Global Public Interests and Dynamic Trends in Osteoporosis From 2004 to 2019: Infodemiology Study

Wang, Peng; Xu, Qing; Cao, Rong-Rong; Deng, Fei‐Yan; Lei, Shu‐Feng

doi:10.2196/25422

Cited by 4 publications

(3 citation statements)

References 37 publications

(43 reference statements)

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“…The search volume for cannabis use in Canada followed a clear biphasic pattern, peaking in the winter and fall, followed by a summer trough. The same is true for an exploration of worldwide searches for information on osteoporosis, where recognizing the academic search pattern, and interpreting it in the light of when school terms start and end in different countries, might have provided an alternate explanation for the observed seasonality of searches and the observed differences between countries [ 24 ]. Academic cycling is also clearly present in an exploration of searches for information about mental health conditions [ 18 ].…”

Section: Discussionmentioning

confidence: 99%

“…Some examples from health care, mathematics, and physics are shown in Figure 1 . This same academic cycling pattern is clearly present in some of the infodemiology literature, but, even when it appears to be the main driver of the variation in search volume, it is either not acknowledged as such or not accounted for when its presence is recognized [ 18 , 23 , 24 ]. In this study, we (1) used the fast Fourier transform (FFT) on Google Trends search volume data with strong academic cycling, (2) identified the frequency domain pattern of that academic cycling, (3) searched for and removed that pattern from the frequency domain of search terms where seasonality is of clinical interest, and (4) recreated the time series data for the terms of clinical interest, with the academic cycling component removed.…”

Section: Introductionmentioning

confidence: 86%

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Confounding Effect of Undergraduate Semester–Driven “Academic" Internet Searches on the Ability to Detect True Disease Seasonality in Google Trends Data: Fourier Filter Method Development and Demonstration

Gillis¹,

Garrison²

2022

JMIR Infodemiology

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Background Internet search volume for medical information, as tracked by Google Trends, has been used to demonstrate unexpected seasonality in the symptom burden of a variety of medical conditions. However, when more technical medical language is used (eg, diagnoses), we believe that this technique is confounded by the cyclic, school year–driven internet search patterns of health care students. Objective This study aimed to (1) demonstrate that artificial “academic cycling” of Google Trends’ search volume is present in many health care terms, (2) demonstrate how signal processing techniques can be used to filter academic cycling out of Google Trends data, and (3) apply this filtering technique to some clinically relevant examples. Methods We obtained the Google Trends search volume data for a variety of academic terms demonstrating strong academic cycling and used a Fourier analysis technique to (1) identify the frequency domain fingerprint of this modulating pattern in one particularly strong example, and (2) filter that pattern out of the original data. After this illustrative example, we then applied the same filtering technique to internet searches for information on 3 medical conditions believed to have true seasonal modulation (myocardial infarction, hypertension, and depression), and all bacterial genus terms within a common medical microbiology textbook. Results Academic cycling explains much of the seasonal variation in internet search volume for many technically oriented search terms, including the bacterial genus term [“Staphylococcus”], for which academic cycling explained 73.8% of the variability in search volume (using the squared Spearman rank correlation coefficient, P<.001). Of the 56 bacterial genus terms examined, 6 displayed sufficiently strong seasonality to warrant further examination post filtering. This included (1) [“Aeromonas” + “Plesiomonas”] (nosocomial infections that were searched for more frequently during the summer), (2) [“Ehrlichia”] (a tick-borne pathogen that was searched for more frequently during late spring), (3) [“Moraxella”] and [“Haemophilus”] (respiratory infections that were searched for more frequently during late winter), (4) [“Legionella”] (searched for more frequently during midsummer), and (5) [“Vibrio”] (which spiked for 2 months during midsummer). The terms [“myocardial infarction”] and [“hypertension”] lacked any obvious seasonal cycling after filtering, whereas [“depression”] maintained an annual cycling pattern. Conclusions Although it is reasonable to search for seasonal modulation of medical conditions using Google Trends’ internet search volume and lay-appropriate search terms, the variation in more technical search terms may be driven by health care students whose search frequency varies with the academic school year. When this is the case, using Fourier analysis to filter out academic cycling is a potential means to establish whether additional seasonality is present.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 86%

Confounding Effect of Undergraduate Semester–Driven “Academic" Internet Searches on the Ability to Detect True Disease Seasonality in Google Trends Data: Fourier Filter Method Development and Demonstration

Gillis¹,

Garrison²

2022

JMIR Infodemiology

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“…With the continuous progress of intelligent transportation, car networking technology has received more social attention. Based on intravehicle networking, intervehicle networking, and in-vehicle mobile networking, car networking [1] is a large-scale network that conducts wireless communication and information exchange and cooperates with high-efficiency communication terminals to improve traffic efficiency and improve driving safety. Mobile communication technology is the foundation of communication networks, so its digitization, informatization, and intelligence will inevitably promote the popularization and development of car networking.…”

