Monitoring Epidemic Alert Levels by Analyzing Internet Search Volume

Zhou, Xi‐Lang; Qin, Li; Zhu, Zhenglin; Zhao, Han; Tang, Hao; Feng, Yujie

doi:10.1109/tbme.2012.2228264

Cited by 23 publications

(17 citation statements)

References 9 publications

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“…Internet search query analysis has been used to study a range of mental health issues and health behaviors, including correlations between mental health-related searches and times of economic stressors (Ayers et al, 2012); seasonality of depressive symptoms (Ayers, Althouse, Allem, Rosenquist, & Ford, 2013); suicide (Bruckner, McClure, & Kim, 2014;Page, Chang, & Gunnell, 2011); use of tobacco, e-cigarettes, and vaping (Ayers, Ribisl, & Brownstein, 2011, Ayers, Althouse, Ribisl, & Emery, 2014Ayers et al, 2016;Cavazos-Rehg et al, 2015); vaccine use (Barak-Corren & Reis, 2015); provider prescribing behaviors (Simmering, Polgreen, & Polgreen, 2014); bath salts use (Yin & Ho, 2012); prenatal care (D'Ambrosio et al, 2015); and krokodil use (an extremely dangerous street drug) (Zheluk et al, 2014). Internet search query analysis has also been used to predict or estimate many physical health conditions (Althouse, Yih Yng, & Cummings, 2011;Carneiro & Mylonakis, 2009;Chan, Sahai, Conrad, & Brownstein, 2011;Cho et al, 2013;Cook, Conrad, Fowlkes, & Mohebbi, 2011;Cooper et al, 2005;Desai et al, 2012;Dugas et al, 2012, Dugas et al, 2013Fazeli Dehkordy et al, 2014;Gluskin, Johansson, Santillana, & Brownstein, 2014;Martin, Xu, & Yasui, 2014;Min, Haojie, Jianfeng, Rutherford, & Fen, 2013;Ocampo, Chunara, & Brownstein, 2013;Ortiz et al, 2011;Patwardhan, Bilkovski, & Goldstein, 2012;Samaras, García-Barriocanal, & Sicilia, 2012;Timpka et al, 2014;Wilson & Brownstein, 2009;Zheluk, Quinn, Hercz, & Gillespie, 2013;Zhou et al, 2013…”

Section: Internet Search Querymentioning

confidence: 99%

The potential use of social media and other internet-related data and communications for child maltreatment surveillance and epidemiological research: Scoping review and recommendations

Schwab‐Reese¹,

Hovdestad²,

Tonmyr³

et al. 2018

Child Abuse & Neglect

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Collecting child maltreatment data is a complicated undertaking for many reasons. As a result, there is an interest by child maltreatment researchers to develop methodologies that allow for the triangulation of data sources. To better understand how social media and internet-based technologies could contribute to these approaches, we conducted a scoping review to provide an overview of social media and internet-based methodologies for health research, to report results of evaluation and validation research on these methods, and to highlight studies with potential relevance to child maltreatment research and surveillance. Many approaches were identified in the broad health literature; however, there has been limited application of these approaches to child maltreatment. The most common use was recruiting participants or engaging existing participants using online methods. From the broad health literature, social media and internet-based approaches to surveillance and epidemiologic research appear promising. Many of the approaches are relatively low cost and easy to implement without extensive infrastructure, but there are also a range of limitations for each method. Several methods have a mixed record of validation and sources of error in estimation are not yet understood or predictable. In addition to the problems relevant to other health outcomes, child maltreatment researchers face additional challenges, including the complex ethical issues associated with both internet-based and child maltreatment research. If these issues are adequately addressed, social media and internet-based technologies may be a promising approach to reducing some of the limitations in existing child maltreatment data.

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Section: Internet Search Querymentioning

confidence: 99%

The potential use of social media and other internet-related data and communications for child maltreatment surveillance and epidemiological research: Scoping review and recommendations

Schwab‐Reese¹,

Hovdestad²,

Tonmyr³

et al. 2018

Child Abuse & Neglect

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“…For example, we exclude from detailed analysis work that provides only alerts [39,40], measures public perception of a disease [41], includes disease dynamics in its model [42], evaluates a third-party method [43], uses non-single-source data feeds [39,44], or crowd-sources health-related data (participatory disease surveillance) [45,46]. We also focus on work that estimates biologically-rooted metrics.…”

Section: Author Summarymentioning

confidence: 99%

Global Disease Monitoring and Forecasting with Wikipedia

Generous

Fairchild²,

Deshpande³

et al. 2016

OJPHI

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Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data, such as social media and search queries, are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with r 2 up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without retraining is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.

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“…We exclude from detailed analysis work that (for example) provides only alerts [38, 178], measures public perception or awareness of a disease [138], includes disease dynamics in its model [147,170], evaluates a third-party method [95, 120], uses non-single-source data feeds [38, 55], or crowd-sources health-related data ( participatory disease surveillance ) [34,35]. …”

Section: Related Workmentioning

confidence: 99%

Measuring Global Disease with Wikipedia

Priedhorsky

Osthus

Daughton

et al. 2017

Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

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Effective disease monitoring provides a foundation for effective public health systems. This has historically been accomplished with patient contact and bureaucratic aggregation, which tends to be slow and expensive. Recent internet-based approaches promise to be real-time and cheap, with few parameters. However, the question of when and how these approaches work remains open. We addressed this question using Wikipedia access logs and category links. Our experiments, replicable and extensible using our open source code and data, test the effect of semantic article filtering, amount of training data, forecast horizon, and model staleness by comparing across 6 diseases and 4 countries using thousands of individual models. We found that our minimal-configuration, language-agnostic article selection process based on semantic relatedness is effective for improving predictions, and that our approach is relatively insensitive to the amount and age of training data. We also found, in contrast to prior work, very little forecasting value, and we argue that this is consistent with theoretical considerations about the nature of forecasting. These mixed results lead us to propose that the currently observational field of internet-based disease surveillance must pivot to include theoretical models of information flow as well as controlled experiments based on simulations of disease.

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Monitoring Epidemic Alert Levels by Analyzing Internet Search Volume

Cited by 23 publications

References 9 publications

The potential use of social media and other internet-related data and communications for child maltreatment surveillance and epidemiological research: Scoping review and recommendations

The potential use of social media and other internet-related data and communications for child maltreatment surveillance and epidemiological research: Scoping review and recommendations

Global Disease Monitoring and Forecasting with Wikipedia

Measuring Global Disease with Wikipedia

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