of the Robert Koch Institute. Data of Internet searches were obtained from Google Trends searching “Borreliose” in Germany for the “last 5 years” as a timespan category. Data were split into the training (from 16 June 2013 to 11 June 2017) and validation (from 12 June 2017, to 27 May 2018) data sets. A seasonal autoregressive moving average model, SARIMA (0,1,1) (0,1,1) [52] model was selected to describe the time series of the weekly Lyme incidence. After this, we added the Google Trends data as an external regressor and identified the SARIMA (0,1,1) (0,1,1) [52] model as optimal. We made predictions for the validation interval using these two models and compared predictions with the values of the validation data set. Results Forecasting for the validation timespan resulted in similar values for the models. Comparing the forecasted values with the reported ones resulted in an residual mean squared error (RMSE) of 0.3763; the mean absolute percentage error (MAPE) was 8.233 for the model without Google searches with an RMSE of 0.3732; and the MAPE was 8.17495 for the Google Trends values‐expanded model. The difference between the predictive performances was insignificant (Diebold‐Mariano Test, p‐value = 0.4152). Conclusion Google Trends data are a good correlate of the reported incidence of Lyme disease in Germany, but it failed to significantly improve the forecasting accuracy in models based on traditional data.

show abstract

“…Most time series cross-sectional studies analyzed digital data to compare phenomena across time periods. An example is Zhang et al [ 21 ], which examined seasonal variation in the volume of Google search queries for cellulitis from 2004 to 2016. An example of cross-sectional study at single point in time is a study that analyzed the types of discourse about Zika virus on Twitter for 2 months [ 22 ].…”

Section: Resultsmentioning

confidence: 99%

Digital Epidemiology: Use of Digital Data Collected for Non-epidemiological Purposes in Epidemiological Studies

Park

Jung

et al. 2018

Healthc Inform Res

View full text Add to dashboard Cite

ObjectivesWe reviewed digital epidemiological studies to characterize how researchers are using digital data by topic domain, study purpose, data source, and analytic method.MethodsWe reviewed research articles published within the last decade that used digital data to answer epidemiological research questions. Data were abstracted from these articles using a data collection tool that we developed. Finally, we summarized the characteristics of the digital epidemiological studies.ResultsWe identified six main topic domains: infectious diseases (58.7%), non-communicable diseases (29.4%), mental health and substance use (8.3%), general population behavior (4.6%), environmental, dietary, and lifestyle (4.6%), and vital status (0.9%). We identified four categories for the study purpose: description (22.9%), exploration (34.9%), explanation (27.5%), and prediction and control (14.7%). We identified eight categories for the data sources: web search query (52.3%), social media posts (31.2%), web portal posts (11.9%), webpage access logs (7.3%), images (7.3%), mobile phone network data (1.8%), global positioning system data (1.8%), and others (2.8%). Of these, 50.5% used correlation analyses, 41.3% regression analyses, 25.6% machine learning, and 19.3% descriptive analyses.ConclusionsDigital data collected for non-epidemiological purposes are being used to study health phenomena in a variety of topic domains. Digital epidemiology requires access to large datasets and advanced analytics. Ensuring open access is clearly at odds with the desire to have as little personal data as possible in these large datasets to protect privacy. Establishment of data cooperatives with restricted access may be a solution to this dilemma.

show abstract

Seasonality of cellulitis: evidence from Google Trends

Cited by 30 publications

References 13 publications

Google Medical Update: Why Is the Search Engine Decreasing Visibility of Health and Medical Information Websites?

Google Medical Update: Why Is the Search Engine Decreasing Visibility of Health and Medical Information Websites?

Can Google Trends data improve forecasting of Lyme disease incidence?

Digital Epidemiology: Use of Digital Data Collected for Non-epidemiological Purposes in Epidemiological Studies

Contact Info

Product

Resources

About