Section: Introductionmentioning

confidence: 99%

Research on Optimization Algorithm for Resource Allocation of Heterogeneous Car Networking Engineering Cloud System Based on Big Data

Yao

2022

Mathematical Problems in Engineering

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A big data-based heterogeneous Internet of Vehicles engineering cloud system resource allocation optimization algorithm is proposed for the sake of meeting the needs of Internet of Vehicles applications and improving the rationality and efficiency of cloud system resource allocation. Based on taking the minimum cloud system delay as the resource allocation target, a multislot cloud system delay optimization model and its indicative function are constructed, the probability distribution function is derived according to the obtained multidimensional probability distribution function set, and the available channels of the vehicle in different time periods are determined. In this way, the matching degree between the vehicle and the channel is solved, the delay optimization model is turned into a convex optimization problem with independent variables, and the resource allocation algorithm for different task offload destinations is optimized. Meanwhile, by building a heterogeneous vehicle network simulation system, the performance of the algorithm is evaluated from the perspectives of resource rental cost, weighted resource utilization, and bit loss rate. As can be learned from the simulation results, the proposed algorithm can effectively reduce the cost of resource rental, and at the same time, the advantages of resource utilization and bit loss rate are relatively significant, so it has certain effectiveness and practicability.

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Confounding Effect of Undergraduate Semester–Driven “Academic" Internet Searches on the Ability to Detect True Disease Seasonality in Google Trends Data: Fourier Filter Method Development and Demonstration (Preprint)

Gillis¹,

Garrison²

2021

Preprint

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BACKGROUND Internet search volume for medical information, as tracked by Google Trends, has been used to demonstrate unexpected seasonality in the symptom burden of a variety of medical conditions. However, when more technical medical language is used (eg, diagnoses), we believe that this technique is confounded by the cyclic, school year–driven internet search patterns of health care students. OBJECTIVE This study aimed to (1) demonstrate that artificial “academic cycling” of Google Trends’ search volume is present in many health care terms, (2) demonstrate how signal processing techniques can be used to filter academic cycling out of Google Trends data, and (3) apply this filtering technique to some clinically relevant examples. METHODS We obtained the Google Trends search volume data for a variety of academic terms demonstrating strong academic cycling and used a Fourier analysis technique to (1) identify the frequency domain fingerprint of this modulating pattern in one particularly strong example, and (2) filter that pattern out of the original data. After this illustrative example, we then applied the same filtering technique to internet searches for information on 3 medical conditions believed to have true seasonal modulation (myocardial infarction, hypertension, and depression), and all bacterial genus terms within a common medical microbiology textbook. RESULTS Academic cycling explains much of the seasonal variation in internet search volume for many technically oriented search terms, including the bacterial genus term [“Staphylococcus”], for which academic cycling explained 73.8% of the variability in search volume (using the squared Spearman rank correlation coefficient, <i>P</i><.001). Of the 56 bacterial genus terms examined, 6 displayed sufficiently strong seasonality to warrant further examination post filtering. This included (1) [“Aeromonas” + “Plesiomonas”] (nosocomial infections that were searched for more frequently during the summer), (2) [“Ehrlichia”] (a tick-borne pathogen that was searched for more frequently during late spring), (3) [“Moraxella”] and [“Haemophilus”] (respiratory infections that were searched for more frequently during late winter), (4) [“Legionella”] (searched for more frequently during midsummer), and (5) [“Vibrio”] (which spiked for 2 months during midsummer). The terms [“myocardial infarction”] and [“hypertension”] lacked any obvious seasonal cycling after filtering, whereas [“depression”] maintained an annual cycling pattern. CONCLUSIONS Although it is reasonable to search for seasonal modulation of medical conditions using Google Trends’ internet search volume and lay-appropriate search terms, the variation in more technical search terms may be driven by health care students whose search frequency varies with the academic school year. When this is the case, using Fourier analysis to filter out academic cycling is a potential means to establish whether additional seasonality is present.

show abstract

Global Public Interests and Dynamic Trends in Osteoporosis From 2004 to 2019: Infodemiology Study

Cited by 4 publications

References 37 publications

Confounding Effect of Undergraduate Semester–Driven “Academic" Internet Searches on the Ability to Detect True Disease Seasonality in Google Trends Data: Fourier Filter Method Development and Demonstration

Confounding Effect of Undergraduate Semester–Driven “Academic" Internet Searches on the Ability to Detect True Disease Seasonality in Google Trends Data: Fourier Filter Method Development and Demonstration

Research on Optimization Algorithm for Resource Allocation of Heterogeneous Car Networking Engineering Cloud System Based on Big Data

Confounding Effect of Undergraduate Semester–Driven “Academic" Internet Searches on the Ability to Detect True Disease Seasonality in Google Trends Data: Fourier Filter Method Development and Demonstration (Preprint)

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